1k citations · 1.8k across the 17 of their papers we have counts for
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Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
Rui Zhao, Jinming Song, Yufeng Yuan +5
We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data. Although such agents can be obtained through se…
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu, Siddharth Srivastava, Nicholas Hay +2
Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine disc…
Meta-Learning MCMC Proposals
Tongzhou Wang, Yi Wu, David A. Moore +1
Effective implementations of sampling-based probabilistic inference often require manually constructed, model-specific proposals. Inspired by recent progresses in meta-learning for…
Swift: Compiled Inference for Probabilistic Programming Languages
Yi Wu, Lei Li, Stuart Russell +1
A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilit…
Towards Practical Bayesian Parameter and State Estimation
Yusuf Bugra Erol, Yi Wu, Lei Li +1
Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and…
Value Iteration Networks
Aviv Tamar, Yi Wu, Garrett Thomas +2
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predict…