Genre
Can neural operators always be continuously discretized? Takashi Furuya
We consider the problem of discretization of neural operators between Hilbert spaces in a general framework including skip connections. We focus on bijec-tive neural operators through the lens of diffeomorphisms in infinite dimensions.
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Project lead, main contributor, correspondence to alexandre.rame@isir.upmc.fr. Equal experimental contribution, order determined at random. Further information and resources related to this project can be found on this website.
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
Left: The reward function is a mixture of Gaussian, and the offline data distribution is unbalanced with most samples located in low-reward states.
b304a04c6a4ac168aa692923814ace17-Paper-Conference.pdf
Proceedings of the International Conference on Machine Learning 2024
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning Liyuan Mao Shanghai Jiao Tong University Haoran Xu UT Austin Xianyuan Zhan Tsinghua University Weinan Zhang
Based on this, we propose a novel approach, Diffusion-DICE, that directly performs this transformation using diffusion models.