248 citations · 377 across the 16 of their papers we have counts for
16 papers
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 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…
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…
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…
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…
Estimating and Improving Fairness with Adversarial Learning
Xiaoxiao Li, Ziteng Cui, Yifan Wu +2
Fairness and accountability are two essential pillars for trustworthy Artificial Intelligence (AI) in healthcare. However, the existing AI model may be biased in its decision marki…
Optimization of Scoring Rules
Jason D. Hartline, Yingkai Li, Liren Shan +1
We characterize the optimal reward functions (scoring rules) that incentivize an agent to acquire information and report it truthfully to the principal. The optimal scoring rules l…
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…
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
Xiaoxiao Li, Vivek Singh, Yifan Wu +3
Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available. Thus, we propose Repetitive Motion Estimation Network (RME…
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…