---
_id: '14208'
abstract:
- lang: eng
text: This paper focuses on over-parameterized deep neural networks (DNNs) with
ReLU activation functions and proves that when the data distribution is well-separated,
DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly)
zero-training error under the lazy training regime. For this purpose, we unify
three interrelated concepts of overparameterization, benign overfitting, and the
Lipschitz constant of DNNs. Our results indicate that interpolating with smoother
functions leads to better generalization. Furthermore, we investigate the special
case where interpolating smooth ground-truth functions is performed by DNNs under
the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates
that the generalization error converges to a constant order that only depends
on label noise and initialization noise, which theoretically verifies benign overfitting.
Our analysis provides a tight lower bound on the normalized margin under non-smooth
activation functions, as well as the minimum eigenvalue of NTK under high-dimensional
settings, which has its own interest in learning theory.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Zhenyu
full_name: Zhu, Zhenyu
last_name: Zhu
- first_name: Fanghui
full_name: Liu, Fanghui
last_name: Liu
- first_name: Grigorios G
full_name: Chrysos, Grigorios G
last_name: Chrysos
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Volkan
full_name: Cevher, Volkan
last_name: Cevher
citation:
ama: 'Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep
neural networks under lazy training. In: Proceedings of the 40th International
Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.'
apa: 'Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., & Cevher, V. (2023).
Benign overfitting in deep neural networks under lazy training. In Proceedings
of the 40th International Conference on Machine Learning (Vol. 202, pp. 43105–43128).
Honolulu, Hawaii, United States: ML Research Press.'
chicago: Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and
Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.”
In Proceedings of the 40th International Conference on Machine Learning,
202:43105–28. ML Research Press, 2023.
ieee: Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting
in deep neural networks under lazy training,” in Proceedings of the 40th International
Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol.
202, pp. 43105–43128.
ista: Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting
in deep neural networks under lazy training. Proceedings of the 40th International
Conference on Machine Learning. International Conference on Machine Learning,
PMLR, vol. 202, 43105–43128.
mla: Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy
Training.” Proceedings of the 40th International Conference on Machine Learning,
vol. 202, ML Research Press, 2023, pp. 43105–28.
short: Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of
the 40th International Conference on Machine Learning, ML Research Press, 2023,
pp. 43105–43128.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, United States
name: International Conference on Machine Learning
start_date: 2023-07-23
date_created: 2023-08-22T14:18:18Z
date_published: 2023-05-30T00:00:00Z
date_updated: 2023-09-13T08:46:46Z
day: '30'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2305.19377'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2305.19377
month: '05'
oa: 1
oa_version: Preprint
page: 43105-43128
publication: Proceedings of the 40th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Benign overfitting in deep neural networks under lazy training
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14209'
abstract:
- lang: eng
text: Diffusion models excel at generating photorealistic images from text-queries.
Naturally, many approaches have been proposed to use these generative abilities
to augment training datasets for downstream tasks, such as classification. However,
diffusion models are themselves trained on large noisily supervised, but nonetheless,
annotated datasets. It is an open question whether the generalization capabilities
of diffusion models beyond using the additional data of the pre-training process
for augmentation lead to improved downstream performance. We perform a systematic
evaluation of existing methods to generate images from diffusion models and study
new extensions to assess their benefit for data augmentation. While we find that
personalizing diffusion models towards the target data outperforms simpler prompting
strategies, we also show that using the training data of the diffusion model alone,
via a simple nearest neighbor retrieval procedure, leads to even stronger downstream
performance. Overall, our study probes the limitations of diffusion models for
data augmentation but also highlights its potential in generating new training
data to improve performance on simple downstream vision tasks.
article_number: '2304.10253'
article_processing_charge: No
arxiv: 1
author:
- first_name: Max F.
full_name: Burg, Max F.
last_name: Burg
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Osama
full_name: Makansi, Osama
last_name: Makansi
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion
models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
apa: Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F.,
& Russell, C. (n.d.). A data augmentation perspective on diffusion models
and retrieval. arXiv. https://doi.org/10.48550/arXiv.2304.10253
chicago: Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi,
Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion
Models and Retrieval.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.10253.
ieee: M. F. Burg et al., “A data augmentation perspective on diffusion models
and retrieval,” arXiv. .
ista: Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A
data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253.
mla: Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and
Retrieval.” ArXiv, 2304.10253, doi:10.48550/arXiv.2304.10253.
short: M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell,
ArXiv (n.d.).
date_created: 2023-08-22T14:18:43Z
date_published: 2023-04-20T00:00:00Z
date_updated: 2023-09-13T08:51:56Z
day: '20'
department:
- _id: FrLo
doi: 10.48550/arXiv.2304.10253
extern: '1'
external_id:
arxiv:
- '2304.10253'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.10253
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: A data augmentation perspective on diffusion models and retrieval
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14210'
abstract:
- lang: eng
text: Recovering the latent factors of variation of high dimensional data has so
far focused on simple synthetic settings. Mostly building on unsupervised and
weakly-supervised objectives, prior work missed out on the positive implications
for representation learning on real world data. In this work, we propose to leverage
knowledge extracted from a diversified set of supervised tasks to learn a common
disentangled representation. Assuming each supervised task only depends on an
unknown subset of the factors of variation, we disentangle the feature space of
a supervised multi-task model, with features activating sparsely across different
tasks and information being shared as appropriate. Importantly, we never directly
observe the factors of variations but establish that access to multiple tasks
is sufficient for identifiability under sufficiency and minimality assumptions.
We validate our approach on six real world distribution shift benchmarks, and
different data modalities (images, text), demonstrating how disentangled representations
can be transferred to real settings.
article_number: '2304.07939'
article_processing_charge: No
arxiv: 1
author:
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Luca
full_name: Zancato, Luca
last_name: Zancato
- first_name: Alessandro
full_name: Achille, Alessandro
last_name: Achille
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
- first_name: Stefano
full_name: Soatto, Stefano
last_name: Soatto
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature
activations for disentangled representation learning. arXiv. doi:10.48550/arXiv.2304.07939
apa: Fumero, M., Wenzel, F., Zancato, L., Achille, A., Rodolà, E., Soatto, S., …
Locatello, F. (n.d.). Leveraging sparse and shared feature activations for disentangled
representation learning. arXiv. https://doi.org/10.48550/arXiv.2304.07939
chicago: Fumero, Marco, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele
Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello. “Leveraging
Sparse and Shared Feature Activations for Disentangled Representation Learning.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.07939.
ieee: M. Fumero et al., “Leveraging sparse and shared feature activations
for disentangled representation learning,” arXiv. .
ista: Fumero M, Wenzel F, Zancato L, Achille A, Rodolà E, Soatto S, Schölkopf B,
Locatello F. Leveraging sparse and shared feature activations for disentangled
representation learning. arXiv, 2304.07939.
mla: Fumero, Marco, et al. “Leveraging Sparse and Shared Feature Activations for
Disentangled Representation Learning.” ArXiv, 2304.07939, doi:10.48550/arXiv.2304.07939.
