Yixin Wang

University of Michigan yixinw@umich.edu

home papers teaching

2022
  • K. Ahuja, Y. Wang, D. Mahajan, and Y. Bengio
    Interventional Causal Representation Learning
    arXiv:2209.11924  
    arxiv
  • K. Bhatia*, N.L. Kuang*, Y.-A. Ma*, and Y. Wang*
    Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
    arXiv:2207.11208  
    arxiv
  • P. Gradu*, T. Zrnic*, Y. Wang, and M.I. Jordan
    Valid Inference after Causal Discovery
    arXiv:2208.05949  
    arxiv
  • K. Krauth, Y. Wang, and M.I. Jordan
    Breaking Feedback Loops in Recommender Systems with Causal Inference
    arXiv:2207.01616  
    arxiv
  • B. Zhu, S. Bates, Z. Yang, Y. Wang, J. Jiao, and M.I. Jordan
    The Sample Complexity of Online Contract Design
    arXiv:2211.05732  
    arxiv
  • P. Chatha, Y. Wang, Z. Wu, and J. Regier
    Dynamic Survival Transformers for Causal Inference with Electronic Health Records
    arXiv:2210.15417  
    arxiv
  • L. Zhang, L.R. Richter, Y. Wang, A. Ostropolets, N. Elhadad, D.M. Blei, G. Hripcsak.
    A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making
    arXiv:2211.11183  
    arxiv
  • K. Tan*, Y. Lu*, C. Kausik, Y. Wang, and A. Tewari
    Offline Policy Evaluation and Optimization under Confounding
    arXiv:2211.16583  
    arxiv
  • H. Nisonoff, Y. Wang, and J. Listgarten
    Augmenting Neural Networks with Priors on Function Values
    arXiv:2202.04798  
    arxiv
  • A.N. Angelopoulos*, K. Krauth*, S. Bates, Y. Wang, and M.I. Jordan
    Recommendation Systems with Distribution-Free Reliability Guarantees
    arXiv:2207.01609  
    arxiv
  • Y. Wang*, A. Degleris*, A.H. Williams, and S.W. Linderman
    Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
    arXiv:2201.05044  
    arxiv code
  • M. Jagadeesan*, A. Wei*, Y. Wang, M.I. Jordan, and J. Steinhardt
    Learning Equilibria in Matching Markets from Bandit Feedback
    Journal of the ACM, to appear.
    Short version appeared in Neural Information Processing Systems, 2021; Spotlight Presentation (Top 3% of All Submissions)
    arxiv
  • W. Guo*, S. Wang*, P. Ding, Y. Wang, and M.I. Jordan
    Multi-Source Causal Inference Using Control Variates
    Transactions on Machine Learning Research, 2022.  
    arxiv
  • C. Mendler-Dünner, F. Ding, and Y. Wang
    Anticipating Performativity by Predicting from Predictions
    Neural Information Processing Systems, 2022.
    arxiv
  • M.I. Jordan*, Y. Wang*, and A. Zhou*
    Empirical Gateaux Derivatives for Causal Inference
    Neural Information Processing Systems, 2022.
    Oral Presentation (Top 3% of All Submissions)
    arxiv
  • G.E. Moran, D. Sridhar, Y. Wang, and D.M. Blei
    Identifiable Variational Autoencoders via Sparse Decoding
    Transactions on Machine Learning Research, 2022.  
    link code pdf
  • M. Yin, C. Shi, Y. Wang, and D.M. Blei
    Conformal Sensitivity Analysis for Individual Treatment Effects
    Journal of the American Statistical Association, to appear.  
    link code
  • L. Zhang, Y. Wang, M. Schuemie, D.M. Blei, and G. Hripcsak
    Adjusting for Indirectly Measured Confounding Using Large-scale Propensity Score
    Journal of Biomedical Informatics, 104204, 2022.  
    link slides
  • W. Guo, M. Yin, Y. Wang and M.I. Jordan
    Partial Identification with Noisy Covariates: A Robust Optimization Approach
    Causal Learning and Reasoning, 2022. 
    arxiv link code
2021
  • Y. Wang and M.I. Jordan
    Desiderata for Representation Learning: A Causal Perspective
    arXiv:2109.03795  
    ACIC Tom Ten Have Award Honorable Mention
    arxiv code slides
  • M. Yin, Y. Wang, and D.M. Blei
    Optimization-based Causal Estimation from Heterogeneous Environments
    arXiv:2109.11990  
    arxiv code
