50 citations · 86 across the 17 of their papers we have counts for
18 papers
Double-Weighting for Covariate Shift Adaptation
José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu
Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates ) of training and testing samples $\mathrm{p}_\text{tr}(x…
Ambiguous Images With Human Judgments for Robust Visual Event Classification
Kate Sanders, Reno Kriz, Anqi Liu +1
Contemporary vision benchmarks predominantly consider tasks on which humans can achieve near-perfect performance. However, humans are frequently presented with visual data that the…
Repeated Environment Inference for Invariant Learning
Aayush Mishra, Anqi Liu
We study the problem of invariant learning when the environment labels are unknown. We focus on the invariant representation notion when the Bayes optimal conditional label distrib…
JAWS: Auditing Predictive Uncertainty Under Covariate Shift
Drew Prinster, Anqi Liu, Suchi Saria
We propose \textbf{JAWS}, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on the core method \textbf{JAW}, the \t…
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions
Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen +3
Tracking and collecting fast-evolving online discussions provides vast data for studying social media usage and its role in people's public lives. However, collecting social media…
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Anqi Liu, Hao Liu, Tongxin Li +3
Discovering the complete set of causal relations among a group of variables is a challenging unsupervised learning problem. Often, this challenge is compounded by the fact that the…
Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach
Haoxuan Wang, Zhiding Yu, Yisong Yue +3
We propose a framework for learning calibrated uncertainties under domain shifts, where the source (training) distribution differs from the target (test) distribution. We detect su…
Robust Fairness under Covariate Shift
Ashkan Rezaei, Anqi Liu, Omid Memarrast +1
Making predictions that are fair with regard to protected group membership (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing tec…
Active Learning under Label Shift
Eric Zhao, Anqi Liu, Animashree Anandkumar +1
We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a t…
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi +3
Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even…
Triply Robust Off-Policy Evaluation
Anqi Liu, Hao Liu, Anima Anandkumar +1
We propose a robust regression approach to off-policy evaluation (OPE) for contextual bandits. We frame OPE as a covariate-shift problem and leverage modern robust regression tools…
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates
Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen +2
Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper,…
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
Quanying Liu, Haiyan Wu, Anqi Liu
We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. T…
Robust Regression for Safe Exploration in Control
Anqi Liu, Guanya Shi, Soon-Jo Chung +2
We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn…
Regularized Learning for Domain Adaptation under Label Shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang +1
We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source…
Consistent Robust Adversarial Prediction for General Multiclass Classification
Rizal Fathony, Kaiser Asif, Anqi Liu +5
We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-cont…
Kernel Robust Bias-Aware Prediction under Covariate Shift
Anqi Liu, Rizal Fathony, Brian D. Ziebart
Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challeng…
Robust Covariate Shift Prediction with General Losses and Feature Views
Anqi Liu, Brian D. Ziebart
Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions. Unfortunately,…