Wang, Yixin

60 publications

ICLR 2026 Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching Yidan Xu, Yixin Wang, XuanLong Nguyen

ICLR 2026 Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces Mike A Merrill, Alexander Glenn Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Yeon Shin, Thomas Walshe, E. Kelly Buchanan, Junhong Shen, Guanghao Ye, Haowei Lin, Jason Poulos, Maoyu Wang, Marianna Nezhurina, Di Lu, Orfeas Menis Mastromichalakis, Zhiwei Xu, Zizhao Chen, Yue Liu, Robert Zhang, Leon Liangyu Chen, Anurag Kashyap, Jan-Lucas Uslu, Jeffrey Li, Jianbo Wu, Minghao Yan, Song Bian, Vedang Sharma, Ke Sun, Steven Dillmann, Akshay Anand, Andrew Lanpouthakoun, Bardia Koopah, Changran Hu, Etash Kumar Guha, Gabriel H. S. Dreiman, Jiacheng Zhu, Karl Krauth, Li Zhong, Niklas Muennighoff, Robert Kwesi Amanfu, Shangyin Tan, Shreyas Pimpalgaonkar, Tushar Aggarwal, Xiangning Lin, Xin Lan, Xuandong Zhao, Yiqing Liang, Yuanli Wang, Zilong Wang, Changzhi Zhou, David Heineman, Hange Liu, Harsh Trivedi, John Yang, Junhong Lin, Manish Shetty, Michael Yang, Nabil Omi, Negin Raoof, Shanda Li, Terry Yue Zhuo, Wuwei Lin, Yiwei Dai, Yuxin Wang, Wenhao Chai, Shang Zhou, Dariush Wahdany, Ziyu She, Jiaming Hu, Zhikang Dong, Yuxuan Zhu, Sasha Cui, Ahson Saiyed, Arinbjörn Kolbeinsson, Christopher Michael Rytting, Ryan Marten, Yixin Wang, Jenia Jitsev, Alex Dimakis, Andy Konwinski, Ludwig Schmidt

AISTATS 2025 Accuracy on the Wrong Line: On the Pitfalls of Noisy Data for Out-of-Distribution Generalisation Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf

AAAI 2025 Audio-Visual Adaptive Fusion Network for Question Answering Based on Contrastive Learning Xujian Zhao, Yixin Wang, Peiquan Jin

ICLRW 2025 Bayesian Invariance Modeling of Multi-Environment Data Luhuan Wu, Mingzhang Yin, Yixin Wang, John Patrick Cunningham, David Blei

ICML 2025 Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation Chang Liu, Yixin Wang, Moontae Lee

JMLR 2025 Deep Generative Models: Complexity, Dimensionality, and Approximation Kevin Wang, Hongqian Niu, Yixin Wang, Didong Li

ICLR 2025 Doubly Robust Identification of Treatment Effects from Multiple Environments Piersilvio De Bartolomeis, Julia Kostin, Javier Abad, Yixin Wang, Fanny Yang

ICML 2025 Identifying Neural Dynamics Using Interventional State Space Models Amin Nejatbakhsh, Yixin Wang

ICLRW 2025 Last Layer Empirical Bayes Valentin Villecroze, Yixin Wang, Gabriel Loaiza-Ganem

ICLR 2025 Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model Zhiwei Xu, Zhiyu Ni, Yixin Wang, Wei Hu

TMLR 2025 On the Challenges and Opportunities in Generative AI Laura Manduchi, Clara Meister, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin

AISTATS 2025 Posterior Mean Matching: Generative Modeling Through Online Bayesian Inference Sebastian Salazar, Michal Kucer, Yixin Wang, Emily Casleton, David Blei

AAAI 2025 Representation Learning: A Causal Perspective Yixin Wang

NeurIPS 2025 Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics Jiayuan Ding, Jianhui Lin, Shiyu Jiang, Yixin Wang, Ziyang Miao, Zhaoyu Fang, Jiliang Tang, Min Li, Xiaojie Qiu

CLeaR 2024 A Causality-Inspired Plus-Minus Model for Player Evaluation in Team Sports Caterina De Bacco, Yixin Wang, David Blei

