111 citations · 158 across the 13 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.LG2023★ 1 cited
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
Daniel Kane, Sihan Liu, Shachar Lovett +3
A fundamental question in reinforcement learning theory is: suppose the optimal value functions are linear in given features, can we learn them efficiently? This problem's counterp…
cs.LG2023
Learning Hidden Markov Models Using Conditional Samples
Sham M. Kakade, Akshay Krishnamurthy, Gaurav Mahajan +1
This paper is concerned with the computational complexity of learning the Hidden Markov Model (HMM). Although HMMs are some of the most widely used tools in sequential and time ser…
cs.LG2023
Do PAC-Learners Learn the Marginal Distribution?
Max Hopkins, Daniel M. Kane, Shachar Lovett +1
The Fundamental Theorem of PAC Learning asserts that learnability of a concept class is equivalent to the of empirical error in to its mean,…