short: M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf,
F. Locatello, ArXiv (n.d.).
corr_author: '1'
date_created: 2023-08-22T14:19:03Z
date_published: 2023-04-17T00:00:00Z
date_updated: 2024-10-09T21:06:54Z
day: '17'
department:
- _id: FrLo
doi: 10.48550/arXiv.2304.07939
external_id:
arxiv:
- '2304.07939'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.07939
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Leveraging sparse and shared feature activations for disentangled representation
learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14211'
abstract:
- lang: eng
text: 'Causal discovery methods are intrinsically constrained by the set of assumptions
needed to ensure structure identifiability. Moreover additional restrictions are
often imposed in order to simplify the inference task: this is the case for the
Gaussian noise assumption on additive non-linear models, which is common to many
causal discovery approaches. In this paper we show the shortcomings of inference
under this hypothesis, analyzing the risk of edge inversion under violation of
Gaussianity of the noise terms. Then, we propose a novel method for inferring
the topological ordering of the variables in the causal graph, from data generated
according to an additive non-linear model with a generic noise distribution. This
leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm
with a minimal set of assumptions and state of the art performance, experimentally
benchmarked on synthetic data.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with
score matching on additive models with arbitrary noise. In: 2nd Conference
on Causal Learning and Reasoning. ; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Causal discovery with score matching on additive models with arbitrary noise.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Causal Discovery with Score Matching on Additive Models
with Arbitrary Noise.” In 2nd Conference on Causal Learning and Reasoning,
2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery
with score matching on additive models with arbitrary noise,” in 2nd Conference
on Causal Learning and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery
with score matching on additive models with arbitrary noise. 2nd Conference on
Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive
Models with Arbitrary Noise.” 2nd Conference on Causal Learning and Reasoning,
2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:21Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2024-10-14T12:30:04Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03265'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03265
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Causal discovery with score matching on additive models with arbitrary noise
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14212'
abstract:
- lang: eng
text: This paper demonstrates how to discover the whole causal graph from the second
derivative of the log-likelihood in observational non-linear additive Gaussian
noise models. Leveraging scalable machine learning approaches to approximate the
score function ∇logp(X), we extend the work of Rolland et al. (2022) that only
recovers the topological order from the score and requires an expensive pruning
step removing spurious edges among those admitted by the ordering. Our analysis
leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces
the complexity of the pruning by a factor proportional to the graph size. In practice,
DAS achieves competitive accuracy with current state-of-the-art while being over
an order of magnitude faster. Overall, our approach enables principled and scalable
causal discovery, significantly lowering the compute bar.
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery
with score matching. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Scalable causal discovery with score matching. In 2nd Conference on Causal
Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable
causal discovery with score matching,” in 2nd Conference on Causal Learning
and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal
discovery with score matching. 2nd Conference on Causal Learning and Reasoning.
CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.”
2nd Conference on Causal Learning and Reasoning, 2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:40Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2024-10-14T12:30:15Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03382'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03382
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable causal discovery with score matching
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14214'
abstract:
- lang: eng
text: 'Recent years have seen a surge of interest in learning high-level causal
representations from low-level image pairs under interventions. Yet, existing
efforts are largely limited to simple synthetic settings that are far away from
real-world problems. In this paper, we present Causal Triplet, a causal representation
learning benchmark featuring not only visually more complex scenes, but also two
crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual
setting, where only certain object-level variables allow for counterfactual observations
whereas others do not; (ii) an interventional downstream task with an emphasis
on out-of-distribution robustness from the independent causal mechanisms principle.
Through extensive experiments, we find that models built with the knowledge of
disentangled or object-centric representations significantly outperform their
distributed counterparts. However, recent causal representation learning methods
still struggle to identify such latent structures, indicating substantial challenges
and opportunities for future work.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Yuejiang
full_name: Liu, Yuejiang
last_name: Liu
- first_name: Alexandre
full_name: Alahi, Alexandre
last_name: Alahi
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric
causal representation learning. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: 'Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &
Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric
causal representation learning. In 2nd Conference on Causal Learning and Reasoning.
Tübingen, Germany.'
chicago: 'Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow,
Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge
for Intervention-Centric Causal Representation Learning.” In 2nd Conference
on Causal Learning and Reasoning, 2023.'
ieee: 'Y. Liu et al., “Causal triplet: An open challenge for intervention-centric
causal representation learning,” in 2nd Conference on Causal Learning and Reasoning,
Tübingen, Germany, 2023.'
ista: 'Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023.
Causal triplet: An open challenge for intervention-centric causal representation
learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on
Causal Learning and Reasoning.'
mla: 'Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric
Causal Representation Learning.” 2nd Conference on Causal Learning and Reasoning,
2023.'
short: Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello,
in:, 2nd Conference on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:20:18Z
date_published: 2023-04-12T00:00:00Z
date_updated: 2024-10-14T12:30:42Z
day: '12'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2301.05169'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2301.05169
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: 'Causal triplet: An open challenge for intervention-centric causal representation
learning'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
OA_type: green
_id: '14216'
abstract:
- lang: eng
text: CLIP proved that aligning visual and language spaces is key to solving many
vision tasks without explicit training, but required to train image and text encoders
from scratch on a huge dataset. LiT improved this by only training the text encoder
and using a pre-trained vision network. In this paper, we show that a common space
can be created without any training at all, using single-domain encoders (trained
with or without supervision) and a much smaller amount of image-text pairs. Furthermore,
our model has unique properties. Most notably, deploying a new version with updated
training samples can be done in a matter of seconds. Additionally, the representations
in the common space are easily interpretable as every dimension corresponds to
the similarity of the input to a unique entry in the multimodal dataset. Experiments
on standard zero-shot visual benchmarks demonstrate the typical transfer ability
of image-text models. Overall, our method represents a simple yet surprisingly
strong baseline for foundation multi-modal models, raising important questions
on their data efficiency and on the role of retrieval in machine learning.
acknowledgement: "AN, MF, and FL partially worked on ASIF when they were at Amazon
Web Services in Tübingen,\r\nGermany. This paper is financially supported by the
PRIN 2020 project no.2020TA3K9N (LEGO.AI), PNRR MUR project PE0000013-FAIR, and
ERC Grant no.802554 (SPECGEO)."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Antonio
full_name: Norelli, Antonio
last_name: Norelli
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Luca
full_name: Moschella, Luca
last_name: Moschella
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF:
Coupled data turns unimodal models to multimodal without training. In: 37th
Conference on Neural Information Processing Systems. Vol 36. Neural Information
Processing Systems Foundation; 2023:15303-15319.'
apa: 'Norelli, A., Fumero, M., Maiorca, V., Moschella, L., Rodolà, E., & Locatello,
F. (2023). ASIF: Coupled data turns unimodal models to multimodal without training.
In 37th Conference on Neural Information Processing Systems (Vol. 36, pp.
15303–15319). New Orleans, LA, United States: Neural Information Processing Systems
Foundation.'
chicago: 'Norelli, Antonio, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele
Rodolà, and Francesco Locatello. “ASIF: Coupled Data Turns Unimodal Models to
Multimodal without Training.” In 37th Conference on Neural Information Processing
Systems, 36:15303–19. Neural Information Processing Systems Foundation, 2023.'
ieee: 'A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello,
“ASIF: Coupled data turns unimodal models to multimodal without training,” in
37th Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2023, vol. 36, pp. 15303–15319.'
ista: 'Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. 2023.
ASIF: Coupled data turns unimodal models to multimodal without training. 37th
Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, Advances in Neural Information Processing Systems, vol. 36,
15303–15319.'
mla: 'Norelli, Antonio, et al. “ASIF: Coupled Data Turns Unimodal Models to Multimodal
without Training.” 37th Conference on Neural Information Processing Systems,
vol. 36, Neural Information Processing Systems Foundation, 2023, pp. 15303–19.'
short: A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello,
in:, 37th Conference on Neural Information Processing Systems, Neural Information
Processing Systems Foundation, 2023, pp. 15303–15319.
conference:
end_date: 2023-12-14
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2023-12-12
corr_author: '1'
date_created: 2023-08-22T14:22:04Z
date_published: 2023-10-04T00:00:00Z
date_updated: 2025-05-14T11:28:52Z
day: '04'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2210.01738'
file:
- access_level: open_access
checksum: e51c90300b92d7135050da5c9e3a8015
content_type: application/pdf
creator: dernst
date_created: 2025-02-04T12:16:13Z
date_updated: 2025-02-04T12:16:13Z
file_id: '18994'
file_name: 2023_NeurIPS_Fumero.pdf
file_size: 12648978
relation: main_file
success: 1
file_date_updated: 2025-02-04T12:16:13Z
has_accepted_license: '1'
intvolume: ' 36'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Preprint
page: 15303-15319
publication: 37th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713899921'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/noranta4/ASIF
status: public
title: 'ASIF: Coupled data turns unimodal models to multimodal without training'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2023'
...
