248 citations · 377 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
A Unified View of Label Shift Estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan +1
Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. There are two dominant approaches for estimating the label margin…
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Chenjun Xiao, Yifan Wu, Chen Ma +2
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorit…
Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu +4
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such trai…
Behavior Regularized Offline Reinforcement Learning
Yifan Wu, George Tucker, Ofir Nachum
In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment. However in many real-world applications, access to the en…
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Yifan Wu, Ezra Winston, Divyansh Kaushik +1
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domai…
The Laplacian in RL: Learning Representations with Efficient Approximations
Yifan Wu, George Tucker, Ofir Nachum
The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the…
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
Yifan Wu, Barnabas Poczos, Aarti Singh
A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that h…