The Annual Conference on Neural Information Processing Systems (NeurIPS) is one of the most prestigious and competitive international conferences in machine learning and computational neuroscience. A total of 15 papers by WASP researchers and PhD students have been accepted to NeurIPS 2023. Two of them have been selected as spotlight presentations.

This number is a true success for the program and a mark of excellence.

The list of accepted papers was updated November 2, 2023. The added paper is marked with an asterisk (*).

Accepted papers

Georg Bökman and Fredrik Kahl. “Investigating how ReLU-networks encode symmetries”*

Lennert De Smet, Emanuele Sansone, and Pedro dos Martires. “Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick”

Carl Hvarfner, Erik Hellsten, Frank Hutter, and Luigi Nardi. “Self-Correcting Bayesian Optimization through Bayesian Active Learning”.

Jacob Lindbäck, Zesen Wang and Mikael Johansson. “Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs”.

Neeratyoy Mallik, Carl Hvarfner, Edward Bergman, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, and Frank Hutter. “PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning”.

Hoomaan Maskan, Konstantinos C. Zygalakis, and Alp Yurtsever. ”A Variational Perspective on High-Resolution ODEs”.

Leonard Papenmeier, Luigi Nardi, and Matthias Poloczek. “Bounce: a Reliable Bayesian Optimization Algorithm for Combinatorial and Mixed Spaces”.

Antonio H. Ribeiro, Dave Zachariah, Francis Bach, and Thomas B. Schön. Regularization properties of adversarially-trained linear regression. (Spotlight presentation)

Alessio Russo and Alexandre Proutiere. “Model-free active exploration in Reinforcement Learning”.

Mathias Schreiner, Ole Winther, and Simon Olsson. “Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics”.

Stefan Stojanovic, Yassir Jedra, and Alexandre Proutiere. ”Spectral entry-wise matrix estimation for low-rank Reinforcement Learning”.

Ruo-Chun Tzeng, Po-An Wang, and Alexandre Proutiere, Chi-Jen Lu. “Closing the statistical-computational gap in best arm identification in combinatorial bandits”.

Filippo Vannella, Alexandre Proutiere, and Jaeseong Jeong. ”Statistical and computational trade-off in multi-agent multi-armed bandits”.

Po-An Wang, Ruo-Chun Tzeng, and Alexandre Proutiere. “Best arm identification with fixed budget: A large deviation perspective”. (Spotlight presentation)

Yushan Zhang, Johan Edstedt, Bastian Wandt, Per-Erik Forssén, Maria Magnusson, and Michael Felsberg. “GMSF: Global Match Scene Flow”.

Published: October 31st, 2023

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