---
_id: '14217'
abstract:
- lang: eng
text: 'Neural networks embed the geometric structure of a data manifold lying in
a high-dimensional space into latent representations. Ideally, the distribution
of the data points in the latent space should depend only on the task, the data,
the loss, and other architecture-specific constraints. However, factors such as
the random weights initialization, training hyperparameters, or other sources
of randomness in the training phase may induce incoherent latent spaces that hinder
any form of reuse. Nevertheless, we empirically observe that, under the same data
and modeling choices, the angles between the encodings within distinct latent
spaces do not change. In this work, we propose the latent similarity between each
sample and a fixed set of anchors as an alternative data representation, demonstrating
that it can enforce the desired invariances without any additional training. We
show how neural architectures can leverage these relative representations to guarantee,
in practice, invariance to latent isometries and rescalings, effectively enabling
latent space communication: from zero-shot model stitching to latent space comparison
between diverse settings. We extensively validate the generalization capability
of our approach on different datasets, spanning various modalities (images, text,
graphs), tasks (e.g., classification, reconstruction) and architectures (e.g.,
CNNs, GCNs, transformers).'
article_processing_charge: No
arxiv: 1
author:
- first_name: Luca
full_name: Moschella, Luca
last_name: Moschella
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Antonio
full_name: Norelli, Antonio
last_name: Norelli
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
citation:
ama: 'Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative
representations enable zero-shot latent space communication. In: The 11th International
Conference on Learning Representations. ; 2023.'
apa: Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., & Rodolà,
E. (2023). Relative representations enable zero-shot latent space communication.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco
Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent
Space Communication.” In The 11th International Conference on Learning Representations,
2023.
ieee: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà,
“Relative representations enable zero-shot latent space communication,” in The
11th International Conference on Learning Representations, Kigali, Rwanda,
2023.
ista: Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. 2023.
Relative representations enable zero-shot latent space communication. The 11th
International Conference on Learning Representations. International Conference
on Machine Learning Representations.
mla: Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space
Communication.” The 11th International Conference on Learning Representations,
2023.
short: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà,
in:, The 11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: International Conference on Machine Learning Representations
start_date: 2023-05-01
date_created: 2023-08-22T14:22:20Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T09:44:26Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.15430'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.15430
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Relative representations enable zero-shot latent space communication
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14218'
abstract:
- lang: eng
text: Humans naturally decompose their environment into entities at the appropriate
level of abstraction to act in the world. Allowing machine learning algorithms
to derive this decomposition in an unsupervised way has become an important line
of research. However, current methods are restricted to simulated data or require
additional information in the form of motion or depth in order to successfully
discover objects. In this work, we overcome this limitation by showing that reconstructing
features from models trained in a self-supervised manner is a sufficient training
signal for object-centric representations to arise in a fully unsupervised way.
Our approach, DINOSAUR, significantly out-performs existing image-based object-centric
learning models on simulated data and is the first unsupervised object-centric
model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR
is conceptually simple and shows competitive performance compared to more involved
pipelines from the computer vision literature.
article_processing_charge: No
arxiv: 1
author:
- first_name: Maximilian
full_name: Seitzer, Maximilian
last_name: Seitzer
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric
learning. In: The 11th International Conference on Learning Representations.
; 2023.'
apa: Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann
Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world
object-centric learning. In The 11th International Conference on Learning Representations.
Kigali, Rwanda.
chicago: Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun
Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging
the Gap to Real-World Object-Centric Learning.” In The 11th International Conference
on Learning Representations, 2023.
ieee: M. Seitzer et al., “Bridging the gap to real-world object-centric learning,”
in The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel
C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap
to real-world object-centric learning. The 11th International Conference on Learning
Representations. ICLR: International Conference on Learning Representations.'
mla: Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric
Learning.” The 11th International Conference on Learning Representations,
2023.
short: M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann
Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:41Z
date_published: 2023-05-10T00:00:00Z
date_updated: 2024-10-14T12:30:54Z
day: '10'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.14860'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.14860
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Bridging the gap to real-world object-centric learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14219'
abstract:
- lang: eng
text: "In this paper, we show that recent advances in self-supervised feature\r\nlearning
enable unsupervised object discovery and semantic segmentation with a\r\nperformance
that matches the state of the field on supervised semantic\r\nsegmentation 10
years ago. We propose a methodology based on unsupervised\r\nsaliency masks and
self-supervised feature clustering to kickstart object\r\ndiscovery followed by
training a semantic segmentation network on pseudo-labels\r\nto bootstrap the
system on images with multiple objects. We present results on\r\nPASCAL VOC that
go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the
first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers
34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of
19.6 for all 81 categories."
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Matthaeus
full_name: Kleindessner, Matthaeus
last_name: Kleindessner
- first_name: Yi
full_name: Zhu, Yi
last_name: Zhu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
citation:
ama: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic
segmentation with self-supervised object-centric representations. In: The 11th
International Conference on Learning Representations. ; 2023.'
apa: Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., & Brox, T. (2023).
Unsupervised semantic segmentation with self-supervised object-centric representations.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello,
and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric
Representations.” In The 11th International Conference on Learning Representations,
2023.
ieee: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised
semantic segmentation with self-supervised object-centric representations,” in
The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised
semantic segmentation with self-supervised object-centric representations. The
11th International Conference on Learning Representations. ICLR: International
Conference on Learning Representations.'
mla: Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised
Object-Centric Representations.” The 11th International Conference on Learning
Representations, 2023.
short: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:58Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T11:25:43Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2207.05027'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2207.05027
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised semantic segmentation with self-supervised object-centric representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14222'
abstract:
- lang: eng
text: Learning generative object models from unlabelled videos is a long standing
problem and required for causal scene modeling. We decompose this problem into
three easier subtasks, and provide candidate solutions for each of them. Inspired
by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks
of moving objects via unsupervised motion segmentation. Second, generative models
are trained on the masks of the background and the moving objects, respectively.
Third, background and foreground models are combined in a conditional "dead leaves"
scene model to sample novel scene configurations where occlusions and depth layering
arise naturally. To evaluate the individual stages, we introduce the Fishbowl
dataset positioned between complex real-world scenes and common object-centric
benchmarks of simplistic objects. We show that our approach allows learning generative
models that generalize beyond the occlusions present in the input videos, and
represent scenes in a modular fashion that allows sampling plausible scenes outside
the training distribution by permitting, for instance, object numbers or densities
not observed in the training set.
article_number: '2110.06562'
article_processing_charge: No
arxiv: 1
author:
- first_name: Matthias
full_name: Tangemann, Matthias
last_name: Tangemann
- first_name: Steffen
full_name: Schneider, Steffen
last_name: Schneider
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Matthias
full_name: Kümmerer, Matthias
last_name: Kümmerer
- first_name: Matthias
full_name: Bethge, Matthias
last_name: Bethge
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning
via common fate. In: 2nd Conference on Causal Learning and Reasoning. ;
2023.'
apa: Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P.,
Brox, T., … Schölkopf, B. (2023). Unsupervised object learning via common fate.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco
Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and
Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: M. Tangemann et al., “Unsupervised object learning via common fate,”
in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany,
2023.
ista: 'Tangemann M, Schneider S, Kügelgen J von, Locatello F, Gehler P, Brox T,
Kümmerer M, Bethge M, Schölkopf B. 2023. Unsupervised object learning via common
fate. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal
Learning and Reasoning, 2110.06562.'
mla: Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.”
