--- _id: '14171' abstract: - lang: eng text: "This paper demonstrates how to recover causal graphs from the score of the\r\ndata distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching algorithms as a building block, we show how to design a new generation\r\nof scalable causal discovery methods. To showcase our approach, we also propose\r\na new efficient method for approximating the score's Jacobian, enabling to\r\nrecover the causal graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive with state-of-the-art causal discovery methods while\r\nbeing significantly faster." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Paul full_name: Rolland, Paul last_name: Rolland - first_name: Volkan full_name: Cevher, Volkan last_name: Cevher - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Chris full_name: Russel, Chris last_name: Russel - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Dominik full_name: Janzing, Dominik last_name: Janzing - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise  models. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:18741-18753.' apa: 'Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., & Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise  models. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.' chicago: Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” In Proceedings of the 39th International Conference on Machine Learning, 162:18741–53. ML Research Press, 2022. ieee: P. Rolland et al., “Score matching enables causal discovery of nonlinear additive noise  models,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753. ista: Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello F. 2022. Score matching enables causal discovery of nonlinear additive noise  models. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 162, 18741–18753. mla: Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 18741–53. short: P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753. conference: end_date: 2022-07-23 location: Baltimore, MD, United States name: International Conference on Machine Learning start_date: 2022-07-17 date_created: 2023-08-22T14:00:18Z date_published: 2022-07-22T00:00:00Z date_updated: 2023-09-11T10:14:20Z day: '22' department: - _id: FrLo extern: '1' external_id: arxiv: - '2203.04413' intvolume: ' 162' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2203.04413 month: '07' oa: 1 oa_version: Preprint page: 18741-18753 publication: Proceedings of the 39th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: Score matching enables causal discovery of nonlinear additive noise models type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 162 year: '2022' ... --- _id: '14172' abstract: - lang: eng text: "An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models\r\nthat learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards\r\nmore realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factoris out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate\r\ngeneralization." article_processing_charge: No arxiv: 1 author: - first_name: Lukas full_name: Schott, Lukas last_name: Schott - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Chris full_name: Russell, Chris last_name: Russell - first_name: Matthias full_name: Bethge, Matthias last_name: Bethge - 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 - first_name: Wieland full_name: Brendel, Wieland last_name: Brendel citation: ama: 'Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning does not generalize strongly within the  same domain. In: 10th International Conference on Learning Representations. ; 2022.' apa: Schott, L., Kügelgen, J. von, Träuble, F., Gehler, P., Russell, C., Bethge, M., … Brendel, W. (2022). Visual representation learning does not generalize strongly within the  same domain. In 10th International Conference on Learning Representations. Virtual. chicago: Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” In 10th International Conference on Learning Representations, 2022. ieee: L. Schott et al., “Visual representation learning does not generalize strongly within the  same domain,” in 10th International Conference on Learning Representations, Virtual, 2022. ista: 'Schott L, Kügelgen J von, Träuble F, Gehler P, Russell C, Bethge M, Schölkopf B, Locatello F, Brendel W. 2022. Visual representation learning does not generalize strongly within the  same domain. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” 10th International Conference on Learning Representations, 2022. short: L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:00:50Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:40:52Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.08221' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2107.08221 month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: Visual representation learning does not generalize strongly within the same domain type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14173' abstract: - lang: eng text: "Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same\r\nexperimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and\r\nfew-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Florian full_name: Wenzel, Florian last_name: Wenzel - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Peter Vincent full_name: Gehler, Peter Vincent last_name: Gehler - first_name: Carl-Johann Simon-Gabriel full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel last_name: Carl-Johann Simon-Gabriel - first_name: Max full_name: Horn, Max last_name: Horn - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: David full_name: Kernert, David last_name: Kernert - first_name: Chris full_name: Russell, Chris last_name: Russell - first_name: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Bernt full_name: Schiele, Bernt last_name: Schiele - 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: 'Wenzel F, Dittadi A, Gehler PV, et al. Assaying out-of-distribution generalization in transfer learning. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:7181-7198.' apa: 'Wenzel, F., Dittadi, A., Gehler, P. V., Carl-Johann Simon-Gabriel, C.-J. S.-G., Horn, M., Zietlow, D., … Locatello, F. (2022). Assaying out-of-distribution generalization in transfer learning. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 7181–7198). New Orleans, LA, United States: Neural Information Processing Systems Foundation.' chicago: Wenzel, Florian, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” In 36th Conference on Neural Information Processing Systems, 35:7181–98. Neural Information Processing Systems Foundation, 2022. ieee: F. Wenzel et al., “Assaying out-of-distribution generalization in transfer learning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198. ista: 'Wenzel F, Dittadi A, Gehler PV, Carl-Johann Simon-Gabriel C-JS-G, Horn M, Zietlow D, Kernert D, Russell C, Brox T, Schiele B, Schölkopf B, Locatello F. 2022. Assaying out-of-distribution generalization in transfer learning. