Yixin Wang

University of Michigan yixinw@umich.edu

home papers teaching

2024
  • K.C. Wibisono and Y. Wang
    From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When
    Neural Information Processing Systems (NeurIPS), 2024.
    arxiv
  • P. Gradu*, T. Zrnic*, Y. Wang, and M.I. Jordan
    Valid Inference after Causal Discovery
    Journal of American Statistical Association, to appear.
    arxiv
  • Y. Wang and M.I. Jordan
    Desiderata for Representation Learning: A Causal Perspective
    Journal of Machine Learning Research, 25(275):1−65, 2024.  
    ACIC Tom Ten Have Award Honorable Mention
    ICSA Conference Junior Researcher Award
    link code slides
  • L. Liao*, Z. Fu*, Z. Yang, Y. Wang, D. Ma, M. Kolar, and Z. Wang
    Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
    Journal of Machine Learning Research, to appear.  
    arxiv
  • S.J. Yang, Y. Wang, and K.Z. Lin
    LCL: Contrastive Learning for Lineage Barcoded scRNA-seq Data
    bioRxiv:2024.10.28.620670
    bioRxiv
  • A. Sanyal, Y. Hu, Y. Yu, Y.A. Ma, Y. Wang, B. Schoelkopf
    Accuracy on the Wrong Line: On the Pitfalls of Noisy Data for Out-of-distribution Generalisation
    arXiv:2406.19049
    Oral presentation at ICML 2024 Workshop on the Next Generation of AI Safety
    arxiv
  • N. Joshi, A. Saparov, Y. Wang, and H. He
    LLMs Are Prone to Fallacies in Causal Inference
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
    arxiv
  • B. Zhang, Y. Wang, and P.S. Dhillon
    Causal Inference for Human-Language Model Collaboration
    Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.
    arxiv
  • H. Liu, Z.-Y. Dou, Y. Wang, N. Peng, and Y. Yue
    Uncertainty Calibration for Tool-Using Language Agents
    Findings of the Association for Computational Linguistics: EMNLP (EMNLP Findings), 2024.
    arxiv
  • E. Dong, A. Schein, Y. Wang, and N. Garg
    Addressing Discretization-Induced Bias in Demographic Prediction
    arXiv:2405.16762  
    arxiv
  • C. Balsells-Rodas, Y. Wang, and Y. Li
    On the Identifiability of Switching Dynamical Systems
    International Conference on Machine Learning (ICML), 2024.
    arxiv
  • M. Yin, C. Shi, Y. Wang, and D.M. Blei
    Conformal Sensitivity Analysis for Individual Treatment Effects
    Journal of the American Statistical Association, 119:545, 122-135, 2024.
    link code
  • K. Ahuja, A. Mansouri, and Y. Wang
    Multi-Domain Causal Representation Learning via Weak Distributional Invariances
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
    arxiv
  • L. Zhang, L.R. Richter, Y. Wang, A. Ostropolets, N. Elhadad, D.M. Blei, G. Hripcsak.
    Causal Fairness Assessment of Treatment Allocation with Electronic Health Records
    Journal of Biomedical Informatics, 2024, 104656.  
    arxiv link
  • M. Yin, Y. Wang, and D.M. Blei
    Optimization-based Causal Estimation from Heterogeneous Environments
    Journal of Machine Learning Research, 25(168):1-44, 2024.  
    arxiv code
  • C. Kausik*, K. Tan*, Y. Lu*, M. Makar, Y. Wang, and A. Tewari
    Offline Policy Evaluation and Optimization under Confounding
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
    arxiv
  • R. Dew, N. Padilla, L.E. Luo, S. Oblander, A. Ansari, K. Boughanmi, M. Braun, F.M. Feinberg, J. Liu, T. Otter, L. Tian, Y. Wang, and M. Yin
    Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices
    SSRN preprint  
    SSRN code
  • C. Balsells-Rodas, Y. Wang, P.A.M. Mediano, and Y. Li
    Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
    arXiv:2406.17698
    arxiv
  • L. Manduchi*, K. Pandey*, R. Bamler, R. Cotterell, S. Daubener, S. Fellenz, A. Fischer, T. Gartner, M. Kirchler, M. Kloft, Y. Li, C. Lippert, G. de Melo, E. T. Nalisnick, B. Ommer, R. Ranganath, M. Rudolph, K. Ullrich, G. Van den Broeck, J. E Vogt, Y. Wang, F. Wenzel, F. Wood, S. Mandt, and V. Fortuin.
    On the Challenges and Opportunities in Generative AI
    arXiv:2403.00025  
    arxiv
  • C. De Bacco, Y. Wang, and D.M. Blei.
    A Causality-inspired Plus-minus Model for Player Evaluation in Team Sports
    Conference on Causal Learning and Reasoning (CLeaR), 2024.  
    link
2023
  • K. Ahuja, D. Mahajan, Y. Wang, and Y. Bengio
    Interventional Causal Representation Learning
    International Conference on Machine Learning (ICML), 2023.
    Oral Presentation (Top 2% of All Submissions)
    arxiv code
  • K.C. Wibisono and Y. Wang
    Bidirectional Attention as a Mixture of Continuous Word Experts
    Uncertainty in Artificial Intelligence, 2023.
