50 citations · 93 across the 10 of their papers we have counts for
10 papers
Robotic Table Tennis: A Case Study into a High Speed Learning System
David B. D'Ambrosio, Jonathan Abelian, Saminda Abeyruwan +32
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the abilit…
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder +8
In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters. We demonstrate t…
Robotic Table Tennis with Model-Free Reinforcement Learning
Wenbo Gao, Laura Graesser, Krzysztof Choromanski +5
We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolut…
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
Xingyou Song, Yuxiang Yang, Krzysztof Choromanski +4
Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows…
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…
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension
Yunfei Teng, Wenbo Gao, Francois Chalus +3
We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a re…
ADMM for Multiaffine Constrained Optimization
Wenbo Gao, Donald Goldfarb, Frank E. Curtis
We expand the scope of the alternating direction method of multipliers (ADMM). Specifically, we show that ADMM, when employed to solve problems with multiaffine constraints that sa…
Quasi-Newton Methods: Superlinear Convergence Without Line Searches for Self-Concordant Functions
Wenbo Gao, Donald Goldfarb
We consider the use of a curvature-adaptive step size in gradient-based iterative methods, including quasi-Newton methods, for minimizing self-concordant functions, extending an ap…
Block BFGS Methods
Wenbo Gao, Donald Goldfarb
We introduce a quasi-Newton method with block updates called Block BFGS. We show that this method, performed with inexact Armijo-Wolfe line searches, converges globally and superli…