13 citations · 26 across the 4 of their papers we have counts for
6 papers
Safe Reinforcement Learning by Imagining the Near Future
Garrett Thomas, Yuping Luo, Tengyu Ma
Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative con…
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
Zhiyuan Li, Yuping Luo, Kaifeng Lyu
Matrix factorization is a simple and natural test-bed to investigate the implicit regularization of gradient descent. Gunasekar et al. (2017) conjectured that Gradient Flow with in…
Provable Representation Learning for Imitation Learning via Bi-level Optimization
Sanjeev Arora, Simon S. Du, Sham Kakade +2
A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k.a. representation learning. We study this strategy in the imitation lea…
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
Yuping Luo, Huazhe Xu, Tengyu Ma
Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently. However, learning from demonstrations…
Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora, Nadav Cohen, Wei Hu +1
Efforts to understand the generalization mystery in deep learning have led to the belief that gradient-based optimization induces a form of implicit regularization, a bias towards…
An online sequence-to-sequence model for noisy speech recognition
Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo +4
Generative models have long been the dominant approach for speech recognition. The success of these models however relies on the use of sophisticated recipes and complicated machin…