255 citations · 2.4k across the 127 of their papers we have counts for
16 papers · 2 filters
Understanding the Effect of Stochasticity in Policy Optimization
Jincheng Mei, Bo Dai, Chenjun Xiao +2
We study the effect of stochasticity in on-policy policy optimization, and make the following four contributions. First, we show that the preferability of optimization methods depe…
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
Han Zhong, Zhongren Chen, Zhuoran Yang +2
We study episodic reinforcement learning (RL) in non-stationary linear kernel Markov decision processes (MDPs). In this setting, both the reward function and the transition kernel…
Efficient Local Planning with Linear Function Approximation
Dong Yin, Botao Hao, Yasin Abbasi-Yadkori +2
We study query and computationally efficient planning algorithms with linear function approximation and a simulator. We assume that the agent only has local access to the simulator…
On the Role of Optimization in Double Descent: A Least Squares Study
Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata +2
Empirically it has been observed that the performance of deep neural networks steadily improves as we increase model size, contradicting the classical view on overfitting and gener…
No Regrets for Learning the Prior in Bandits
Soumya Basu, Branislav Kveton, Manzil Zaheer +1
We propose , a Thompson sampling algorithm that adapts sequentially to bandit tasks that it interacts with. The key idea in is to adapt to an unknown tas…
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping
Ilja Kuzborskij, Csaba Szepesvári
We explore the ability of overparameterized shallow neural networks to learn Lipschitz regression functions with and without label noise when trained by Gradient Descent (GD). To a…