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 7181–7198.' mla: Wenzel, Florian, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 7181–98. short: F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel, M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf, F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 7181–7198. conference: end_date: 2022-12-09 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-22T14:01:13Z date_published: 2022-12-15T00:00:00Z date_updated: 2023-09-06T10:34:43Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '2207.09239' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2207.09239 month: '12' oa: 1 oa_version: Preprint page: 7181-7198 publication: 36th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713871088' publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Assaying out-of-distribution generalization in transfer learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14174' abstract: - lang: eng text: "Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitate efficient downstream learning and generalization. By training 240 representations and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of\r\npretrained VAE-based representations affect the OOD generalization of downstream agents. We observe that many agents are surprisingly robust to realistic distribution shifts, including the challenging sim-to-real case. In addition, we find that the generalization performance of a simple downstream proxy task reliably predicts the generalization performance of our RL agents\r\nunder a wide range of OOD settings. Such proxy tasks can thus be used to select pretrained representations that will lead to agents that generalize." article_processing_charge: No arxiv: 1 author: - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Manuel full_name: Wüthrich, Manuel last_name: Wüthrich - first_name: Felix full_name: Widmaier, Felix last_name: Widmaier - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Olivier full_name: Bachem, Olivier last_name: Bachem - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer citation: ama: 'Dittadi A, Träuble F, Wüthrich M, et al. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In: 10th International Conference on Learning Representations. ; 2022.' apa: Dittadi, A., Träuble, F., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., … Bauer, S. (2022). The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In 10th International Conference on Learning Representations. Virtual. chicago: Dittadi, Andrea, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” In 10th International Conference on Learning Representations, 2022. ieee: A. Dittadi et al., “The role of pretrained representations for the OOD generalization of  reinforcement learning agents,” in 10th International Conference on Learning Representations, Virtual, 2022. ista: 'Dittadi A, Träuble F, Wüthrich M, Widmaier F, Gehler P, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2022. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Dittadi, Andrea, et al. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” 10th International Conference on Learning Representations, 2022. short: A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:02:13Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:48:36Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.05686' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2107.05686' month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: The role of pretrained representations for the OOD generalization of reinforcement learning agents type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14175' abstract: - lang: eng text: "Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions\r\nof different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social\r\ninteraction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality." article_processing_charge: No arxiv: 1 author: - first_name: Osama full_name: Makansi, Osama last_name: Makansi - 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: Dominik full_name: Janzing, Dominik last_name: Janzing - first_name: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Makansi O, Kügelgen J von, Locatello F, et al. You mostly walk alone: Analyzing feature attribution in trajectory prediction. In: 10th International Conference on Learning Representations. ; 2022.' apa: 'Makansi, O., Kügelgen, J. von, Locatello, F., Gehler, P., Janzing, D., Brox, T., & Schölkopf, B. (2022). You mostly walk alone: Analyzing feature attribution in trajectory prediction. In 10th International Conference on Learning Representations. Virtual.' chicago: 'Makansi, Osama, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, and Bernhard Schölkopf. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” In 10th International Conference on Learning Representations, 2022.' ieee: 'O. Makansi et al., “You mostly walk alone: Analyzing feature attribution in trajectory prediction,” in 10th International Conference on Learning Representations, Virtual, 2022.' ista: 'Makansi O, Kügelgen J von, Locatello F, Gehler P, Janzing D, Brox T, Schölkopf B. 2022. You mostly walk alone: Analyzing feature attribution in trajectory prediction. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: 'Makansi, Osama, et al. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” 10th International Conference on Learning Representations, 2022.' short: O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:02:34Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:52:20Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2110.05304' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2110.05304 month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: 'You mostly walk alone: Analyzing feature attribution in trajectory prediction' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14215' abstract: - lang: eng text: Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals. article_processing_charge: No arxiv: 1 author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Martin full_name: Weiss, Martin last_name: Weiss - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Alexandre full_name: Lacoste, Alexandre last_name: Lacoste - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Chris full_name: Pal, Chris last_name: Pal - first_name: Li Erran full_name: Li, Li Erran last_name: Li - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture for geospatial systems. In: 36th Conference on Neural Information Processing Systems.' apa: Rahaman, N., Weiss, M., Träuble, F., Locatello, F., Lacoste, A., Bengio, Y., … Schölkopf, B. (n.d.). A general purpose neural architecture for geospatial systems. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States. chicago: Rahaman, Nasim, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. “A General Purpose Neural Architecture for Geospatial Systems.” In 36th Conference on Neural Information Processing Systems, n.d. ieee: N. Rahaman et al., “A general purpose neural architecture for geospatial systems,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States. ista: 'Rahaman N, Weiss M, Träuble F, Locatello F, Lacoste A, Bengio Y, Pal C, Li LE, Schölkopf B. A general purpose neural architecture for geospatial systems. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.' mla: Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial Systems.” 36th Conference on Neural Information Processing Systems. short: N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d. conference: end_date: 2022-12-09 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-22T14:21:47Z date_published: 2022-11-04T00:00:00Z date_updated: 2023-09-13T09:35:59Z day: '04' department: - _id: FrLo extern: '1' external_id: arxiv: - '2211.02348' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.02348 month: '11' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: submitted quality_controlled: '1' status: public title: A general purpose neural architecture for geospatial systems type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14220' abstract: - lang: eng text: Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as K2. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in K, allows agents to extrapolate and generalize zero-shot to any new object number. article_number: '2201.13388' article_processing_charge: No arxiv: 1 author: - first_name: Davide full_name: Mambelli, Davide last_name: Mambelli - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer - 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: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv. doi:10.48550/arXiv.2201.13388 apa: Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., & Locatello, F. (n.d.). Compositional multi-object reinforcement learning with linear relation networks. arXiv. https://doi.org/10.48550/arXiv.2201.13388 chicago: Mambelli, Davide, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, and Francesco Locatello. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2201.13388. ieee: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, and F. Locatello, “Compositional multi-object reinforcement learning with linear relation networks,” arXiv. . ista: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv, 2201.13388. mla: Mambelli, Davide, et al. