Yixin Wang
2024
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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 -
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 -
M. Yin, Y. Wang, and D.M. Blei
Optimization-based Causal Estimation from Heterogeneous Environments
Journal of Machine Learning Research, to appear.
arxiv code -
L. Liao*, Z. Fu*, Z. Yang, Y. Wang, M. Kolar, and Z. Wang
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Journal of Machine Learning Research, to appear.
arxiv -
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 -
E. Dong, A. Schein, Y. Wang, and N. Garg
What to Do about Argmax Bias: An Application to US Voter Registration Data
ACM Conference on Fairness, Accountability, and Transparency (FaccT), 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 -
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
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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 -
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, to appear.
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. Balsells-Rodas, Y. Wang, and Y. Li
Identifiability of Markov Switching Models
arXiv:2305.15925
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
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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 -
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
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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
ICSA Conference Junior Researcher Award
arxiv code slides -
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
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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 (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
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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 (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
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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
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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