activity
20182021
most citedBehavior Regularized Offline Reinforcement Learning

248 citations · 334 across the 10 of their papers we have counts for

collaborators

15 papers

cs.LG20215 cited

Mixture Proportion Estimation and PU Learning: A Modern Approach

Saurabh Garg, Yifan Wu, Alex Smola +2

Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifie…

cs.DC2021

On the Fairness of Swarm Learning in Skin Lesion Classification

Di Fan, Yifan Wu, Xiaoxiao Li

in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by…

cs.DC2021

Accelerating GPU-Based Out-of-Core Stencil Computation with On-the-Fly Compression

Jingcheng Shen, Yifan Wu, Masao Okita +1

Stencil computation is an important class of scientific applications that can be efficiently executed by graphics processing units (GPUs). Out-of-core approach helps run large scal…

cs.CV2021

Unsupervised Learning of Multi-level Structures for Anomaly Detection

Songmin Dai, Jide Li, Lu Wang +3

The main difficulty in high-dimensional anomaly detection tasks is the lack of anomalous data for training. And simply collecting anomalous data from the real world, common distrib…

cs.LG20212 cited

On the Optimality of Batch Policy Optimization Algorithms

Chenjun Xiao, Yifan Wu, Tor Lattimore +5

Batch policy optimization considers leveraging existing data for policy construction before interacting with an environment. Although interest in this problem has grown significant…

cs.LG20216 cited

Instabilities of Offline RL with Pre-Trained Neural Representation

Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov +1

In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substanti…