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” ArXiv, 2201.13388, doi:10.48550/arXiv.2201.13388. short: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.). date_created: 2023-08-22T14:23:16Z date_published: 2022-01-31T00:00:00Z date_updated: 2024-10-14T12:27:39Z day: '31' department: - _id: FrLo doi: 10.48550/arXiv.2201.13388 extern: '1' external_id: arxiv: - '2201.13388' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2201.13388 month: '01' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Compositional multi-object reinforcement learning with linear relation networks type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14381' abstract: - lang: eng text: Expander graphs (sparse but highly connected graphs) have, since their inception, been the source of deep links between Mathematics and Computer Science as well as applications to other areas. In recent years, a fascinating theory of high-dimensional expanders has begun to emerge, which is still in a formative stage but has nonetheless already lead to a number of striking results. Unlike for graphs, in higher dimensions there is a rich array of non-equivalent notions of expansion (coboundary expansion, cosystolic expansion, topological expansion, spectral expansion, etc.), with differents strengths and applications. In this talk, we will survey this landscape of high-dimensional expansion, with a focus on two main results. First, we will present Gromov’s Topological Overlap Theorem, which asserts that coboundary expansion (a quantitative version of vanishing mod 2 cohomology) implies topological expansion (roughly, the property that for every map from a simplicial complex to a manifold of the same dimension, the images of a positive fraction of the simplices have a point in common). Second, we will outline a construction of bounded degree 2-dimensional topological expanders, due to Kaufman, Kazhdan, and Lubotzky. article_processing_charge: No article_type: original author: - first_name: Uli full_name: Wagner, Uli id: 36690CA2-F248-11E8-B48F-1D18A9856A87 last_name: Wagner orcid: 0000-0002-1494-0568 citation: ama: Wagner U. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de la Societe Mathematique de France. 2022;438:281-294. doi:10.24033/ast.1188 apa: Wagner, U. (2022). High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de La Societe Mathematique de France. Societe Mathematique de France. https://doi.org/10.24033/ast.1188 chicago: Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” Bulletin de La Societe Mathematique de France. Societe Mathematique de France, 2022. https://doi.org/10.24033/ast.1188. ieee: U. Wagner, “High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others),” Bulletin de la Societe Mathematique de France, vol. 438. Societe Mathematique de France, pp. 281–294, 2022. ista: Wagner U. 2022. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de la Societe Mathematique de France. 438, 281–294. mla: Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” Bulletin de La Societe Mathematique de France, vol. 438, Societe Mathematique de France, 2022, pp. 281–94, doi:10.24033/ast.1188. short: U. Wagner, Bulletin de La Societe Mathematique de France 438 (2022) 281–294. corr_author: '1' date_created: 2023-10-01T22:01:14Z date_published: 2022-01-01T00:00:00Z date_updated: 2025-09-10T09:55:10Z day: '01' department: - _id: UlWa doi: 10.24033/ast.1188 external_id: isi: - '000958364400007' intvolume: ' 438' isi: 1 language: - iso: eng month: '01' oa_version: None page: 281-294 publication: Bulletin de la Societe Mathematique de France publication_identifier: eissn: - 2102-622X issn: - 0037-9484 publication_status: published publisher: Societe Mathematique de France quality_controlled: '1' scopus_import: '1' status: public title: High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 438 year: '2022' ... --- _id: '14437' abstract: - lang: eng text: Future LEDs could be based on lead halide perovskites. A breakthrough in preparing device-compatible solids composed of nanoscale perovskite crystals overcomes a long-standing hurdle in making blue perovskite LEDs. article_processing_charge: No article_type: letter_note author: - first_name: Hendrik full_name: Utzat, Hendrik last_name: Utzat - first_name: Maria full_name: Ibáñez, Maria id: 43C61214-F248-11E8-B48F-1D18A9856A87 last_name: Ibáñez orcid: 0000-0001-5013-2843 citation: ama: Utzat H, Ibáñez M. Molecular engineering enables bright blue LEDs. Nature. 2022;612(7941):638-639. doi:10.1038/d41586-022-04447-0 apa: Utzat, H., & Ibáñez, M. (2022). Molecular engineering enables bright blue LEDs. Nature. Springer Nature. https://doi.org/10.1038/d41586-022-04447-0 chicago: Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” Nature. Springer Nature, 2022. https://doi.org/10.1038/d41586-022-04447-0. ieee: H. Utzat and M. Ibáñez, “Molecular engineering enables bright blue LEDs,” Nature, vol. 612, no. 7941. Springer Nature, pp. 638–639, 2022. ista: Utzat H, Ibáñez M. 2022. Molecular engineering enables bright blue LEDs. Nature. 612(7941), 638–639. mla: Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” Nature, vol. 612, no. 7941, Springer Nature, 2022, pp. 638–39, doi:10.1038/d41586-022-04447-0. short: H. Utzat, M. Ibáñez, Nature 612 (2022) 638–639. corr_author: '1' date_created: 2023-10-17T11:14:43Z date_published: 2022-12-21T00:00:00Z date_updated: 2025-09-10T09:55:51Z day: '21' department: - _id: MaIb doi: 10.1038/d41586-022-04447-0 external_id: isi: - '000934065100010' pmid: - '36543947' intvolume: ' 612' isi: 1 issue: '7941' keyword: - Multidisciplinary language: - iso: eng month: '12' oa_version: None page: 638-639 pmid: 1 publication: Nature publication_identifier: eissn: - 1476-4687 issn: - 0028-0836 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Molecular engineering enables bright blue LEDs type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 612 year: '2022' ... --- _id: '14520' abstract: - lang: eng text: 'This dataset comprises all data shown in the figures of the submitted article "Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses" at arxiv.org/abs/2206.14104. Additional raw data are available from the corresponding author on reasonable request.' article_processing_charge: No author: - first_name: Martin full_name: Zemlicka, Martin id: 2DCF8DE6-F248-11E8-B48F-1D18A9856A87 last_name: Zemlicka orcid: 0009-0005-0878-3032 - first_name: Elena full_name: Redchenko, Elena id: 2C21D6E8-F248-11E8-B48F-1D18A9856A87 last_name: Redchenko - first_name: Matilda full_name: Peruzzo, Matilda id: 3F920B30-F248-11E8-B48F-1D18A9856A87 last_name: Peruzzo orcid: 0000-0002-3415-4628 - first_name: Farid full_name: Hassani, Farid id: 2AED110C-F248-11E8-B48F-1D18A9856A87 last_name: Hassani orcid: 0000-0001-6937-5773 - first_name: Andrea full_name: Trioni, Andrea id: 42F71B44-F248-11E8-B48F-1D18A9856A87 last_name: Trioni - first_name: Shabir full_name: Barzanjeh, Shabir id: 2D25E1F6-F248-11E8-B48F-1D18A9856A87 last_name: Barzanjeh orcid: 0000-0003-0415-1423 - first_name: Johannes M full_name: Fink, Johannes M id: 4B591CBA-F248-11E8-B48F-1D18A9856A87 last_name: Fink orcid: 0000-0001-8112-028X citation: ama: 'Zemlicka M, Redchenko E, Peruzzo M, et al. Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. 2022. doi:10.5281/ZENODO.8408897' apa: 'Zemlicka, M., Redchenko, E., Peruzzo, M., Hassani, F., Trioni, A., Barzanjeh, S., & Fink, J. M. (2022). Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. Zenodo. https://doi.org/10.5281/ZENODO.8408897' chicago: 'Zemlicka, Martin, Elena Redchenko, Matilda Peruzzo, Farid Hassani, Andrea Trioni, Shabir Barzanjeh, and Johannes M Fink. “Compact Vacuum Gap Transmon Qubits: Selective and Sensitive Probes for Superconductor Surface Losses.” Zenodo, 2022. https://doi.org/10.5281/ZENODO.8408897.' ieee: 'M. Zemlicka et al., “Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses.” Zenodo, 2022.' ista: 'Zemlicka M, Redchenko E, Peruzzo M, Hassani F, Trioni A, Barzanjeh S, Fink JM. 2022. Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses, Zenodo, 10.5281/ZENODO.8408897.' mla: 'Zemlicka, Martin, et al. Compact Vacuum Gap Transmon Qubits: Selective and Sensitive Probes for Superconductor Surface Losses. Zenodo, 2022, doi:10.5281/ZENODO.8408897.' short: M. Zemlicka, E. Redchenko, M. Peruzzo, F. Hassani, A. Trioni, S. Barzanjeh, J.M. Fink, (2022). corr_author: '1' date_created: 2023-11-13T08:09:10Z date_published: 2022-06-28T00:00:00Z date_updated: 2026-06-03T07:16:02Z day: '28' ddc: - '530' department: - _id: JoFi doi: 10.5281/ZENODO.8408897 has_accepted_license: '1' license: https://creativecommons.org/publicdomain/zero/1.0/ main_file_link: - open_access: '1' url: https://doi.org/10.5281/ZENODO.8408897 month: '06' oa: 1 oa_version: Published Version publisher: Zenodo related_material: record: - id: '14517' relation: used_in_publication status: public status: public title: 'Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses' tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- OA_place: repository _id: '14600' abstract: - lang: eng text: We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold $p\in[0,1]$ over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on $3$ stochastic non-linear reinforcement learning tasks. article_number: '2210.05308' article_processing_charge: No arxiv: 1 author: - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - 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: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. doi:10.48550/ARXIV.2210.05308 apa: Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (n.d.). Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. https://doi.org/10.48550/ARXIV.2210.05308 chicago: Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2210.05308. ieee: D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” arXiv. . ista: Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv, 2210.05308. mla: Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” ArXiv, 2210.05308, doi:10.48550/ARXIV.2210.05308. short: D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.). corr_author: '1' date_created: 2023-11-24T13:10:09Z date_published: 2022-11-29T00:00:00Z date_updated: 2026-04-07T13:27:56Z day: '29' department: - _id: KrCh - _id: ToHe doi: 10.48550/ARXIV.2210.05308 ec_funded: 1 external_id: arxiv: - '2210.05308' language: - iso: eng license: https://creativecommons.org/licenses/by-sa/4.0/ main_file_link: - open_access: '1' url: https://arxiv.org/abs/2210.05308 month: '11' oa: 1 oa_version: Preprint project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: draft related_material: record: - id: '14830' relation: later_version status: public - id: '14539' relation: dissertation_contains status: public status: public title: Learning control policies for stochastic systems with reach-avoid guarantees tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- OA_place: repository _id: '14601' abstract: - lang: eng text: "In this work, we address the problem of learning provably stable neural\r\nnetwork policies for stochastic control systems. While recent work has\r\ndemonstrated the feasibility of certifying given policies using martingale\r\ntheory, the problem of how to learn such policies is little explored. Here, we\r\nstudy the effectiveness of jointly learning a policy together with a martingale\r\ncertificate that proves its stability using a single learning algorithm. We\r\nobserve that the joint optimization problem becomes easily stuck in local\r\nminima when starting from a randomly initialized policy. Our results suggest\r\nthat some form of pre-training of the policy is required for the joint\r\noptimization to repair and verify the policy successfully." article_number: '2205.11991' article_processing_charge: No arxiv: 1 author: - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - 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 citation: ama: Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv. doi:10.48550/arXiv.2205.11991 apa: Zikelic, D., Lechner, M., Chatterjee, K., & Henzinger, T. A. (n.d.). Learning stabilizing policies in stochastic control systems. arXiv. https://doi.org/10.48550/arXiv.2205.11991 chicago: Zikelic, Dorde, Mathias Lechner, Krishnendu Chatterjee, and Thomas A Henzinger. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2205.11991. ieee: D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” arXiv. . ista: Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv, 2205.11991. mla: Zikelic, Dorde, et al. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, 2205.11991, doi:10.48550/arXiv.2205.11991. short: D. Zikelic, M. Lechner, K. Chatterjee, T.A. Henzinger, ArXiv (n.d.). corr_author: '1' date_created: 2023-11-24T13:22:30Z date_published: 2022-05-24T00:00:00Z date_updated: 2026-04-07T13:27:56Z day: '24' department: - _id: KrCh - _id: ToHe doi: 10.48550/arXiv.2205.11991 ec_funded: 1 external_id: arxiv: - '2205.11991' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2205.11991 month: '05' oa: 1 oa_version: Preprint 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: arXiv publication_status: draft related_material: record: - id: '14539' relation: dissertation_contains status: public status: public title: Learning stabilizing policies in stochastic control systems type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- OA_place: repository _id: '15090' abstract: - lang: eng text: Given a locally finite set A⊆Rd and a coloring χ:A→{0,1,…,s}, we introduce the chromatic Delaunay mosaic of χ, which is a Delaunay mosaic in Rs+d that represents how points of different colors mingle. Our main results are bounds on the size of the chromatic Delaunay mosaic, in which we assume that d and s are constants. For example, if A is finite with n=#A, and the coloring is random, then the chromatic Delaunay mosaic has O(n⌈d/2⌉) cells in expectation. In contrast, for Delone sets and Poisson point processes in Rd, the expected number of cells within a closed ball is only a constant times the number of points in this ball. Furthermore, in R2 all colorings of a dense set of n points have chromatic Delaunay mosaics of size O(n). This encourages the use of chromatic Delaunay mosaics in applications. article_number: '2212.03121' article_processing_charge: No arxiv: 1 author: - first_name: Ranita full_name: Biswas, Ranita id: 3C2B033E-F248-11E8-B48F-1D18A9856A87 last_name: Biswas orcid: 0000-0002-5372-7890 - first_name: Sebastiano full_name: Cultrera di Montesano, Sebastiano id: 34D2A09C-F248-11E8-B48F-1D18A9856A87 last_name: Cultrera di Montesano orcid: 0000-0001-6249-0832 - first_name: Ondrej full_name: Draganov, Ondrej id: 2B23F01E-F248-11E8-B48F-1D18A9856A87 last_name: Draganov orcid: 0000-0003-0464-3823 - first_name: Herbert full_name: Edelsbrunner, Herbert id: 3FB178DA-F248-11E8-B48F-1D18A9856A87 last_name: Edelsbrunner orcid: 0000-0002-9823-6833 - first_name: Morteza full_name: Saghafian, Morteza id: f86f7148-b140-11ec-9577-95435b8df824 last_name: Saghafian citation: ama: Biswas R, Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. On the size of chromatic Delaunay mosaics. arXiv. apa: Biswas, R., Cultrera di Montesano, S., Draganov, O., Edelsbrunner, H., & Saghafian, M. (n.d.). On the size of chromatic Delaunay mosaics. arXiv. chicago: Biswas, Ranita, Sebastiano Cultrera di Montesano, Ondrej Draganov, Herbert Edelsbrunner, and Morteza Saghafian. “On the Size of Chromatic Delaunay Mosaics.” ArXiv, n.d. ieee: R. Biswas, S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, and M. Saghafian, “On the size of chromatic Delaunay mosaics,” arXiv. . ista: Biswas R, Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. On the size of chromatic Delaunay mosaics. arXiv, 2212.03121. mla: Biswas, Ranita, et al. “On the Size of Chromatic Delaunay Mosaics.” ArXiv, 2212.03121. short: R. Biswas, S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, M. Saghafian, ArXiv (n.d.). corr_author: '1' date_created: 2024-03-08T09:54:20Z date_published: 