2nd Conference on Causal Learning and Reasoning, 2110.06562, 2023.
short: M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T.
Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning
and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:23:54Z
date_published: 2023-04-15T00:00:00Z
date_updated: 2023-09-13T11:31:14Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.06562'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2110.06562
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised object learning via common fate
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14238'
abstract:
- lang: eng
text: We demonstrate that a sodium dimer, Na2(13Σ+u), residing on the surface of
a helium nanodroplet, can be set into rotation by a nonresonant 1.0 ps infrared
laser pulse. The time-dependent degree of alignment measured, exhibits a periodic,
gradually decreasing structure that deviates qualitatively from that expected
for gas-phase dimers. Comparison to alignment dynamics calculated from the time-dependent
rotational Schrödinger equation shows that the deviation is due to the alignment
dependent interaction between the dimer and the droplet surface. This interaction
confines the dimer to the tangential plane of the droplet surface at the point
where it resides and is the reason that the observed alignment dynamics is also
well described by a 2D quantum rotor model.
acknowledgement: H. S. acknowledges support from The Villum Foundation through a Villum
Investigator Grant No. 25886. M. L. acknowledges support by the European Research
Council (ERC) Starting Grant No. 801770 (ANGULON). F. J. and R. E. Z. acknowledge
support from the Centre for Scientific Computing, Aarhus and the JKU scientific
computing administration, Linz, respectively.
article_number: '053201'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Lorenz
full_name: Kranabetter, Lorenz
last_name: Kranabetter
- first_name: Henrik H.
full_name: Kristensen, Henrik H.
last_name: Kristensen
- first_name: Areg
full_name: Ghazaryan, Areg
id: 4AF46FD6-F248-11E8-B48F-1D18A9856A87
last_name: Ghazaryan
orcid: 0000-0001-9666-3543
- first_name: Constant A.
full_name: Schouder, Constant A.
last_name: Schouder
- first_name: Adam S.
full_name: Chatterley, Adam S.
last_name: Chatterley
- first_name: Paul
full_name: Janssen, Paul
last_name: Janssen
- first_name: Frank
full_name: Jensen, Frank
last_name: Jensen
- first_name: Robert E.
full_name: Zillich, Robert E.
last_name: Zillich
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
- first_name: Henrik
full_name: Stapelfeldt, Henrik
last_name: Stapelfeldt
citation:
ama: Kranabetter L, Kristensen HH, Ghazaryan A, et al. Nonadiabatic laser-induced
alignment dynamics of molecules on a surface. Physical Review Letters.
2023;131(5). doi:10.1103/PhysRevLett.131.053201
apa: Kranabetter, L., Kristensen, H. H., Ghazaryan, A., Schouder, C. A., Chatterley,
A. S., Janssen, P., … Stapelfeldt, H. (2023). Nonadiabatic laser-induced alignment
dynamics of molecules on a surface. Physical Review Letters. American Physical
Society. https://doi.org/10.1103/PhysRevLett.131.053201
chicago: Kranabetter, Lorenz, Henrik H. Kristensen, Areg Ghazaryan, Constant A.
Schouder, Adam S. Chatterley, Paul Janssen, Frank Jensen, Robert E. Zillich, Mikhail
Lemeshko, and Henrik Stapelfeldt. “Nonadiabatic Laser-Induced Alignment Dynamics
of Molecules on a Surface.” Physical Review Letters. American Physical
Society, 2023. https://doi.org/10.1103/PhysRevLett.131.053201.
ieee: L. Kranabetter et al., “Nonadiabatic laser-induced alignment dynamics
of molecules on a surface,” Physical Review Letters, vol. 131, no. 5. American
Physical Society, 2023.
ista: Kranabetter L, Kristensen HH, Ghazaryan A, Schouder CA, Chatterley AS, Janssen
P, Jensen F, Zillich RE, Lemeshko M, Stapelfeldt H. 2023. Nonadiabatic laser-induced
alignment dynamics of molecules on a surface. Physical Review Letters. 131(5),
053201.
mla: Kranabetter, Lorenz, et al. “Nonadiabatic Laser-Induced Alignment Dynamics
of Molecules on a Surface.” Physical Review Letters, vol. 131, no. 5, 053201,
American Physical Society, 2023, doi:10.1103/PhysRevLett.131.053201.
short: L. Kranabetter, H.H. Kristensen, A. Ghazaryan, C.A. Schouder, A.S. Chatterley,
P. Janssen, F. Jensen, R.E. Zillich, M. Lemeshko, H. Stapelfeldt, Physical Review
Letters 131 (2023).
date_created: 2023-08-27T22:01:16Z
date_published: 2023-08-04T00:00:00Z
date_updated: 2025-04-14T07:48:54Z
day: '04'
department:
- _id: MiLe
doi: 10.1103/PhysRevLett.131.053201
ec_funded: 1
external_id:
arxiv:
- '2308.15247'
isi:
- '001101784100001'
pmid:
- '37595218'
intvolume: ' 131'
isi: 1
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2308.15247
month: '08'
oa: 1
oa_version: Preprint
pmid: 1
project:
- _id: 2688CF98-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '801770'
name: 'Angulon: physics and applications of a new quasiparticle'
publication: Physical Review Letters
publication_identifier:
eissn:
- 1079-7114
issn:
- 0031-9007
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Nonadiabatic laser-induced alignment dynamics of molecules on a surface
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 131
year: '2023'
...
---
_id: '14239'
abstract:
- lang: eng
text: "Given a resolution of rational singularities π:X~→X over a field of characteristic
zero, we use a Hodge-theoretic argument to prove that the image of the functor
\ Rπ∗:Db(X~)→Db(X)\r\n between bounded derived categories of coherent sheaves
generates Db(X)\r\n as a triangulated category. This gives a weak version of
the Bondal–Orlov localization conjecture [BO02], answering a question from [PS21].
The same result is established more generally for proper (not necessarily birational)
morphisms π:X~→X , with X~\r\n smooth, satisfying Rπ∗(OX~)=OX ."
acknowledgement: "We thank Agnieszka Bodzenta-Skibińska, Paolo Cascini, Wahei Hara,
Sándor Kovács, Alexander Kuznetsov, Mircea Musta ă, Nebojsa Pavic, Pavel Sechin,
and Michael Wemyss for discussions and e-mail correspondence. We also thank the
anonymous referee for the helpful comments. M.M. was supported by the Institute
of Science and Technology Austria. This project has received funding from the European
Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie
grant agreement no. 101034413. E.S. was partially supported by the EPSRC grant EP/T019379/1
“Derived categories and algebraic K-theory of singularities”, and by the ERC Synergy
grant “Modern Aspects of Geometry: Categories, Cycles and Cohomology of Hyperkähler
Varieties.”\r\n\r\n"
article_number: e66
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Mirko
full_name: Mauri, Mirko
id: 2cf70c34-09c1-11ed-bd8d-c34fac206130
last_name: Mauri
- first_name: Evgeny
full_name: Shinder, Evgeny
last_name: Shinder
citation:
ama: Mauri M, Shinder E. Homological Bondal-Orlov localization conjecture for rational
singularities. Forum of Mathematics, Sigma. 2023;11. doi:10.1017/fms.2023.65
apa: Mauri, M., & Shinder, E. (2023). Homological Bondal-Orlov localization
conjecture for rational singularities. Forum of Mathematics, Sigma. Cambridge
University Press. https://doi.org/10.1017/fms.2023.65
chicago: Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization
Conjecture for Rational Singularities.” Forum of Mathematics, Sigma. Cambridge
University Press, 2023. https://doi.org/10.1017/fms.2023.65.
ieee: M. Mauri and E. Shinder, “Homological Bondal-Orlov localization conjecture
for rational singularities,” Forum of Mathematics, Sigma, vol. 11. Cambridge
University Press, 2023.
