30 citations · 66 across the 5 of their papers we have counts for
7 papers
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Brandon Trabucco, Xinyang Geng, Aviral Kumar +1
Black-box model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function, are ubiquitous in a wide range of domains,…
Conservative Objective Models for Effective Offline Model-Based Optimization
Brandon Trabucco, Aviral Kumar, Xinyang Geng +1
Computational design problems arise in a number of settings, from synthetic biology to computer architectures. In this paper, we aim to solve data-driven model-based optimization (…
Variable-Shot Adaptation for Online Meta-Learning
Tianhe Yu, Xinyang Geng, Chelsea Finn +1
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.…
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
Russell Mendonca, Xinyang Geng, Chelsea Finn +1
Reinforcement learning algorithms can acquire policies for complex tasks autonomously. However, the number of samples required to learn a diverse set of skills can be prohibitively…
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
Benjamin Eysenbach, Xinyang Geng, Sergey Levine +1
Multi-task reinforcement learning (RL) aims to simultaneously learn policies for solving many tasks. Several prior works have found that relabeling past experience with different r…
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja +1
Reinforcement learning requires manual specification of a reward function to learn a task. While in principle this reward function only needs to specify the task goal, in practice…