2022-12-06T00:00:00Z date_updated: 2026-04-07T12:58:47Z day: '06' department: - _id: HeEd ec_funded: 1 external_id: arxiv: - '2212.03121' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2212.03121 month: '12' oa: 1 oa_version: Preprint project: - _id: 266A2E9E-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '788183' name: Alpha Shape Theory Extended - _id: 0aa4bc98-070f-11eb-9043-e6fff9c6a316 grant_number: I4887 name: Persistent Homology, Algorithms and Stochastic Geometry - _id: 268116B8-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z00342 name: Mathematics, Computer Science publication: arXiv publication_status: draft related_material: record: - id: '20456' relation: later_version status: public - id: '15094' relation: dissertation_contains status: public status: public title: On the size of chromatic Delaunay mosaics 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: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '15268' abstract: - lang: eng text: Apolipoprotein A‐I (apoA‐I) has a key function in the reverse cholesterol transport. However, aggregation of apoA‐I single point mutants can lead to hereditary amyloid pathology. Although several studies have tackled the biophysical and structural consequences introduced by these mutations, there is little information addressing the relationship between the evolutionary and structural features that contribute to the amyloid behavior of apoA‐I. We combined evolutionary studies, in silico mutagenesis and molecular dynamics (MD) simulations to provide a comprehensive analysis of the conservation and pathogenic role of the aggregation‐prone regions (APRs) present in apoA‐I. Sequence analysis demonstrated that among the four amyloidogenic regions described for human apoA‐I, only two (APR1 and APR4) are evolutionary conserved across different species of Sarcopterygii. Moreover, stability analysis carried out with the FoldX engine showed that APR1 contributes to the marginal stability of apoA‐I. Structural properties of full‐length apoA‐I models suggest that aggregation is avoided by placing APRs into highly packed and rigid portions of its native fold. Compared to silent variants extracted from the gnomAD database, the thermodynamic and pathogenic impact of amyloid mutations showed evidence of a higher destabilizing effect. MD simulations of the amyloid variant G26R evidenced the partial unfolding of the alpha‐helix bundle with the concomitant exposure of APR1 to the solvent, suggesting an insight into the early steps involved in its aggregation. Our findings highlight APR1 as a relevant component for apoA‐I structural integrity and emphasize a destabilizing effect of amyloid variants that leads to the exposure of this region. article_processing_charge: No article_type: original author: - first_name: Romina A. full_name: Gisonno, Romina A. last_name: Gisonno - first_name: Tomas full_name: Masson, Tomas id: 93ac43e8-8599-11eb-9b86-f6efb0a4c207 last_name: Masson orcid: 0000-0002-2634-6283 - first_name: Nahuel A. full_name: Ramella, Nahuel A. last_name: Ramella - first_name: Exequiel E. full_name: Barrera, Exequiel E. last_name: Barrera - first_name: Víctor full_name: Romanowski, Víctor last_name: Romanowski - first_name: M. Alejandra full_name: Tricerri, M. Alejandra last_name: Tricerri citation: ama: 'Gisonno RA, Masson T, Ramella NA, Barrera EE, Romanowski V, Tricerri MA. Evolutionary and structural constraints influencing apolipoprotein A‐I amyloid behavior. Proteins: Structure, Function, and Bioinformatics. 2022;90(1):258-269. doi:10.1002/prot.26217' apa: 'Gisonno, R. A., Masson, T., Ramella, N. A., Barrera, E. E., Romanowski, V., & Tricerri, M. A. (2022). Evolutionary and structural constraints influencing apolipoprotein A‐I amyloid behavior. Proteins: Structure, Function, and Bioinformatics. Wiley. https://doi.org/10.1002/prot.26217' chicago: 'Gisonno, Romina A., Tomas Masson, Nahuel A. Ramella, Exequiel E. Barrera, Víctor Romanowski, and M. Alejandra Tricerri. “Evolutionary and Structural Constraints Influencing Apolipoprotein A‐I Amyloid Behavior.” Proteins: Structure, Function, and Bioinformatics. Wiley, 2022. https://doi.org/10.1002/prot.26217.' ieee: 'R. A. Gisonno, T. Masson, N. A. Ramella, E. E. Barrera, V. Romanowski, and M. A. Tricerri, “Evolutionary and structural constraints influencing apolipoprotein A‐I amyloid behavior,” Proteins: Structure, Function, and Bioinformatics, vol. 90, no. 1. Wiley, pp. 258–269, 2022.' ista: 'Gisonno RA, Masson T, Ramella NA, Barrera EE, Romanowski V, Tricerri MA. 2022. Evolutionary and structural constraints influencing apolipoprotein A‐I amyloid behavior. Proteins: Structure, Function, and Bioinformatics. 90(1), 258–269.' mla: 'Gisonno, Romina A., et al. “Evolutionary and Structural Constraints Influencing Apolipoprotein A‐I Amyloid Behavior.” Proteins: Structure, Function, and Bioinformatics, vol. 90, no. 1, Wiley, 2022, pp. 258–69, doi:10.1002/prot.26217.' short: 'R.A. Gisonno, T. Masson, N.A. Ramella, E.E. Barrera, V. Romanowski, M.A. Tricerri, Proteins: Structure, Function, and Bioinformatics 90 (2022) 258–269.' corr_author: '1' date_created: 2024-04-03T07:49:53Z date_published: 2022-01-01T00:00:00Z date_updated: 2024-10-09T21:08:44Z day: '01' department: - _id: MaJö doi: 10.1002/prot.26217 external_id: pmid: - '34414600' intvolume: ' 90' issue: '1' keyword: - Molecular Biology - Biochemistry - Structural Biology language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1101/2020.09.18.304337 month: '01' oa: 1 oa_version: Preprint page: 258-269 pmid: 1 publication: 'Proteins: Structure, Function, and Bioinformatics' publication_identifier: eissn: - 1097-0134 issn: - 0887-3585 publication_status: published publisher: Wiley quality_controlled: '1' status: public title: Evolutionary and structural constraints influencing apolipoprotein A‐I amyloid behavior type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 90 year: '2022' ... --- _id: '17057' abstract: - lang: eng text: Martin Loose studied chemistry at the University of Heidelberg, Germany. He then joined Petra Schwille's group at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, where he obtained his PhD degree in 2010 for work on self-organization and pattern formation in the bacterial Min protein system. He then moved to Tim Mitchison's lab at Harvard Medical School, Boston, USA for his postdoc, funded by Human Frontier Science Program (HSFP) and European Molecular Biology Organization (EMBO) long-term fellowships; there, he discovered that the bacterial cell division proteins FtsA and FtsZ self-organize into dynamic cytoskeletal patterns. Martin established his independent research group at the Institute of Science and Technology (IST) Austria in 2015, supported by an European Research Council (ERC) starting grant and HFSP Young Investigator Grant. His lab studies the self-organization of bacterial cell division and small GTPase networks. article_number: jcs259715 article_processing_charge: No author: - first_name: Martin full_name: Loose, Martin id: 462D4284-F248-11E8-B48F-1D18A9856A87 last_name: Loose orcid: 0000-0001-7309-9724 citation: ama: Loose M. Cell Scientist to Watch – Martin Loose. Vol 135. The Company of Biologists; 2022. doi:10.1242/jcs.259715 apa: Loose, M. (2022). Cell scientist to watch – Martin Loose. Journal of Cell Science (Vol. 135). The Company of Biologists. https://doi.org/10.1242/jcs.259715 chicago: Loose, Martin. Cell Scientist to Watch – Martin Loose. Journal of Cell Science. Vol. 135. The Company of Biologists, 2022. https://doi.org/10.1242/jcs.259715. ieee: M. Loose, Cell scientist to watch – Martin Loose, vol. 135, no. 2. The Company of Biologists, 2022. ista: Loose M. 2022. Cell scientist to watch – Martin Loose, The Company of Biologists,p. mla: Loose, Martin. “Cell Scientist to Watch – Martin Loose.” Journal of Cell Science, vol. 135, no. 2, jcs259715, The Company of Biologists, 2022, doi:10.1242/jcs.259715. short: M. Loose, Cell Scientist to Watch – Martin Loose, The Company of Biologists, 2022. date_created: 