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. At NeurIPS 2024, at least three of the workshops and 19 of the accepted papers involves WASP PhD students, WASP alumni and WASP researchers as authors or co-authors. Three of the papers have been selected as spotlight posters. This number is a true success for WASP and a mark of excellence.
The acceptance rate for NeurIPS Main Track was 25.8 % this year.
The list of accepted papers was updated October 21, 2024.
Accepted papers with contribution from WASP PhD students, WASP alumni and WASP researchers:
Active preference learning for ordering items in- and out-of-sample
Herman Bergström, Emil Carlsson, Devdatt Dubhashi, Fredrik Johansson
Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data
Sofia Ek and Dave Zachariah
If You Want to Be Robust, Be Wary of Initialization
Sofiane Ennadir, Johannes Lutzeyer, Michalis Vazirgiannis, El Houcine Bergou
Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors
Yazid Janati Badr Moufad, Alain Durmus, Eric Moulines, Jimmy Olsson
Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation
Yassir Jedra, Stefan Stojanovic, Alexandre Proutiere
Income SCM: From tabular data set to time-series simulator and causal estimation benchmark
Fredrik Johansson
Learning from Offline Foundation Features with Tensor Augmentations
Emir Konuk, Christos Matsoukas, Moein Sorkhei, Phitchapha Lertsiravarameth, Kevin Smith
Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos
Cuong Le, Manon Kok, Viktor Johansson, Bastian Wandt
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson, Tomas Landelius, Marc Deisenroth, Fredrik Lindsten (spotlight poster)
Learning Formal Mathematics From Intrinsic Motivation
Gabriel Poesia, David Broman, Nick Haber, Noah Goodman
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity
Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund
Multi-Reward Best Policy Identification
Alessio Russo, Filippo Vannella
Can Transformers Smell Like Humans?
Farzaneh Taleb, Miguel Vasco, Antonio Ribeiro, Mårten Björkman, Danica Kragic (spotlight poster)
SE(3)-bi-equivariant Transformers for Point Cloud Assembly
Ziming Wang, Rebecka Jörnsten
Outlier-Robust Distributionally Robust Optimization via Unbalanced Optimal Transport
Zifan Wang, Yi Shen, Michael Zavlanos, Karl H. Johansson
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya, Devdatt Dubhashi, Alexandru Gheorghiu
DiffSF: Diffusion Models for Scene Flow Estimation
Yushan Zhang, Bastian Wandt, Maria Magnusson, Michael Felsberg (spotlight poster)
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So, Mikael Johansson
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
Ruoqi Zhang, Ziwei Luo, Jens Sjölund, Thomas B. Schön, Per Mattsson
Workshops
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias Oechtering, Mikael Skoglund
Hiding in a Plain Sight: Out-of-Distribution Data in the Logit Space Embeddings
Vangjush Komini, Sarunas Girdzijauskas
Amplified Early Stopping Bias: Overestimated Performance with Deep Learning
Nona Rajabi, Antonio Ribeiro, Miguel Vasco, Danica Kragic
Published: October 21st, 2024