Yixin Wang

Orcid: 0000-0002-6617-4842

Affiliations:

University of Michigan, Department of Statistics, Ann Arbor, MI, USA
University of California at Berkeley (UC Berkeley), Department of EECS, USA
Columbia University, Department of Statistics, NY, USA (PhD 2020)

According to our database¹, Yixin Wang authored at least 50 papers between 2016 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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In proceedings

Article

PhD thesis

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Bibliography

2025

Bayesian Invariance Modeling of Multi-Environment Data.

[BibT_eX]

[DOI]

CoRR, June, 2025

Posterior Mean Matching: Generative Modeling through Online Bayesian Inference.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

Representation Learning: A Causal Perspective.

[BibT_eX]

[DOI]

Yixin Wang

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Optimization-based Causal Estimation from Heterogeneous Environments.

[BibT_eX]

[DOI]

Mingzhang Yin

Yixin Wang

David M. Blei

J. Mach. Learn. Res., 2024

Causal fairness assessment of treatment allocation with electronic health records.

[BibT_eX]

[DOI]

J. Biomed. Informatics, 2024

CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

A causality-inspired plus-minus model for player evaluation in team sports.

[BibT_eX]

[DOI]

Caterina De Bacco

Yixin Wang

David M. Blei

Proceedings of the Causal Learning and Reasoning, 2024

2023

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification.

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., June, 2023

Learning Equilibria in Matching Markets with Bandit Feedback.

[BibT_eX]

[DOI]

J. ACM, June, 2023

Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness.

[BibT_eX]

[DOI]

Yixin Wang

Dhanya Sridhar

David M. Blei

Trans. Mach. Learn. Res., 2023

Addressing Hindsight Bias in Multigoal Reinforcement Learning.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2023

The Sample Complexity of Online Contract Design.

[BibT_eX]

[DOI]

Proceedings of the 24th ACM Conference on Economics and Computation, 2023

CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

On Learning Necessary and Sufficient Causal Graphs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Delayed and Indirect Impacts of Link Recommendations.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Recommendation Systems with Distribution-Free Reliability Guarantees.

[BibT_eX]

[DOI]

Anastasios N. Angelopoulos

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2023

2022

Identifiable Deep Generative Models via Sparse Decoding.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

Adjusting for indirectly measured confounding using large-scale propensity score.

[BibT_eX]

[DOI]

J. Biomed. Informatics, 2022

A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making.

[BibT_eX]

[DOI]

CoRR, 2022

Valid Inference after Causal Discovery.

[BibT_eX]

[DOI]

CoRR, 2022

Breaking Feedback Loops in Recommender Systems with Causal Inference.

[BibT_eX]

[DOI]

Karl Krauth

Yixin Wang

Michael I. Jordan

CoRR, 2022

Empirical Gateaux Derivatives for Causal Inference.

[BibT_eX]

[DOI]

Michael I. Jordan

Yixin Wang

Angela Zhou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Partial Identification with Noisy Covariates: A Robust Optimization Approach.

[BibT_eX]

[DOI]

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Identifiable Variational Autoencoders via Sparse Decoding.

[BibT_eX]

[DOI]

CoRR, 2021

Optimization-based Causal Estimation from Heterogenous Environments.

[BibT_eX]

[DOI]

Mingzhang Yin

Yixin Wang

David M. Blei

CoRR, 2021

Desiderata for Representation Learning: A Causal Perspective.

[BibT_eX]

[DOI]

Yixin Wang

Michael I. Jordan

CoRR, 2021

Multi-Source Causal Inference Using Control Variates.

[BibT_eX]

[DOI]

CoRR, 2021

Posterior Collapse and Latent Variable Non-identifiability.

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Equilibria in Matching Markets from Bandit Feedback.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bayesian Causal Inference for Real World Interactive Systems.

[BibT_eX]

[DOI]

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

A Proxy Variable View of Shared Confounding.

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Towards Clarifying the Theory of the Deconfounder.

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

CoRR, 2020

Causal Inference for Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures.

[BibT_eX]

[DOI]

Proceedings of the AMIA 2020, 2020

2019

The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019).

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

CoRR, 2019

Multiple Causes: A Causal Graphical View.

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

CoRR, 2019

Equal Opportunity and Affirmative Action via Counterfactual Predictions.

[BibT_eX]

[DOI]

Yixin Wang

Dhanya Sridhar

David M. Blei

CoRR, 2019

The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs).

[BibT_eX]

[DOI]

CoRR, 2019

Using Embeddings to Correct for Unobserved Confounding.

[BibT_eX]

[DOI]

Victor Veitch

Yixin Wang

David M. Blei

CoRR, 2019

Variational Bayes under Model Misspecification.

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Using Embeddings to Correct for Unobserved Confounding in Networks.

[BibT_eX]

[DOI]

Victor Veitch

Yixin Wang

David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning for Healthcare Conference, 2019

2018

The Deconfounded Recommender: A Causal Inference Approach to Recommendation.

[BibT_eX]

[DOI]

CoRR, 2018

The Blessings of Multiple Causes.

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

CoRR, 2018

Black Box FDR.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Frequentist Consistency of Variational Bayes.

[BibT_eX]

[DOI]

Yixin Wang

David M. Blei

CoRR, 2017

Robust Probabilistic Modeling with Bayesian Data Reweighting.

[BibT_eX]

[DOI]

Yixin Wang

Alp Kucukelbir

David M. Blei

Proceedings of the 34th International Conference on Machine Learning, 2017

Evaluating Bayesian Models with Posterior Dispersion Indices.

[BibT_eX]

[DOI]

Alp Kucukelbir

Yixin Wang

David M. Blei

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Reweighted Data for Robust Probabilistic Models.

[BibT_eX]

[DOI]

Yixin Wang

Alp Kucukelbir

David M. Blei

CoRR, 2016

Yixin Wang

Timeline

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