2024-05-28T13:28:30Z date_published: 2022-01-19T00:00:00Z date_updated: 2026-06-18T17:51:26Z day: '19' ddc: - '570' department: - _id: MaLo doi: 10.1242/jcs.259715 external_id: isi: - '000762665200015' intvolume: ' 135' isi: 1 issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1242/jcs.259715 month: '01' oa: 1 oa_version: Published Version publication: Journal of Cell Science publication_identifier: eissn: - 1477-9137 issn: - 0021-9533 publication_status: published publisher: The Company of Biologists quality_controlled: '1' status: public title: Cell scientist to watch – Martin Loose type: other_academic_publication user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 135 year: '2022' ... --- _id: '17059' abstract: - lang: eng text: The recent focus on the efficiency of deep neural networks (DNNs) has led to significant work on model compression approaches, of which weight pruning is one of the most popular. At the same time, there is rapidly-growing computational support for efficiently executing the unstructured-sparse models obtained via pruning. Yet, most existing pruning methods minimize just the number of remaining weights, i.e. the size of the model, rather than optimizing for inference time. We address this gap by introducing SPDY, a new compression method which automatically determines layer-wise sparsity targets achieving a desired inference speedup on a given system, while minimizing accuracy loss. SPDY is the composition of two new techniques. The first is an efficient and general dynamic programming algorithm for solving constrained layer-wise compression problems, given a set of layer-wise error scores. The second technique is a local search procedure for automatically determining such scores in an accurate and robust manner. Experiments across popular vision and language models show that SPDY guarantees speedups while recovering higher accuracy relative to existing strategies, both for one-shot and gradual pruning scenarios, and is compatible with most existing pruning approaches. We also extend our approach to the recently-proposed task of pruning with very little data, where we achieve the best known accuracy recovery when pruning to the GPU-supported 2:4 sparsity pattern. acknowledgement: "We gratefully acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 programme (grant agreement No 805223 ScaleML),\r\nas well as computational support from AWS EC2. We thank Eldar Kurtic for code and hyper-parameters for BERT pruning, and the Neural Magic Team, notably Michael Goin and\r\nMark Kurtz, for support with their software." alternative_title: - PMLR article_processing_charge: Yes author: - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Frantar E, Alistarh D-A. SPDY: Accurate pruning with speedup guarantees. In: 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:6726-6743.' apa: 'Frantar, E., & Alistarh, D.-A. (2022). SPDY: Accurate pruning with speedup guarantees. In 39th International Conference on Machine Learning (Vol. 162, pp. 6726–6743). Baltimore, MD, United States: ML Research Press.' chicago: 'Frantar, Elias, and Dan-Adrian Alistarh. “SPDY: Accurate Pruning with Speedup Guarantees.” In 39th International Conference on Machine Learning, 162:6726–43. ML Research Press, 2022.' ieee: 'E. Frantar and D.-A. Alistarh, “SPDY: Accurate pruning with speedup guarantees,” in 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 6726–6743.' ista: 'Frantar E, Alistarh D-A. 2022. SPDY: Accurate pruning with speedup guarantees. 39th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 162, 6726–6743.' mla: 'Frantar, Elias, and Dan-Adrian Alistarh. “SPDY: Accurate Pruning with Speedup Guarantees.” 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 6726–43.' short: E. Frantar, D.-A. Alistarh, in:, 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 6726–6743. conference: end_date: 2022-07-23 location: Baltimore, MD, United States name: 'ICML: International Conference on Machine Learning' start_date: 2022-07-17 corr_author: '1' date_created: 2024-05-28T13:45:20Z date_published: 2022-07-20T00:00:00Z date_updated: 2025-04-14T07:49:14Z day: '20' ddc: - '000' department: - _id: DaAl ec_funded: 1 external_id: isi: - '000922378801029' file: - access_level: open_access checksum: 5179a1e4dfc0fbfab6674907299e414a content_type: application/pdf creator: dernst date_created: 2024-08-19T06:54:41Z date_updated: 2024-08-19T06:54:41Z file_id: '17440' file_name: 2022_PMLR_Frantar.pdf file_size: 615916 relation: main_file success: 1 file_date_updated: 2024-08-19T06:54:41Z has_accepted_license: '1' intvolume: ' 162' isi: 1 language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 6726-6743 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: 39th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'SPDY: Accurate pruning with speedup guarantees' 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: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 162 year: '2022' ... --- _id: '17060' abstract: - lang: eng text: "Payment channel networks (PCNs) are one of the most prominent solutions to the limited transaction throughput of blockchains. Nevertheless, PCNs suffer themselves from a throughput limitation due to the capital constraints of their channels. A similar dependence on high capital is also found in inter-bank payment settlements, where the so-called netting technique is used to mitigate liquidity demands.\r\nIn this work, we alleviate this limitation by introducing the notion of transaction aggregation: instead of executing transactions sequentially through a PCN, we enable senders to aggregate multiple transactions and execute them simultaneously to benefit from several amounts that may \"cancel out\". Two direct advantages of our proposal is the decrease in intermediary fees paid by senders as well as the obfuscation of the transaction data from the intermediaries.\r\nWe formulate the transaction aggregation as a computational problem, a generalization of the Bank Clearing Problem. We present a generic framework for the transaction aggregation execution, and thereafter we propose Wiser as an implementation of this framework in a specific hub-based setting. To overcome the NP-hardness of the transaction aggregation problem, in Wiser we propose a fixed-parameter linear algorithm for a special case of transaction aggregation as well as the Bank Clearing Problem. Wiser can also be seen as a modern variant of the Hawala money transfer system, as well as a decentralized implementation of the overseas remittance service of Wise." acknowledgement: "This work was supported partially by ERC Starting Grant QIP–805241, by the Vienna business agency (Wirtschaftsagentur) through the Vienna Cybersecurity and Privacy Research Center\r\n(ViSP) and by the Austrian Science Fund (FWF) project I 4800-N (ADVISE).\r\nThe first author would like to thank Daniel Dadush for suggesting the use of discrepancy techniques to solve the transaction aggregation problem." article_processing_charge: Yes (in subscription journal) arxiv: 1 author: - first_name: Samarth full_name: Tiwari, Samarth last_name: Tiwari - first_name: Michelle X full_name: Yeo, Michelle X id: 2D82B818-F248-11E8-B48F-1D18A9856A87 last_name: Yeo orcid: 0009-0001-3676-4809 - first_name: Zeta full_name: Avarikioti, Zeta last_name: Avarikioti - first_name: Iosif full_name: Salem, Iosif last_name: Salem - first_name: Krzysztof Z full_name: Pietrzak, Krzysztof Z id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87 last_name: Pietrzak orcid: 0000-0002-9139-1654 - first_name: Stefan full_name: Schmid, Stefan last_name: Schmid citation: ama: 'Tiwari S, Yeo MX, Avarikioti Z, Salem I, Pietrzak KZ, Schmid S. Wiser: Increasing throughput in payment channel networks with transaction aggregation. In: Proceedings of the 4th ACM Conference on Advances in Financial Technologies. Association for Computing Machinery; 2022:217-231. doi:10.1145/3558535.3559775' apa: 'Tiwari, S., Yeo, M. X., Avarikioti, Z., Salem, I., Pietrzak, K. Z., & Schmid, S. (2022). Wiser: Increasing throughput in payment