273 citations · 616 across the 36 of their papers we have counts for
9 papers · 1 filter
ES-MAML: Simple Hessian-Free Meta Learning
Xingyou Song, Wenbo Gao, Yuxiang Yang +3
We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on poli…
Reinforcement Learning with Chromatic Networks for Compact Architecture Search
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder +6
We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way. By def…
Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization
Albert S Berahas, Liyuan Cao, Krzysztof Choromanski +1
In this paper, we consider derivative free optimization problems, where the objective function is smooth but is computed with some amount of noise, the function evaluations are exp…
Learning to Score Behaviors for Guided Policy Optimization
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang +3
We introduce a new approach for comparing reinforcement learning policies, using Wasserstein distances (WDs) in a newly defined latent behavioral space. We show that by utilizing t…
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder +1
We propose a new class of structured methods for Monte Carlo (MC) sampling, called DPPMC, designed for high-dimensional nonisotropic distributions where samples are correlated to r…
A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization
Albert S. Berahas, Liyuan Cao, Krzysztof Choromanski +1
In this paper, we analyze several methods for approximating gradients of noisy functions using only function values. These methods include finite differences, linear interpolation,…