ista: Mauri M, Shinder E. 2023. Homological Bondal-Orlov localization conjecture
for rational singularities. Forum of Mathematics, Sigma. 11, e66.
mla: Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization Conjecture
for Rational Singularities.” Forum of Mathematics, Sigma, vol. 11, e66,
Cambridge University Press, 2023, doi:10.1017/fms.2023.65.
short: M. Mauri, E. Shinder, Forum of Mathematics, Sigma 11 (2023).
corr_author: '1'
date_created: 2023-08-27T22:01:16Z
date_published: 2023-08-03T00:00:00Z
date_updated: 2025-04-14T07:54:52Z
day: '03'
ddc:
- '510'
department:
- _id: TaHa
doi: 10.1017/fms.2023.65
ec_funded: 1
external_id:
arxiv:
- '2212.06786'
isi:
- '001041926700001'
file:
- access_level: open_access
checksum: c36241750cc5cb06890aec0ecdfee626
content_type: application/pdf
creator: dernst
date_created: 2023-09-05T06:43:11Z
date_updated: 2023-09-05T06:43:11Z
file_id: '14266'
file_name: 2023_ForumMathematics_Mauri.pdf
file_size: 280865
relation: main_file
success: 1
file_date_updated: 2023-09-05T06:43:11Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Forum of Mathematics, Sigma
publication_identifier:
eissn:
- 2050-5094
publication_status: published
publisher: Cambridge University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Homological Bondal-Orlov localization conjecture for rational singularities
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2023'
...
---
_id: '14240'
abstract:
- lang: eng
text: This paper introduces a novel method for simulating large bodies of water
as a height field. At the start of each time step, we partition the waves into
a bulk flow (which approximately satisfies the assumptions of the shallow water
equations) and surface waves (which approximately satisfy the assumptions of Airy
wave theory). We then solve the two wave regimes separately using appropriate
state-of-the-art techniques, and re-combine the resulting wave velocities at the
end of each step. This strategy leads to the first heightfield wave model capable
of simulating complex interactions between both deep and shallow water effects,
like the waves from a boat wake sloshing up onto a beach, or a dam break producing
wave interference patterns and eddies. We also analyze the numerical dispersion
created by our method and derive an exact correction factor for waves at a constant
water depth, giving us a numerically perfect re-creation of theoretical water
wave dispersion patterns.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank Georg Sperl for helping with early research for this paper,
Mickael Ly and Yi-Lu Chen for proofreading, and members of the ISTA Visual Computing
Group for general feedback. This project was funded in part by the European Research
Council (ERC Consolidator Grant 101045083 CoDiNA).\r\nThe motorboat and sailboat
were modeled by Sergei and the palmtrees by YadroGames. The environment map was
created by Emil Persson."
article_number: '83'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Stefan
full_name: Jeschke, Stefan
id: 44D6411A-F248-11E8-B48F-1D18A9856A87
last_name: Jeschke
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: Jeschke S, Wojtan C. Generalizing shallow water simulations with dispersive
surface waves. ACM Transactions on Graphics. 2023;42(4). doi:10.1145/3592098
apa: Jeschke, S., & Wojtan, C. (2023). Generalizing shallow water simulations
with dispersive surface waves. ACM Transactions on Graphics. Association
for Computing Machinery. https://doi.org/10.1145/3592098
chicago: Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations
with Dispersive Surface Waves.” ACM Transactions on Graphics. Association
for Computing Machinery, 2023. https://doi.org/10.1145/3592098.
ieee: S. Jeschke and C. Wojtan, “Generalizing shallow water simulations with dispersive
surface waves,” ACM Transactions on Graphics, vol. 42, no. 4. Association
for Computing Machinery, 2023.
ista: Jeschke S, Wojtan C. 2023. Generalizing shallow water simulations with dispersive
surface waves. ACM Transactions on Graphics. 42(4), 83.
mla: Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations
with Dispersive Surface Waves.” ACM Transactions on Graphics, vol. 42,
no. 4, 83, Association for Computing Machinery, 2023, doi:10.1145/3592098.
short: S. Jeschke, C. Wojtan, ACM Transactions on Graphics 42 (2023).
corr_author: '1'
date_created: 2023-08-27T22:01:17Z
date_published: 2023-08-01T00:00:00Z
date_updated: 2025-04-14T08:01:13Z
day: '01'
ddc:
- '000'
department:
- _id: ChWo
doi: 10.1145/3592098
external_id:
isi:
- '001044671300049'
file:
- access_level: open_access
checksum: 1d178bb2f8011d9f5aedda6427e18c7a
content_type: video/mp4
creator: sjeschke
date_created: 2023-12-21T12:26:40Z
date_updated: 2023-12-21T12:26:40Z
file_id: '14704'
file_name: PaperVideo_final.mp4
file_size: 511572575
relation: main_file
success: 1
- access_level: open_access
checksum: a49b2e744d5cd1276bb8b2e0ce6dc638
content_type: application/pdf
creator: dernst
date_created: 2024-01-02T09:34:27Z
date_updated: 2024-01-02T09:34:27Z
file_id: '14725'
file_name: 2023_ACMToG_Jeschke.pdf
file_size: 7469177
relation: main_file
success: 1
file_date_updated: 2024-01-02T09:34:27Z
has_accepted_license: '1'
intvolume: ' 42'
isi: 1
issue: '4'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: ACM Transactions on Graphics
publication_identifier:
eissn:
- 1557-7368
issn:
- 0730-0301
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Generalizing shallow water simulations with dispersive surface waves
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 42
year: '2023'
...
---
_id: '14241'
abstract:
- lang: eng
text: We present a technique to optimize the reflectivity of a surface while preserving
its overall shape. The naïve optimization of the mesh vertices using the gradients
of reflectivity simulations results in undesirable distortion. In contrast, our
robust formulation optimizes the surface normal as an independent variable that
bridges the reflectivity term with differential rendering, and the regularization
term with as-rigid-as-possible elastic energy. We further adaptively subdivide
the input mesh to improve the convergence. Consequently, our method can minimize
the retroreflectivity of a wide range of input shapes, resulting in sharply creased
shapes ubiquitous among stealth aircraft and Sci-Fi vehicles. Furthermore, by
changing the reward for the direction of the outgoing light directions, our method
can be applied to other reflectivity design tasks, such as the optimization of
architectural walls to concentrate light in a specific region. We have tested
the proposed method using light-transport simulations and real-world 3D-printed
objects.
acknowledgement: "The authors would like to thank Yuki Koyama and Takeo Igarashi for
early discussions, and Yuta Yaguchi for support in 3D printing. This research is
partially supported by the Israel Science Foundation grant number 1390/19.\r\n"
article_number: '20'
article_processing_charge: No
arxiv: 1
author:
- first_name: Kenji
full_name: Tojo, Kenji
last_name: Tojo
- first_name: Ariel
full_name: Shamir, Ariel
last_name: Shamir
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
- first_name: Nobuyuki
full_name: Umetani, Nobuyuki
last_name: Umetani
citation:
ama: 'Tojo K, Shamir A, Bickel B, Umetani N. Stealth shaper: Reflectivity optimization
as surface stylization. In: SIGGRAPH 2023 Conference Proceedings. Association
for Computing Machinery; 2023. doi:10.1145/3588432.3591542'
apa: 'Tojo, K., Shamir, A., Bickel, B., & Umetani, N. (2023). Stealth shaper:
Reflectivity optimization as surface stylization. In SIGGRAPH 2023 Conference
Proceedings. Los Angeles, CA, United States: Association for Computing Machinery.
https://doi.org/10.1145/3588432.3591542'
chicago: 'Tojo, Kenji, Ariel Shamir, Bernd Bickel, and Nobuyuki Umetani. “Stealth
Shaper: Reflectivity Optimization as Surface Stylization.” In SIGGRAPH 2023
Conference Proceedings. Association for Computing Machinery, 2023. https://doi.org/10.1145/3588432.3591542.'