    arxiv code
  • Y. Wang*, A. Degleris*, A.H. Williams, and S.W. Linderman
    Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
    Journal of the American Statistical Association, to appear.  
    arxiv code
  • H. Nisonoff, Y. Wang, and J. Listgarten
    Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction
    ACS Synthetic Biology, 12, 11, 3242–3251, 2023.
    Featured as ACS Editors' Choice
    Selected for the special collection in honor of Darwin Day (2024)  
    arxiv code link
  • M. Jagadeesan*, A. Wei*, Y. Wang, M.I. Jordan, and J. Steinhardt
    Learning Equilibria in Matching Markets from Bandit Feedback
    Journal of the ACM, 70, 3, 46, 2023.
    Short version appeared in Neural Information Processing Systems (NeurIPS), 2021; Spotlight Presentation (Top 3% of All Submissions)
    arxiv
  • B. Zhu, S. Bates, Z. Yang, Y. Wang, J. Jiao, and M.I. Jordan
    The Sample Complexity of Online Contract Design
    ACM Conference on Economics and Computation (EC), 2023.
    arxiv
  • H. Zhang, S. Lu, Y. Wang, and M. Curmei
    Delayed and Indirect Impacts of Link Recommendations
    ACM Conference on Fairness, Accountability, and Transparency (FaccT), 2023.
    arxiv link code
  • H. Cai, Y. Wang, M.I. Jordan, and R. Song
    On Learning Necessary and Sufficient Causal Graphs
    Neural Information Processing Systems (NeurIPS), 2023.
    Spotlight Presentation (Top 3% of All Submissions)
    arxiv
  • A.N. Angelopoulos*, K. Krauth*, S. Bates, Y. Wang, and M.I. Jordan
    Recommendation Systems with Distribution-Free Reliability Guarantees
    Symposium on Conformal and Probabilistic Prediction with Applications (COPA), 2023.
    Alexey Chervonenkis Best Paper Award
    arxiv
  • T. Makino, Y. Wang, K.J. Geras, and K. Cho.
    Detecting incidental correlation in multimodal learning via latent variable modeling
    Transactions on Machine Learning Research (TMLR), 2023  
    link
  • Y. Wang, D. Sridhar, and D.M. Blei
    Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness
    Transactions on Machine Learning Research (TMLR), 2023  
    link
  • X. Lu, W. Ai, Y. Wang, and Q. Mei
    Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic
    International AAAI Conference on Web and Social Media (ICWSM), 2023.
    arxiv
  • Y. Wang and J.R. Zubizarreta
    Large Sample Properties of Matching for Balance
    Statistica Sinica, 33, 3, 2023.  
    link arxiv
  • C.J. Gruich, V. Madhavan, Y. Wang, and B.R. Goldsmith
    Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification
    Machine Learning: Science and Technology, 4, 2, 2023.  
    arxiv
2022
  • 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
  • K. Krauth, Y. Wang, and M.I. Jordan
    Breaking Feedback Loops in Recommender Systems with Causal Inference
    arXiv:2207.01616  
    arxiv
  • P. Chatha, Y. Wang, Z. Wu, and J. Regier
    Dynamic Survival Transformers for Causal Inference with Electronic Health Records
    arXiv:2210.15417  
    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 (TMLR), 2022.  
    arxiv
  • C. Mendler-Dünner, F. Ding, and Y. Wang
    Anticipating Performativity by Predicting from Predictions
    Neural Information Processing Systems (NeurIPS), 2022.
    arxiv
  • M.I. Jordan*, Y. Wang*, and A. Zhou*
    Empirical Gateaux Derivatives for Causal Inference
    Neural Information Processing Systems (NeurIPS), 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 (TMLR), 2022.  
    link code pdf
  • 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 (CLeaR), 2022. 
    arxiv link code
2021
  • Y. Wang, D.M. Blei, and J.P. Cunningham
    Posterior Collapse and Latent Variable Non-identifiability
    Neural Information Processing Systems (NeurIPS), 2021.
    link code slides
  • Y. Wang and D.M. Blei
    A Proxy Variable View of Shared Confounding
    International Conference on Machine Learning (ICML), 2021.  
    pdf link code
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 (NeurIPS), 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 (RecSys), 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 (NeurIPS), 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 (NeurIPS), 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
2018
  • W. Tansey, Y. Wang, D.M. Blei, and R. Rabadan
    Black Box FDR
    International Conference on Machine Learning (ICML), 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 (ICML), 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 (ICML), 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