Deciding What to Learn: A Rate-Distortion Approach

15 January 2021

Papers citing "Deciding What to Learn: A Rate-Distortion Approach"

8 / 8 papers shown

Title
On Bits and Bandits: Quantifying the Regret-Information Trade-off Itai Shufaro Nadav Merlis Nir Weinberger Shie Mannor 38 0 0 26 May 2024
Bayesian Reinforcement Learning with Limited Cognitive Load Dilip Arumugam Mark K. Ho Noah D. Goodman Benjamin Van Roy OffRL 36 8 0 05 May 2023
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar C. Thornton William W. Howard R. M. Buehrer 19 1 0 01 Dec 2022
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning Dilip Arumugam Mark K. Ho Noah D. Goodman Benjamin Van Roy 31 4 0 30 Oct 2022
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning Dilip Arumugam Benjamin Van Roy OffRL 38 1 0 04 Jun 2022
Non-Stationary Bandit Learning via Predictive Sampling Yueyang Liu Kuang Xu Benjamin Van Roy 24 19 0 04 May 2022
The Value of Information When Deciding What to Learn Dilip Arumugam Benjamin Van Roy 37 12 0 26 Oct 2021
Reinforcement Learning, Bit by Bit Xiuyuan Lu Benjamin Van Roy Vikranth Dwaracherla M. Ibrahimi Ian Osband Zheng Wen 30 70 0 06 Mar 2021