ieee: 'K. Tojo, A. Shamir, B. Bickel, and N. Umetani, “Stealth shaper: Reflectivity
optimization as surface stylization,” in SIGGRAPH 2023 Conference Proceedings,
Los Angeles, CA, United States, 2023.'
ista: 'Tojo K, Shamir A, Bickel B, Umetani N. 2023. Stealth shaper: Reflectivity
optimization as surface stylization. SIGGRAPH 2023 Conference Proceedings. SIGGRAPH:
Computer Graphics and Interactive Techniques Conference, 20.'
mla: 'Tojo, Kenji, et al. “Stealth Shaper: Reflectivity Optimization as Surface
Stylization.” SIGGRAPH 2023 Conference Proceedings, 20, Association for
Computing Machinery, 2023, doi:10.1145/3588432.3591542.'
short: K. Tojo, A. Shamir, B. Bickel, N. Umetani, in:, SIGGRAPH 2023 Conference
Proceedings, Association for Computing Machinery, 2023.
conference:
end_date: 2023-08-10
location: Los Angeles, CA, United States
name: 'SIGGRAPH: Computer Graphics and Interactive Techniques Conference'
start_date: 2023-08-06
corr_author: '1'
date_created: 2023-08-27T22:01:17Z
date_published: 2023-07-23T00:00:00Z
date_updated: 2025-09-09T12:49:15Z
day: '23'
department:
- _id: BeBi
doi: 10.1145/3588432.3591542
external_id:
arxiv:
- '2305.05944'
isi:
- '001117690500020'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2305.05944
month: '07'
oa: 1
oa_version: Preprint
publication: SIGGRAPH 2023 Conference Proceedings
publication_identifier:
isbn:
- '9798400701597'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Stealth shaper: Reflectivity optimization as surface stylization'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2023'
...
---
_id: '14242'
abstract:
- lang: eng
text: We study the problem of training and certifying adversarially robust quantized
neural networks (QNNs). Quantization is a technique for making neural networks
more efficient by running them using low-bit integer arithmetic and is therefore
commonly adopted in industry. Recent work has shown that floating-point neural
networks that have been verified to be robust can become vulnerable to adversarial
attacks after quantization, and certification of the quantized representation
is necessary to guarantee robustness. In this work, we present quantization-aware
interval bound propagation (QA-IBP), a novel method for training robust QNNs.
Inspired by advances in robust learning of non-quantized networks, our training
algorithm computes the gradient of an abstract representation of the actual network.
Unlike existing approaches, our method can handle the discrete semantics of QNNs.
Based on QA-IBP, we also develop a complete verification procedure for verifying
the adversarial robustness of QNNs, which is guaranteed to terminate and produce
a correct answer. Compared to existing approaches, the key advantage of our verification
procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate
experimentally that our approach significantly outperforms existing methods and
establish the new state-of-the-art for training and certifying the robustness
of QNNs.
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. Research
was sponsored by the United\r\nStates Air Force Research Laboratory and the United
States Air Force Artificial Intelligence Accelerator and was accomplished under
Cooperative Agreement Number FA8750-19-2-\r\n1000. The views and conclusions contained
in this document are those of the authors and should not be interpreted as representing
the official policies, either expressed or implied,\r\nof the United States Air
Force or the U.S. Government. The U.S. Government is authorized to reproduce and
distribute reprints for Government purposes notwithstanding any copyright\r\nnotation
herein. The research was also funded in part by the AI2050 program at Schmidt Futures
(Grant G-22-63172) and Capgemini SE."
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
citation:
ama: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. Association for the Advancement of Artificial Intelligence; 2023:14964-14973.
doi:10.1609/aaai.v37i12.26747'
apa: 'Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., & Rus, D.
(2023). Quantization-aware interval bound propagation for training certifiably
robust quantized neural networks. In Proceedings of the 37th AAAI Conference
on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United
States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i12.26747'
chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, Thomas A Henzinger,
and Daniela Rus. “Quantization-Aware Interval Bound Propagation for Training Certifiably
Robust Quantized Neural Networks.” In Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 37:14964–73. Association for the Advancement of
Artificial Intelligence, 2023. https://doi.org/10.1609/aaai.v37i12.26747.
ieee: M. Lechner, D. Zikelic, K. Chatterjee, T. A. Henzinger, and D. Rus, “Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks,”
in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 12, pp. 14964–14973.
ista: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. 2023. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference
on Artificial Intelligence vol. 37, 14964–14973.'
mla: Lechner, Mathias, et al. “Quantization-Aware Interval Bound Propagation for
Training Certifiably Robust Quantized Neural Networks.” Proceedings of the
37th AAAI Conference on Artificial Intelligence, vol. 37, no. 12, Association
for the Advancement of Artificial Intelligence, 2023, pp. 14964–73, doi:10.1609/aaai.v37i12.26747.
short: M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, D. Rus, in:, Proceedings
of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement
of Artificial Intelligence, 2023, pp. 14964–14973.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:17Z
date_published: 2023-06-26T00:00:00Z
date_updated: 2025-03-31T16:01:08Z
day: '26'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i12.26747
ec_funded: 1
external_id:
arxiv:
- '2211.16187'
intvolume: ' 37'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2211.16187
month: '06'
oa: 1
oa_version: Preprint
page: 14964-14973
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781577358800'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantization-aware interval bound propagation for training certifiably robust
quantized neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '14243'
abstract:
- lang: eng
text: 'Two-player zero-sum "graph games" are central in logic, verification, and
multi-agent systems. The game proceeds by placing a token on a vertex of a graph,
and allowing the players to move it to produce an infinite path, which determines
the winner or payoff of the game. Traditionally, the players alternate turns in
moving the token. In "bidding games", however, the players have budgets and in
each turn, an auction (bidding) determines which player moves the token. So far,
bidding games have only been studied as full-information games. In this work we
initiate the study of partial-information bidding games: we study bidding games
in which a player''s initial budget is drawn from a known probability distribution.
We show that while for some bidding mechanisms and objectives, it is straightforward
to adapt the results from the full-information setting to the partial-information
setting, for others, the analysis is significantly more challenging, requires
new techniques, and gives rise to interesting results. Specifically, we study
games with "mean-payoff" objectives in combination with "poorman" bidding. We
construct optimal strategies for a partially-informed player who plays against
a fully-informed adversary. We show that, somewhat surprisingly, the "value" under
pure strategies does not necessarily exist in such games.'
acknowledgement: This research was supported in part by ISF grant no.1679/21, by the
ERC CoG 863818 (ForM-SMArt), and the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.
article_processing_charge: No
arxiv: 1
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Ismael R
full_name: Jecker, Ismael R
id: 85D7C63E-7D5D-11E9-9C0F-98C4E5697425
last_name: Jecker
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Avni G, Jecker IR, Zikelic D. Bidding graph games with partially-observable
budgets. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. ; 2023:5464-5471. doi:10.1609/aaai.v37i5.25679'
apa: Avni, G., Jecker, I. R., & Zikelic, D. (2023). Bidding graph games with
partially-observable budgets. In Proceedings of the 37th AAAI Conference on
Artificial Intelligence (Vol. 37, pp. 5464–5471). Washington, DC, United States.
https://doi.org/10.1609/aaai.v37i5.25679
chicago: Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Bidding Graph Games with
Partially-Observable Budgets.” In Proceedings of the 37th AAAI Conference on
Artificial Intelligence, 37:5464–71, 2023. https://doi.org/10.1609/aaai.v37i5.25679.
ieee: G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable
budgets,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 5, pp. 5464–5471.
ista: 'Avni G, Jecker IR, Zikelic D. 2023. Bidding graph games with partially-observable
budgets. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI:
Conference on Artificial Intelligence vol. 37, 5464–5471.'
mla: Avni, Guy, et al. “Bidding Graph Games with Partially-Observable Budgets.”
Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol.
37, no. 5, 2023, pp. 5464–71, doi:10.1609/aaai.v37i5.25679.
short: G. Avni, I.R. Jecker, D. Zikelic, in:, Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 2023, pp. 5464–5471.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:18Z
date_published: 2023-06-27T00:00:00Z
date_updated: 2025-03-31T16:01:08Z
day: '27'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i5.25679
ec_funded: 1
external_id:
arxiv:
- '2211.13626'
intvolume: ' 37'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1609/aaai.v37i5.25679
month: '06'
oa: 1
oa_version: Published Version
page: 5464-5471
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781577358800'
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Bidding graph games with partially-observable budgets
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '14244'
abstract:
- lang: eng
text: "In this paper, we determine the motivic class — in particular, the weight
polynomial and conjecturally the Poincaré polynomial — of the open de Rham space,
defined and studied by Boalch, of certain moduli spaces of irregular meromorphic
connections on the trivial rank \r\n bundle on P1. The computation is by motivic
Fourier transform. We show that the result satisfies the purity conjecture, that
is, it agrees with the pure part of the conjectured mixed Hodge polynomial of
the corresponding wild character variety. We also identify the open de Rham spaces
with quiver varieties with multiplicities of Yamakawa and Geiss–Leclerc–Schröer.
We finish with constructing natural complete hyperkähler metrics on them, which
in the four-dimensional cases are expected to be of type ALF."
acknowledgement: We would like to thank Gergely Bérczy, Roger Bielawski, Philip Boalch,
Sergey Cherkis, Andrew Dancer, Brent Doran, Eloïse Hamilton, Frances Kirwan, Bernard
Leclerc, Emmanuel Letellier, Alessia Mandini, Maxence Mayrand, András Némethi, Szilárd
Szabó, and Daisuke Yamakawa for discussions related to the paper. We especially
thank the referee for an extensive list of very careful comments. At various stages
of this project, the authors were supported by the Advanced Grant “Arithmetic and
physics of Higgs moduli spaces” no. 320593 of the European Research Council, by
grant no. 153627 and NCCR SwissMAP, both funded by the Swiss National Science Foundation
as well as by EPF Lausanne and IST Austria. In the final stages of this project,
MLW was supported by SFB/TR 45 “Periods, moduli and arithmetic of algebraic varieties,”
subproject M08-10 “Moduli of vector bundles on higher-dimensional varieties.” DW
was also supported by the Fondation Sciences Mathématiques de Paris, as well as
public grants overseen by the Agence national de la recherche (ANR) of France as
part of the Investissements d'avenir program, under reference numbers ANR-10-LABX-0098
and ANR-15-CE40-0008 (Défigéo).
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Tamás
full_name: Hausel, Tamás
id: 4A0666D8-F248-11E8-B48F-1D18A9856A87
last_name: Hausel
orcid: 0000-0002-9582-2634
- first_name: Michael Lennox
full_name: Wong, Michael Lennox
last_name: Wong
- first_name: Dimitri
full_name: Wyss, Dimitri
last_name: Wyss
citation:
ama: Hausel T, Wong ML, Wyss D. Arithmetic and metric aspects of open de Rham spaces.
Proceedings of the London Mathematical Society. 2023;127(4):958-1027. doi:10.1112/plms.12555
apa: Hausel, T., Wong, M. L., & Wyss, D. (2023). Arithmetic and metric aspects
of open de Rham spaces. Proceedings of the London Mathematical Society.
Wiley. https://doi.org/10.1112/plms.12555
chicago: Hausel, Tamás, Michael Lennox Wong, and Dimitri Wyss. “Arithmetic and Metric
Aspects of Open de Rham Spaces.” Proceedings of the London Mathematical Society.
Wiley, 2023. https://doi.org/10.1112/plms.12555.
ieee: T. Hausel, M. L. Wong, and D. Wyss, “Arithmetic and metric aspects of open
de Rham spaces,” Proceedings of the London Mathematical Society, vol. 127,
no. 4. Wiley, pp. 958–1027, 2023.
ista: Hausel T, Wong ML, Wyss D. 2023. Arithmetic and metric aspects of open de
Rham spaces. Proceedings of the London Mathematical Society. 127(4), 958–1027.
mla: Hausel, Tamás, et al. “Arithmetic and Metric Aspects of Open de Rham Spaces.”
Proceedings of the London Mathematical Society, vol. 127, no. 4, Wiley,
2023, pp. 958–1027, doi:10.1112/plms.12555.
short: T. Hausel, M.L. Wong, D. Wyss, Proceedings of the London Mathematical Society
127 (2023) 958–1027.
corr_author: '1'
date_created: 2023-08-27T22:01:18Z
date_published: 2023-10-01T00:00:00Z
date_updated: 2025-04-14T09:12:46Z
day: '01'
ddc:
- '510'
department:
- _id: TaHa
doi: 10.1112/plms.12555
ec_funded: 1
external_id:
arxiv:
- '1807.04057'
isi:
- '001049312700001'
file:
- access_level: open_access
checksum: 2af4d2d6a8ae42f7d3fba0188e79ae82
content_type: application/pdf
creator: dernst
date_created: 2024-01-30T12:56:00Z
date_updated: 2024-01-30T12:56:00Z
file_id: '14910'
file_name: 2023_ProcLondonMathSoc_Hausel.pdf
file_size: 651335
relation: main_file
success: 1
file_date_updated: 2024-01-30T12:56:00Z
has_accepted_license: '1'
intvolume: ' 127'
isi: 1
issue: '4'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 958-1027
project:
- _id: 25E549F4-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '320593'
name: Arithmetic and physics of Higgs moduli spaces
- _id: 25E6C798-B435-11E9-9278-68D0E5697425
grant_number: '153627'
name: Arithmetic quantization of character and quiver varities
publication: Proceedings of the London Mathematical Society
publication_identifier:
eissn:
- 1460-244X
issn:
- 0024-6115
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Arithmetic and metric aspects of open de Rham spaces
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 127
year: '2023'
...
---
_id: '14245'
abstract:
- lang: eng
text: We establish effective counting results for lattice points in families of
domains in real, complex and quaternionic hyperbolic spaces of any dimension.
The domains we focus on are defined as product sets with respect to an Iwasawa
decomposition. Several natural diophantine problems can be reduced to counting
lattice points in such domains. These include equidistribution of the ratio of
the length of the shortest solution (x,y) to the gcd equation bx−ay=1 relative
to the length of (a,b), where (a,b) ranges over primitive vectors in a disc whose
radius increases, the natural analog of this problem in imaginary quadratic number
fields, as well as equidistribution of integral solutions to the diophantine equation
defined by an integral Lorentz form in three or more variables. We establish an
effective rate of convergence for these equidistribution problems, depending on
the size of the spectral gap associated with a suitable lattice subgroup in the
isometry group of the relevant hyperbolic space. The main result underlying our
discussion amounts to establishing effective joint equidistribution for the horospherical
component and the radial component in the Iwasawa decomposition of lattice elements.
acknowledgement: The authors thank the referee for important comments which led to
significant improvements is the presentation of several results in the paper. They
also thank Ami Paz for preparing the figures for this paper. Horesh thanks Ami Paz
and Yakov Karasik for helpful discussions. Nevo thanks John Parker and Rene Rühr
for providing some very useful references. Nevo is supported by ISF Grant No. 2095/15.
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Tal
full_name: Horesh, Tal
id: C8B7BF48-8D81-11E9-BCA9-F536E6697425
last_name: Horesh
- first_name: Amos
full_name: Nevo, Amos
last_name: Nevo
citation:
ama: 'Horesh T, Nevo A. Horospherical coordinates of lattice points in hyperbolic
spaces: Effective counting and equidistribution. Pacific Journal of Mathematics.