channel networks with transaction aggregation. In Proceedings of the 4th ACM Conference on Advances in Financial Technologies (pp. 217–231). Cambridge, MA, United States: Association for Computing Machinery. https://doi.org/10.1145/3558535.3559775' chicago: 'Tiwari, Samarth, Michelle X Yeo, Zeta Avarikioti, Iosif Salem, Krzysztof Z Pietrzak, and Stefan Schmid. “Wiser: Increasing Throughput in Payment Channel Networks with Transaction Aggregation.” In Proceedings of the 4th ACM Conference on Advances in Financial Technologies, 217–31. Association for Computing Machinery, 2022. https://doi.org/10.1145/3558535.3559775.' ieee: 'S. Tiwari, M. X. Yeo, Z. Avarikioti, I. Salem, K. Z. Pietrzak, and S. Schmid, “Wiser: Increasing throughput in payment channel networks with transaction aggregation,” in Proceedings of the 4th ACM Conference on Advances in Financial Technologies, Cambridge, MA, United States, 2022, pp. 217–231.' ista: 'Tiwari S, Yeo MX, Avarikioti Z, Salem I, Pietrzak KZ, Schmid S. 2022. Wiser: Increasing throughput in payment channel networks with transaction aggregation. Proceedings of the 4th ACM Conference on Advances in Financial Technologies. AFT: Conference on Advances in Financial Technologies, 217–231.' mla: 'Tiwari, Samarth, et al. “Wiser: Increasing Throughput in Payment Channel Networks with Transaction Aggregation.” Proceedings of the 4th ACM Conference on Advances in Financial Technologies, Association for Computing Machinery, 2022, pp. 217–31, doi:10.1145/3558535.3559775.' short: S. Tiwari, M.X. Yeo, Z. Avarikioti, I. Salem, K.Z. Pietrzak, S. Schmid, in:, Proceedings of the 4th ACM Conference on Advances in Financial Technologies, Association for Computing Machinery, 2022, pp. 217–231. conference: end_date: 2022-09-21 location: Cambridge, MA, United States name: 'AFT: Conference on Advances in Financial Technologies' start_date: 2022-09-19 date_created: 2024-05-28T13:58:35Z date_published: 2022-09-19T00:00:00Z date_updated: 2025-09-10T09:57:48Z day: '19' ddc: - '000' department: - _id: KrPi doi: 10.1145/3558535.3559775 external_id: arxiv: - '2205.11597' isi: - '001041852800015' file: - access_level: open_access checksum: 54a7d405f8e57dba24728599ca63818c content_type: application/pdf creator: dernst date_created: 2024-08-19T06:45:21Z date_updated: 2024-08-19T06:45:21Z file_id: '17439' file_name: 2022_AFT_Tiwari.pdf file_size: 574728 relation: main_file success: 1 file_date_updated: 2024-08-19T06:45:21Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: 217-231 publication: Proceedings of the 4th ACM Conference on Advances in Financial Technologies publication_identifier: isbn: - '9781450398619' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' scopus_import: '1' status: public title: 'Wiser: Increasing throughput in payment channel networks with transaction aggregation' 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: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2022' ... --- _id: '17061' abstract: - lang: eng text: Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one’s own success. However, when two such “selfish” learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner’s dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness. acknowledgement: The authors are grateful to Jörg Oechssler for many helpful comments. A.M. was supported by a Simons Postdoctoral Fellowship (Math+X) at the University of Pennsylvania; K.C. was supported by the European Research Council Consolidator Grant 863818 (ForM-SMArt); and C.H. was supported by the European Research Council Starting Grant 850529 (E-DIRECT). article_number: pgac141 article_processing_charge: Yes article_type: original arxiv: 1 author: - first_name: Alex full_name: McAvoy, Alex last_name: McAvoy - first_name: Julian full_name: Kates-Harbeck, Julian last_name: Kates-Harbeck - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Christian full_name: Hilbe, Christian id: 2FDF8F3C-F248-11E8-B48F-1D18A9856A87 last_name: Hilbe orcid: 0000-0001-5116-955X citation: ama: McAvoy A, Kates-Harbeck J, Chatterjee K, Hilbe C. Evolutionary instability of selfish learning in repeated games. PNAS Nexus. 2022;1(4). doi:10.1093/pnasnexus/pgac141 apa: McAvoy, A., Kates-Harbeck, J., Chatterjee, K., & Hilbe, C. (2022). Evolutionary instability of selfish learning in repeated games. PNAS Nexus. Oxford University Press. https://doi.org/10.1093/pnasnexus/pgac141 chicago: McAvoy, Alex, Julian Kates-Harbeck, Krishnendu Chatterjee, and Christian Hilbe. “Evolutionary Instability of Selfish Learning in Repeated Games.” PNAS Nexus. Oxford University Press, 2022. https://doi.org/10.1093/pnasnexus/pgac141. ieee: A. McAvoy, J. Kates-Harbeck, K. Chatterjee, and C. Hilbe, “Evolutionary instability of selfish learning in repeated games,” PNAS Nexus, vol. 1, no. 4. Oxford University Press, 2022. ista: McAvoy A, Kates-Harbeck J, Chatterjee K, Hilbe C. 2022. Evolutionary instability of selfish learning in repeated games. PNAS Nexus. 1(4), pgac141. mla: McAvoy, Alex, et al. “Evolutionary Instability of Selfish Learning in Repeated Games.” PNAS Nexus, vol. 1, no. 4, pgac141, Oxford University Press, 2022, doi:10.1093/pnasnexus/pgac141. short: A. McAvoy, J. Kates-Harbeck, K. Chatterjee, C. Hilbe, PNAS Nexus 1 (2022). date_created: 2024-05-28T14:23:12Z date_published: 2022-09-01T00:00:00Z date_updated: 2025-06-11T13:54:20Z day: '01' ddc: - '000' department: - _id: KrCh doi: 10.1093/pnasnexus/pgac141 ec_funded: 1 external_id: arxiv: - '2105.06199' pmid: - '36714856' file: - access_level: open_access checksum: 79a8e3e4be7e8a2b407b4efddd65f3f3 content_type: application/pdf creator: dernst date_created: 2024-08-06T07:33:30Z date_updated: 2024-08-06T07:33:30Z file_id: '17400' file_name: 2022_PNASNexus_McAvoy.pdf file_size: 2410962 relation: main_file success: 1 file_date_updated: 2024-08-06T07:33:30Z has_accepted_license: '1' intvolume: ' 1' issue: '4' language: - iso: eng month: '09' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: PNAS Nexus publication_identifier: issn: - 2752-6542 publication_status: published publisher: Oxford University Press quality_controlled: '1' related_material: link: - relation: software url: https://github.com/alexmcavoy/fmtl/ scopus_import: '1' status: public title: Evolutionary instability of selfish learning in repeated games 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: 1 year: '2022' ... --- _id: '17065' abstract: - lang: eng text: Past work on optimizing fabrication plans given a carpentry design can provide Pareto-optimal plans trading off between material waste, fabrication time, precision, and other considerations. However, when developing fabrication plans, experts rarely restrict to a single design, instead considering families of design variations, sometimes adjusting designs to simplify fabrication. Jointly exploring the design and fabrication plan spaces for each design is intractable using current techniques. We present a new approach to jointly optimize design and fabrication plans for carpentered objects. To make this bi-level optimization tractable, we adapt recent work from program synthesis based on equality graphs (e-graphs), which encode sets of equivalent programs. Our insight is that subproblems within our bi-level problem share significant substructures. By representing both designs and fabrication plans in a new bag of parts (BOP) e-graph, we amortize the cost of optimizing design components shared among multiple candidates. Even using BOP e-graphs, the optimization space grows quickly in practice. Hence, we also show how a feedback-guided search strategy dubbed Iterative Contraction and Expansion on E-graphs (ICEE) can keep the size of the e-graph manageable and direct the search towards promising candidates. We illustrate the advantages of our pipeline through examples from the carpentry domain. acknowledgement: The authors would like to thank anonymous reviewers for their helpful feedback; Haomiao Wu for her contribution to the algorithm development in the early stage of the project; Elias Baldwin, David Tsay, Alexander Lefort, and Qiyang Tan for helping the experiments. article_number: '32' article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Haisen full_name: Zhao, Haisen id: fb7f793a-80d1-11eb-8869-d56e5b2a8ff4 last_name: Zhao orcid: 0000-0002-6389-1045 - first_name: Max full_name: Willsey, Max last_name: Willsey - first_name: Amy full_name: Zhu, Amy last_name: Zhu - first_name: Chandrakana full_name: Nandi, Chandrakana last_name: Nandi - first_name: Zachary full_name: Tatlock, Zachary last_name: Tatlock - first_name: Justin full_name: Solomon, Justin last_name: Solomon - first_name: Adriana full_name: Schulz, Adriana last_name: Schulz citation: ama: Zhao H, Willsey M, Zhu A, et al. Co-optimization of design and fabrication plans for carpentry. ACM Transactions on Graphics. 