48 citations · 227 across the 25 of their papers we have counts for
5 papers · 1 filter
DCUR: Data Curriculum for Teaching via Samples with Reinforcement Learning
Daniel Seita, Abhinav Gopal, Zhao Mandi +1
Deep reinforcement learning (RL) has shown great empirical successes, but suffers from brittleness and sample inefficiency. A potential remedy is to use a previously-trained policy…
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
Daniel Seita, David Chan, Roshan Rao +3
Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning. When providing expert demonstrations to human s…
Risk Averse Robust Adversarial Reinforcement Learning
Xinlei Pan, Daniel Seita, Yang Gao +1
Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the tra…
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
Daniel Seita, Xinlei Pan, Haoyu Chen +1
We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data. Previous work on reducing the cost of Metropolis-Hastings tests…
Fast Parallel SAME Gibbs Sampling on General Discrete Bayesian Networks
Daniel Seita, Haoyu Chen, John Canny
A fundamental task in machine learning and related fields is to perform inference on Bayesian networks. Since exact inference takes exponential time in general, a variety of approx…