Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels
S. Bombari, S. Kiyani, M. Mondelli, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 2738–2776.

Deep neural collapse is provably optimal for the deep unconstrained features model
P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, n.d.

Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence
D. Wu, V. Kungurtsev, M. Mondelli, in:, Transactions on Machine Learning Research, ML Research Press, 2023.

Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer
T. Shaw, P. Buri, M. McCarthy, E. Miles, F. Pellicciotti, (2023).

The metabolism of the developing brain : How large neutral amino acids modulate perinatal neuronal excitability and survival
L. Knaus, The Metabolism of the Developing Brainf: How Large Neutral Amino Acids Modulate Perinatal Neuronal Excitability and Survival, Institute of Science and Technology Austria, 2023.

Large neutral amino acid levels tune perinatal neuronal excitability and survival
L. Knaus, B. Basilico, D. Malzl, M. Gerykova Bujalkova, M. Smogavec, L.A. Schwarz, S. Gorkiewicz, N. Amberg, F. Pauler, C. Knittl-Frank, M. Tassinari, N. Maulide, T. Rülicke, J. Menche, S. Hippenmeyer, G. Novarino, Cell 186 (2023) 1950–1967.e25.

Radio frequency driven superconducting diode and parity conserving  Cooper pair transport in a two-dimensional germanium hole gas
M. Valentini, O. Sagi, L. Baghumyan, T. de Gijsel, J. Jung, S. Calcaterra, A. Ballabio, J.A. Servin, K. Aggarwal, M. Janik, T. Adletzberger, R.S. Souto, M. Leijnse, J. Danon, C. Schrade, E. Bakkers, D. Chrastina, G. Isella, G. Katsaros, ArXiv (n.d.).

Image retrieval outperforms diffusion models on data augmentation
M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, Journal of Machine Learning Research (2023).

Multi-view causal representation learning with partial observability
D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, ArXiv (n.d.).

Latent space translation via semantic alignment
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, E. Rodolà, ArXiv (n.d.).

Grounded object centric learning
A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).

Sample complexity bounds for score-matching: Causal discovery and generative modeling
Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).

Assumption violations in causal discovery and the robustness of score matching
F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello, ArXiv (n.d.).

Leveraging sparse and shared feature activations for disentangled representation learning
M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).

Rotating features for object discovery
S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).

Shortcuts for causal discovery of nonlinear models by score matching
F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).

A sparsity principle for partially observable causal representation learning
D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview, 2023.

Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, T. Hou, in:, Proceedings of 2023 IEEE International Symposium on Information Theory, IEEE, n.d.

Dynamics and proliferation of turbulent stripes in plane-Poiseuille and plane-Couette flows
E. Marensi, G. Yalniz, B. Hof, Journal of Fluid Mechanics 974 (2023).

To DISP or not? The farreaching reaction mechanisms underpinning Lithiumair batteries
R.B. Jethwa, S. Mondal, B. Pant, S.A. Freunberger, Angewandte Chemie International Edition (2023).