  • Y. Wang, D.M. Blei, and J.P. Cunningham
    Posterior Collapse and Latent Variable Non-identifiability
    Neural Information Processing Systems, 2021.
    link code slides
  • Y. Wang and J.R. Zubizarreta
    Large Sample Properties of Matching for Balance
    Statistica Sinica, to appear.  
    link arxiv
  • C. Bai, L. Wang, Y. Wang, Z. Wang, R. Zhao, C. Bai, and P. Liu
    Addressing Hindsight Bias in Multi-Goal Reinforcement Learning
    IEEE Transactions on Cybernetics, to appear.
    link code
  • Y. Wang and D.M. Blei
    A Proxy Variable View of Shared Confounding
    International Conference on Machine Learning, 2021.  
    pdf link code
  • L. Liao*, Z. Fu*, Z. Yang, Y. Wang, M. Kolar, and Z. Wang
    Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
    arXiv:2102.09907  
    arxiv
2020
  • W. Tansey, Y. Wang, R. Rabadan, and D.M. Blei
    Double Empirical Bayes Testing
    International Statistical Review, 88:S91-S113, 2020.  
    pdf link code
  • A. Williams, A. Degleris, Y. Wang, and S.W. Linderman
    Point Process Models for Sequence Detection in High-dimensional Neural Spike Trains
    Neural Information Processing Systems, 2020.
    Oral Presentation (Top 1.1% of All Submissions)  
    pdf link arxiv code
  • Y. Wang, D. Liang, L. Charlin, and D.M. Blei
    Causal Inference for Recommender Systems
    ACM Conference on Recommender Systems, 2020.  
    pdf link code
  • Y. Wang and J.R. Zubizarreta
    Minimal Dispersion Approximately Balancing Weights: Asymptotic Properties and Practical Considerations
    Biometrika, 107:1, 93–105, 2020.
    pdf link arxiv code
  • Y. Wang and D.M. Blei
    Towards Clarifying the Theory of the Deconfounder
    arXiv:2003.04948  
    arxiv
2019
  • Y. Wang and D.M. Blei
    The Blessings of Multiple Causes
    (with discussion)
    Journal of the American Statistical Association, 114:528, 1574-1596, 2019.  
    link rejoinder tutorial code
  • Y. Wang and D.M. Blei
    Frequentist Consistency of Variational Bayes
    Journal of the American Statistical Association 114.527: 1147-1161, 2019.
    INFORMS Data Mining Best Paper Award  
    pdf link arxiv
  • Y. Wang and D.M. Blei
    Variational Bayes under Model Misspecification
    Neural Information Processing Systems, 2019.  
    pdf link arxiv code
  • V. Veitch, Y. Wang, and D.M. Blei
    Using Embeddings to Correct for Unobserved Confounding in Networks
    Neural Information Processing Systems, 2019.  
    pdf link arxiv code
  • Y. Wang, A.C. Miller, and D.M. Blei
    Comment: Variational Autoencoders as Empirical Bayes
    Statistical Science 34.2: 229-233, 2019.  
    pdf link
  • L. Zhang, Y. Wang, A. Ostropolets, J.J. Mulgrave, D.M. Blei, and G. Hripcsak
    The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs)
    Machine Learning for Healthcare, 2019.  
    pdf link code
  • Y. Wang, D. Sridhar, and D.M. Blei
    Equal Opportunity and Affirmative Action via Counterfactual Predictions
    arXiv:1905.10870  
    arxiv
2018
  • W. Tansey, Y. Wang, D.M. Blei, and R. Rabadan
    Black Box FDR
    International Conference on Machine Learning, 2018.  
    pdf link arxiv code
2017
  • A. Kucukelbir, Y. Wang, and D.M. Blei
    Evaluating Bayesian Models with Posterior Dispersion Indices
    International Conference on Machine Learning, 2017.  
    pdf link arxiv code
  • Y. Wang, A. Kucukelbir, and D.M. Blei
    Robust Probabilistic Modeling with Bayesian Data Reweighting
    International Conference on Machine Learning, 2017.
    ICSA Conference Young Researcher Award  
    pdf link arxiv code
2016
  • Y. Wang and M.K.P. So
    A Bayesian Hierarchical Model for Spatial Extremes with Multiple Durations
    Computational Statistics & Data Analysis 95: 39-56, 2016.  
    pdf link