2022;41(3). doi:10.1145/3508499 apa: Zhao, H., Willsey, M., Zhu, A., Nandi, C., Tatlock, Z., Solomon, J., & Schulz, A. (2022). Co-optimization of design and fabrication plans for carpentry. ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3508499 chicago: Zhao, Haisen, Max Willsey, Amy Zhu, Chandrakana Nandi, Zachary Tatlock, Justin Solomon, and Adriana Schulz. “Co-Optimization of Design and Fabrication Plans for Carpentry.” ACM Transactions on Graphics. Association for Computing Machinery, 2022. https://doi.org/10.1145/3508499. ieee: H. Zhao et al., “Co-optimization of design and fabrication plans for carpentry,” ACM Transactions on Graphics, vol. 41, no. 3. Association for Computing Machinery, 2022. ista: Zhao H, Willsey M, Zhu A, Nandi C, Tatlock Z, Solomon J, Schulz A. 2022. Co-optimization of design and fabrication plans for carpentry. ACM Transactions on Graphics. 41(3), 32. mla: Zhao, Haisen, et al. “Co-Optimization of Design and Fabrication Plans for Carpentry.” ACM Transactions on Graphics, vol. 41, no. 3, 32, Association for Computing Machinery, 2022, doi:10.1145/3508499. short: H. Zhao, M. Willsey, A. Zhu, C. Nandi, Z. Tatlock, J. Solomon, A. Schulz, ACM Transactions on Graphics 41 (2022). date_created: 2024-05-29T06:09:23Z date_published: 2022-03-09T00:00:00Z date_updated: 2024-08-06T07:03:14Z day: '09' department: - _id: BeBi doi: 10.1145/3508499 external_id: arxiv: - '2107.12265' intvolume: ' 41' issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2107.12265 month: '03' oa: 1 oa_version: Preprint 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: Co-optimization of design and fabrication plans for carpentry type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 41 year: '2022' ... --- _id: '17066' abstract: - lang: eng text: A cell’s size affects the likelihood that it will die. But how is cell size controlled in this context and how does cell size impact commitment to the cell death fate? We present evidence that the caspase CED-3 interacts with the RhoGEF ECT-2 in Caenorhabditis elegans neuroblasts that generate “unwanted” cells. We propose that this interaction promotes polar actomyosin contractility, which leads to unequal neuroblast division and the generation of a daughter cell that is below the critical “lethal” size threshold. Furthermore, we find that hyperactivation of ECT-2 RhoGEF reduces the sizes of unwanted cells. Importantly, this suppresses the “cell death abnormal” phenotype caused by the partial loss of ced-3 caspase and therefore increases the likelihood that unwanted cells die. A putative null mutation of ced-3 caspase, however, is not suppressed, which indicates that cell size affects CED-3 caspase activation and/or activity. Therefore, we have uncovered novel sequential and reciprocal interactions between the apoptosis pathway and cell size that impact a cell’s commitment to the cell death fate. acknowledgement: "We thank members of the Conradt, Lambie, and Hajnal labs for discussions and comments on the manuscript. We thank M. Bauer, L. Jocham, N. Lebedeva, and L. McGuinness for excellent technical support; A. Hajnal and T. Kohlbrenner (University of Zurich, Switzerland) for allele zh135; and H.R. Horvitz (Massachusetts of Technology, USA) for plasmid pET-CED-3.\r\nSome strains were provided by the Caenorhabditis Genetics Center (CGC), which is funded by NIH Office of Research Infrastructure Programs (https://orip.nih.gov/) (P40 OD010440). This work was supported by UCL (Capital Equipment Fund, CEF2), a predoctoral fellowship from the China Scholarship Council (https://www.csc.edu.cn/) to HW, a predoctoral fellowship from the Studienstiftung des Deutschen Volkes (https://www.studienstiftung.de/) to NM, a Wolfson Fellowship from the Royal Society (https://royalsociety.org/) to BC (RSWF\\R1\\180008), the Deutsche Forschungsgemeinschaft (https://www.dfg.de/en/index.jsp) (ZA619/3-1 and ZA619/3-2 to EZ; C0204/10-1 and EXC114 to BC), and the Biotechnology and Biological Sciences Research Council (https://bbsrc.ukri.org/) (BB/V007572/1 to BC). " article_number: e3001786 article_processing_charge: Yes article_type: original author: - first_name: Aditya full_name: Sethi, Aditya last_name: Sethi - first_name: Hai full_name: Wei, Hai last_name: Wei - first_name: Nikhil full_name: Mishra, Nikhil id: C4D70E82-1081-11EA-B3ED-9A4C3DDC885E last_name: Mishra orcid: 0000-0002-6425-5788 - first_name: Ioannis full_name: Segos, Ioannis last_name: Segos - first_name: Eric J. full_name: Lambie, Eric J. last_name: Lambie - first_name: Esther full_name: Zanin, Esther last_name: Zanin - first_name: Barbara full_name: Conradt, Barbara last_name: Conradt citation: ama: Sethi A, Wei H, Mishra N, et al. A caspase–RhoGEF axis contributes to the cell size threshold for apoptotic death in developing Caenorhabditis elegans. PLOS Biology. 2022;20(10). doi:10.1371/journal.pbio.3001786 apa: Sethi, A., Wei, H., Mishra, N., Segos, I., Lambie, E. J., Zanin, E., & Conradt, B. (2022). A caspase–RhoGEF axis contributes to the cell size threshold for apoptotic death in developing Caenorhabditis elegans. PLOS Biology. Public Library of Science. https://doi.org/10.1371/journal.pbio.3001786 chicago: Sethi, Aditya, Hai Wei, Nikhil Mishra, Ioannis Segos, Eric J. Lambie, Esther Zanin, and Barbara Conradt. “A Caspase–RhoGEF Axis Contributes to the Cell Size Threshold for Apoptotic Death in Developing Caenorhabditis Elegans.” PLOS Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pbio.3001786. ieee: A. Sethi et al., “A caspase–RhoGEF axis contributes to the cell size threshold for apoptotic death in developing Caenorhabditis elegans,” PLOS Biology, vol. 20, no. 10. Public Library of Science, 2022. ista: Sethi A, Wei H, Mishra N, Segos I, Lambie EJ, Zanin E, Conradt B. 2022. A caspase–RhoGEF axis contributes to the cell size threshold for apoptotic death in developing Caenorhabditis elegans. PLOS Biology. 20(10), e3001786. mla: Sethi, Aditya, et al. “A Caspase–RhoGEF Axis Contributes to the Cell Size Threshold for Apoptotic Death in Developing Caenorhabditis Elegans.” PLOS Biology, vol. 20, no. 10, e3001786, Public Library of Science, 2022, doi:10.1371/journal.pbio.3001786. short: A. Sethi, H. Wei, N. Mishra, I. Segos, E.J. Lambie, E. Zanin, B. Conradt, PLOS Biology 20 (2022). date_created: 2024-05-29T06:09:34Z date_published: 2022-10-06T00:00:00Z date_updated: 2024-08-06T07:08:54Z day: '06' ddc: - '570' department: - _id: CaHe doi: 10.1371/journal.pbio.3001786 external_id: pmid: - '36201522' file: - access_level: open_access checksum: a7b46460b7819c196028481cc18a7c85 content_type: application/pdf creator: dernst date_created: 2024-08-06T07:07:52Z date_updated: 2024-08-06T07:07:52Z file_id: '17399' file_name: 2022_PlosBio_Sethi.pdf file_size: 2515388 relation: main_file success: 1 file_date_updated: 2024-08-06T07:07:52Z has_accepted_license: '1' intvolume: ' 20' issue: '10' language: - iso: eng month: '10' oa: 1 oa_version: Published Version pmid: 1 publication: PLOS Biology publication_identifier: issn: - 1545-7885 publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: A caspase–RhoGEF axis contributes to the cell size threshold for apoptotic death in developing Caenorhabditis elegans 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: 20 year: '2022' ...