Yixin Wang
2022
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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
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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
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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
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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
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W. Tansey, Y. Wang, D.M. Blei, and R. Rabadan
Black Box FDR
International Conference on Machine Learning, 2018.
pdf link arxiv code
2017
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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