research
∙
10/12/2022
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Natural agents can effectively learn from multiple data sources that dif...
share
research
∙
10/11/2022
ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning
The goal of offline reinforcement learning (RL) is to learn near-optimal...
research
∙
10/03/2022
Latent State Marginalization as a Low-cost Approach for Improving Exploration
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework...
research
∙
02/11/2022
Online Decision Transformer
Recent work has shown that offline reinforcement learning (RL) can be fo...
research
∙
06/09/2020
Near-Optimal Confidence Sequences for Bounded Random Variables
Many inference problems, such as sequential decision problems like A/B t...
research
∙
03/10/2020
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Datasets containing sensitive information are often sequentially analyze...
research
∙
03/07/2020
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Distributed training is useful to train complicated models to shorten th...
research
∙
05/23/2016
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent
We address the rectangular matrix completion problem by lifting the unkn...
research
∙
06/19/2015
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
We propose a simple, scalable, and fast gradient descent algorithm to op...
research
∙
03/18/2015