A list of past publications (By area), not updated frequently. Please refer to google scholar for most updated info.

Distributionally Robust Learning (under data shifts):

Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, and Anima Anandkumar. “Deep Distributionally Robust Learning for Calibrated Uncertainties under Domain Shift”, on Arxiv 2021.

Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, and Brian D. Ziebart “Consistent Robust Adversarial Prediction for General Multiclass Classification”, On Arxiv 2018.

Anqi Liu and Brian D. Ziebart “Robust Covariate Shift Prediction with General Losses and Feature Views”, On Arxiv 2017.

Anqi Liu, Rizal Fathony, and Brian D. Ziebart “Kernel Robust Bias-Aware Prediction under Covariate Shift”, On Arxiv.

Rizal Fathony, Anqi Liu, Kaiser Asif, and Brian D. Ziebart “Adversarial Multiclass Classification: A Risk Minimization Perspective”, In NeurIPS2016.

Xiangli Chen, Mathew Monfort, Anqi Liu, and Brian D. Ziebart “Robust Covariate Shift Regression”, In AISTATS2016.

Anqi Liu and Brian D. Ziebart “Robust Classification under Sample Selection Bias”, In NeurIPS2014. Spotlight.

Active Learning (under data shifts):

Eric Zhao, Anqi Liu, Anima Anandkumar, and Yisong Yue “Active Learning under Label Shift”, in AISTATS 2021.

Sima Behpour, Anqi Liu, and Brian D. Ziebart “Active Learning for Probabilistic Structured Prediction of Cuts and Matchings”, In ICML2019.

Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Anima Anandkumar “Regularized Learning for Domain Adaptation under Label Shifts”, In ICLR2019.

Anqi Liu, Lev Reyzin, and Brian D. Ziebart “Shift-Pessimistic Active Learning using Robust Bias-Aware Prediction”, In AAAI2015.

Safe Decision Making (under data shifts):

Hao Liu, Anima Anandkumar, Yisong Yue, and Anqi Liu. “Distributionally Robust Off-Policy Evaluation”, PDF coming soon.

Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, and Soon-Jo Chung. “Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems”, RA-L, 2020.

Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, and Yisong Yue “Robust Regression for Safe Exploration in Control”, In L4DC 2020.

Human Decision Making Modeling:

Anqi Liu, Hao Liu, Tongxin Li, Saeed Karimi Bidhendi, Yisong Yue, and Anima Anandkumar “Disentangling Observed Causal Effects from Latent Confounders using Method of Moments”, on Arxiv 2021.

Quanying Liu, Haiyan Wu, Anqi Liu “Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning”, in Real-world Sequential Decision Making Workshop, ICML 2019.

Mathew Monfort, Anqi Liu, and Brian D. Ziebart “Trajectory Forecasting and Intent Recognition via Predictive Inverse Linear-Quadratic Regulation”, In AAAI2015.

Fair Machine Learning:

Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian D. Ziebart. “Robust Fairness Under Covariate Shift”, in AAAI 2021.

Computational Social Science/AI for Social Good:

Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R Michael Alvarez, Anima Anandkumar “Dynamic Social Media Monitoring for Fast-Evolving Online Discussions”, In KDD ADS track, 2021.

Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R Michael Alvarez, Anima Anandkumar “Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates”, In AI for Social Good workshop at NeurIPS, 2019.

Text Mining/Information Retrieval:

Hong Wang, Anqi Liu, Jing Wang, Brian D Ziebart, Clement T Yu, Warren Shen “Context Retrieval for Web Tables”, In International Conference on The Theory of Information Retrieval, 2015