ICLR

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces

Saket Tiwari, Omer Gottesman, George Konidaris

机构 * Brown University（布朗大学） ； Amazon Web Services（亚马逊网络服务） ； Department of Computer Science（计算机科学系） 纠错

Comments Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). arXiv admin note: text overlap with arXiv:2301.00009

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence

Dharmesh Tailor, Alvaro H. C. Correia, Eric Nalisnick, Christos Louizos

Comments Accepted at the 13th International Conference on Learning Representations (ICLR 2025)

Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training

Maximillian Chen, Ruoxi Sun, Tomas Pfister, Sercan Ö. Arık

机构 * Google（谷歌） ； Columbia University（哥伦比亚大学） 纠错

Comments ICLR 2025; Code: https://github.com/google-research/google-research/tree/master/learning_to_clarify

Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control

Devdhar Patel, Hava Siegelmann

机构 * Manning College of Information and Computer Sciences University of Massachusetts Amherst（信息与计算机科学学院 马萨诸塞大学阿默斯特分校） 纠错

Comments 30 pages, 14 figures, 7 tables. Presented at the Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore, April 24-28, 2025