Yixin Wang
Assistant Professor
Studies probabilistic machine learning, causal inference and Bayesian statistics, including probabilistic views of language and diffusion models.
Yixin Wang's lab at Michigan works on probabilistic & causal inference, learning theory & optimization and image & video generation. 55 papers since 2023, 8 at top venues, mostly JMLR and Findings of EMNLP. Applications close .
At a glance
- papers, 2023–2026
- 14/ year
- at T1 venues
- 15%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 16 papers2026 · T1 3 · collecting
- 12 papers2025 · T1 1
- 14 papers2024 · T1 3
- 13 papers2023 · T1 1
T1 venuesOtherVenues: JMLR, Findings of EMNLP, Journal of the ACM
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference19 papers
- Learning theory & optimization19 papers
- probabilistic3 papers
- Image & video generation0 papers
- statistics0 papers
Paper list
From the lab homepage.
202616 papers
- Annals of Applied Statistics, 20(1):744-763, .A Bayesian Reinforcement Learning Framework for Optimizing the BCI-utility of P300 Brain-Computer Interfaces (opens in a new tab)
- JMLR (T1)A Negative-Control-Outcome Perspective on the Synthetic Control Method (opens in a new tab)
- JMLR (T1)Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .Are Easier or Harder Examples Better? Rethinking Data Selection for Reward Models and Preference Optimization (opens in a new tab)
- ACM Transactions on Recommender Systems, 4(1), Article 14, 1-20, .Breaking Feedback Loops in Recommender Systems with Causal Inference (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .Data-Constrained Language Model Pretraining: Improved Regularization and Scaling Laws (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .Human-AI Teaming Through the Lens of Calibration (opens in a new tab)
- International Conference on Machine Learning (ICML), .Learning to Extrapolate to New Tasks: A Relational Approach to Task Extrapolation (opens in a new tab)
- International Conference on Machine Learning (ICML), .Leveraging Lineage Barcodes as Natural Augmentations for Contrastive Learning of Cell Fate in scRNA-seq Data (opens in a new tab)
- International Conference on Artificial Intelligence and Statistics (AISTATS), .Meta-probabilistic Modeling (opens in a new tab)
- JMLR (T1)On the Identifiability of Regime-Switching Models with Multi-Lag Dependencies (opens in a new tab)
- International Journal of Research in Marketing, 43(2), Part B, 462-484, .Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices (opens in a new tab)
202512 papers
- International Conference on Artificial Intelligence and Statistics (AISTATS), .Accuracy on the Wrong Line: On the Pitfalls of Noisy Data for Out-of-distribution Generalisation (opens in a new tab)
- PNAS Nexus, 4(2):pgaf027, .Addressing Discretization-Induced Bias in Demographic Prediction (opens in a new tab)
- International Conference on Machine Learning (ICML), .Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation (opens in a new tab)
- JMLR (T1)Deep Generative Models: Complexity, Dimensionality, and Approximation (opens in a new tab)
- International Conference on Learning Representations (ICLR), .Doubly Robust Identification of Treatment Effects from Multiple Environments (opens in a new tab)
- International Conference on Machine Learning (ICML), .Identifying Neural Dynamics Using Interventional State Space Models (opens in a new tab)
- Annals of Applied Statistics, to appear.Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length (opens in a new tab)
- International Conference on Learning Representations (ICLR), .Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model (opens in a new tab)
- Transactions on Machine Learning Research (TMLR), .On the Challenges and Opportunities in Generative AI (opens in a new tab)
- Conference on Empirical Methods in Natural Language Processing (EMNLP), .Permutative Preference Alignment from Listwise Ranking of Human Judgments (opens in a new tab)
- International Conference on Artificial Intelligence and Statistics (AISTATS), .Posterior Mean Matching: Generative Modeling through Online Bayesian Inference (opens in a new tab)
- Journal of the American Statistical Association, 120(550), 1127-1138, .Valid Inference After Causal Discovery (opens in a new tab)
202414 papers
- Conference on Causal Learning and Reasoning (CLeaR), .A Causality-inspired Plus-minus Model for Player Evaluation in Team Sports (opens in a new tab)
- Journal of Biomedical Informatics, 155:104656, .Causal Fairness Assessment of Treatment Allocation with Electronic Health Records (opens in a new tab)
- Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), .Causal Inference for Human-Language Model Collaboration (opens in a new tab)
- Journal of the American Statistical Association, 119:545, 122-135, .Conformal Sensitivity Analysis for Individual Treatment Effects (opens in a new tab)
- JMLR (T1)Desiderata for Representation Learning: A Causal Perspective (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When (opens in a new tab)
- JMLR (T1)Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning (opens in a new tab)
- Conference on Empirical Methods in Natural Language Processing (EMNLP), .LLMs Are Prone to Fallacies in Causal Inference (opens in a new tab)
- International Conference on Artificial Intelligence and Statistics (AISTATS), .Multi-Domain Causal Representation Learning via Weak Distributional Invariances (opens in a new tab)
- International Conference on Artificial Intelligence and Statistics (AISTATS), .Offline Policy Evaluation and Optimization under Confounding (opens in a new tab)
- International Conference on Machine Learning (ICML), .On the Identifiability of Switching Dynamical Systems (opens in a new tab)
- JMLR (T1)Optimization-based Causal Estimation from Heterogeneous Environments (opens in a new tab)
202313 papers
- Transactions on Machine Learning Research (TMLR), .Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness (opens in a new tab)
- Uncertainty in Artificial Intelligence (UAI), .Bidirectional Attention as a Mixture of Continuous Word Experts (opens in a new tab)
- Machine Learning: Science and Technology, 4(2):025019, .Clarifying Trust of Materials Property Predictions Using Neural Networks with Distribution-Specific Uncertainty Quantification (opens in a new tab)
- ACS Synthetic Biology, 12, 11, 3242-3251, .Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction (opens in a new tab)
- ACM Conference on Fairness, Accountability, and Transparency (FAccT), .Delayed and Indirect Impacts of Link Recommendations (opens in a new tab)
- Transactions on Machine Learning Research (TMLR), .Detecting Incidental Correlation in Multimodal Learning via Latent Variable Modeling (opens in a new tab)
- International Conference on Machine Learning (ICML), .Interventional Causal Representation Learning (opens in a new tab)
- Statistica Sinica, 33(3):1789-1808, .Large Sample Properties of Matching for Balance (opens in a new tab)
- Journal of the ACM (T1)Learning Equilibria in Matching Markets with Bandit Feedback (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .On Learning Necessary and Sufficient Causal Graphs (opens in a new tab)
- Symposium on Conformal and Probabilistic Prediction with Applications (COPA), .Recommendation Systems with Distribution-Free Reliability Guarantees (opens in a new tab)
- International AAAI Conference on Web and Social Media (ICWSM), .Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
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