2023;324(2):265-294. doi:10.2140/pjm.2023.324.265'
apa: 'Horesh, T., & Nevo, A. (2023). Horospherical coordinates of lattice points
in hyperbolic spaces: Effective counting and equidistribution. Pacific Journal
of Mathematics. Mathematical Sciences Publishers. https://doi.org/10.2140/pjm.2023.324.265'
chicago: 'Horesh, Tal, and Amos Nevo. “Horospherical Coordinates of Lattice Points
in Hyperbolic Spaces: Effective Counting and Equidistribution.” Pacific Journal
of Mathematics. Mathematical Sciences Publishers, 2023. https://doi.org/10.2140/pjm.2023.324.265.'
ieee: 'T. Horesh and A. Nevo, “Horospherical coordinates of lattice points in hyperbolic
spaces: Effective counting and equidistribution,” Pacific Journal of Mathematics,
vol. 324, no. 2. Mathematical Sciences Publishers, pp. 265–294, 2023.'
ista: 'Horesh T, Nevo A. 2023. Horospherical coordinates of lattice points in hyperbolic
spaces: Effective counting and equidistribution. Pacific Journal of Mathematics.
324(2), 265–294.'
mla: 'Horesh, Tal, and Amos Nevo. “Horospherical Coordinates of Lattice Points in
Hyperbolic Spaces: Effective Counting and Equidistribution.” Pacific Journal
of Mathematics, vol. 324, no. 2, Mathematical Sciences Publishers, 2023, pp.
265–94, doi:10.2140/pjm.2023.324.265.'
short: T. Horesh, A. Nevo, Pacific Journal of Mathematics 324 (2023) 265–294.
corr_author: '1'
date_created: 2023-08-27T22:01:18Z
date_published: 2023-07-26T00:00:00Z
date_updated: 2024-10-09T21:06:46Z
day: '26'
ddc:
- '510'
department:
- _id: TiBr
doi: 10.2140/pjm.2023.324.265
external_id:
arxiv:
- '1612.08215'
isi:
- '001047690500001'
file:
- access_level: open_access
checksum: a675b53cfb31fa46be1e879b7e77fe8c
content_type: application/pdf
creator: dernst
date_created: 2023-09-05T07:26:17Z
date_updated: 2023-09-05T07:26:17Z
file_id: '14267'
file_name: 2023_PacificJourMaths_Horesh.pdf
file_size: 654895
relation: main_file
success: 1
file_date_updated: 2023-09-05T07:26:17Z
has_accepted_license: '1'
intvolume: ' 324'
isi: 1
issue: '2'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 265-294
publication: Pacific Journal of Mathematics
publication_identifier:
eissn:
- 1945-5844
issn:
- 0030-8730
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Horospherical coordinates of lattice points in hyperbolic spaces: Effective
counting and equidistribution'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 324
year: '2023'
...
---
_id: '14246'
abstract:
- lang: eng
text: The model of a ring threaded by the Aharonov-Bohm flux underlies our understanding
of a coupling between gauge potentials and matter. The typical formulation of
the model is based upon a single particle picture, and should be extended when
interactions with other particles become relevant. Here, we illustrate such an
extension for a particle in an Aharonov-Bohm ring subject to interactions with
a weakly interacting Bose gas. We show that the ground state of the system can
be described using the Bose-polaron concept—a particle dressed by interactions
with a bosonic environment. We connect the energy spectrum to the effective mass
of the polaron, and demonstrate how to change currents in the system by tuning
boson-particle interactions. Our results suggest the Aharonov-Bohm ring as a platform
for studying coherence and few- to many-body crossover of quasi-particles that
arise from an impurity immersed in a medium.
acknowledgement: "Open Access funding enabled and organized by Projekt DEAL.\r\nWe
would like to thank Jonas Jager for sharing his data with us in the early stages
of this project. We thank Joachim Brand and Ray Yang for sharing with us data from
Yang et al.46. This work has received funding from the DFG Project no. 413495248
[VO 2437/1-1] (F.B., H.-W.H., A.G.V.). We acknowledge support from the Deutsche
Forschungsgemeinschaft (DFG - German Research Foundation) and the Open Access Publishing
Fund of the Technical University of Darmstadt."
article_number: '224'
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Fabian
full_name: Brauneis, Fabian
last_name: Brauneis
- first_name: Areg
full_name: Ghazaryan, Areg
id: 4AF46FD6-F248-11E8-B48F-1D18A9856A87
last_name: Ghazaryan
orcid: 0000-0001-9666-3543
- first_name: Hans-Werner
full_name: Hammer, Hans-Werner
last_name: Hammer
- first_name: Artem
full_name: Volosniev, Artem
id: 37D278BC-F248-11E8-B48F-1D18A9856A87
last_name: Volosniev
orcid: 0000-0003-0393-5525
citation:
ama: Brauneis F, Ghazaryan A, Hammer H-W, Volosniev A. Emergence of a Bose polaron
in a small ring threaded by the Aharonov-Bohm flux. Communications Physics.
2023;6. doi:10.1038/s42005-023-01281-2
apa: Brauneis, F., Ghazaryan, A., Hammer, H.-W., & Volosniev, A. (2023). Emergence
of a Bose polaron in a small ring threaded by the Aharonov-Bohm flux. Communications
Physics. Springer Nature. https://doi.org/10.1038/s42005-023-01281-2
chicago: Brauneis, Fabian, Areg Ghazaryan, Hans-Werner Hammer, and Artem Volosniev.
“Emergence of a Bose Polaron in a Small Ring Threaded by the Aharonov-Bohm Flux.”
Communications Physics. Springer Nature, 2023. https://doi.org/10.1038/s42005-023-01281-2.
ieee: F. Brauneis, A. Ghazaryan, H.-W. Hammer, and A. Volosniev, “Emergence of a
Bose polaron in a small ring threaded by the Aharonov-Bohm flux,” Communications
Physics, vol. 6. Springer Nature, 2023.
ista: Brauneis F, Ghazaryan A, Hammer H-W, Volosniev A. 2023. Emergence of a Bose
polaron in a small ring threaded by the Aharonov-Bohm flux. Communications Physics.
6, 224.
mla: Brauneis, Fabian, et al. “Emergence of a Bose Polaron in a Small Ring Threaded
by the Aharonov-Bohm Flux.” Communications Physics, vol. 6, 224, Springer
Nature, 2023, doi:10.1038/s42005-023-01281-2.
short: F. Brauneis, A. Ghazaryan, H.-W. Hammer, A. Volosniev, Communications Physics
6 (2023).
corr_author: '1'
date_created: 2023-08-28T12:36:49Z
date_published: 2023-08-22T00:00:00Z
date_updated: 2024-10-09T21:06:47Z
day: '22'
ddc:
- '530'
department:
- _id: MiLe
doi: 10.1038/s42005-023-01281-2
external_id:
arxiv:
- '2301.10488'
isi:
- '001052577500002'
file:
- access_level: open_access
checksum: 6edfc59b0ee7dc406d0968b05236e83d
content_type: application/pdf
creator: dernst
date_created: 2023-09-05T08:45:49Z
date_updated: 2023-09-05T08:45:49Z
file_id: '14268'
file_name: 2023_CommPhysics_Brauneis.pdf
file_size: 855960
relation: main_file
success: 1
file_date_updated: 2023-09-05T08:45:49Z
has_accepted_license: '1'
intvolume: ' 6'
isi: 1
keyword:
- General Physics and Astronomy
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
publication: Communications Physics
publication_identifier:
issn:
- 2399-3650
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Emergence of a Bose polaron in a small ring threaded by the Aharonov-Bohm flux
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2023'
...