ICMLW 2024 Accuracy on the Wrong Line: On the Pitfalls of Noisy Data for OOD Generalisation Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf

JMLR 2024 Desiderata for Representation Learning: A Causal Perspective Yixin Wang, Michael I. Jordan

NeurIPS 2024 From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When Kevin Christian Wibisono, Yixin Wang

ICMLW 2024 In-Context Learning from Training on Unstructured Data: The Role of Co-Occurrence, Positional Information, and Training Data Structure Kevin Christian Wibisono, Yixin Wang

JMLR 2024 Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Dingli Ma, Mladen Kolar, Zhaoran Wang

NeurIPSW 2024 Leveraging LLM-Generated Structural Prior for Causal Inference with Concurrent Causes Xingjian Zhang, Shixuan Liu, Yixin Wang, Qiaozhu Mei

AISTATS 2024 Multi-Domain Causal Representation Learning via Weak Distributional Invariances Kartik Ahuja, Amin Mansouri, Yixin Wang

AISTATS 2024 Offline Policy Evaluation and Optimization Under Confounding Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari

ICML 2024 On the Identifiability of Switching Dynamical Systems Carles Balsells-Rodas, Yixin Wang, Yingzhen Li

JMLR 2024 Optimization-Based Causal Estimation from Heterogeneous Environments Mingzhang Yin, Yixin Wang, David M. Blei

ICMLW 2024 Tail Extrapolation in Target-Aware Conditional Molecule Generation Weichi Yao, Cameron Gruich, Bryan Goldsmith, Yixin Wang

TMLR 2023 Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness Yixin Wang, Dhanya Sridhar, David Blei

UAI 2023 Bidirectional Attention as a Mixture of Continuous Word Experts Kevin C. Wibisono, Yixin Wang

TMLR 2023 Detecting Incidental Correlation in Multimodal Learning via Latent Variable Modeling Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho

ICML 2023 Interventional Causal Representation Learning Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio

AISTATS 2023 Learning to Optimize with Stochastic Dominance Constraints Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai

NeurIPSW 2023 Multi-Domain Causal Representation Learning via Weak Distributional Invariances Kartik Ahuja, Amin Mansouri, Yixin Wang

NeurIPS 2023 On Learning Necessary and Sufficient Causal Graphs Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song

ICMLW 2023 On the Identifiability of Markov Switching Models Carles Balsells-Rodas, Yixin Wang, Yingzhen Li

NeurIPSW 2023 On the Role of Unstructured Training Data in Transformers' In-Context Learning Capabilities Kevin Christian Wibisono, Yixin Wang

ICMLW 2023 Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph Yixin Wang, Zihao Lin, Haoyu Dong

CVPR 2023 SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency Yang Liu, Yao Zhang, Yixin Wang, Yang Zhang, Jiang Tian, Zhongchao Shi, Jianping Fan, Zhiqiang He

NeurIPS 2022 Anticipating Performativity by Predicting from Predictions Celestine Mendler-Dünner, Frances Ding, Yixin Wang

NeurIPSW 2022 Dynamic Survival Transformers for Causal Inference with Electronic Health Records Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier

NeurIPS 2022 Empirical Gateaux Derivatives for Causal Inference Michael I. Jordan, Yixin Wang, Angela Zhou

TMLR 2022 Identifiable Deep Generative Models via Sparse Decoding Gemma Elyse Moran, Dhanya Sridhar, Yixin Wang, David Blei

NeurIPSW 2022 Interventional Causal Representation Learning Kartik Ahuja, Yixin Wang, Divyat Mahajan, Yoshua Bengio

TMLR 2022 Multi-Source Causal Inference Using Control Variates Under Outcome Selection Bias Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael Jordan

ICMLW 2022 Optimization-Based Causal Estimation from Heterogenous Environments Mingzhang Yin, Yixin Wang, David Blei

CLeaR 2022 Partial Identification with Noisy Covariates: A Robust Optimization Approach Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael Jordan

ICMLW 2022 Representation Learning as Finding Necessary and Sufficient Causes Yixin Wang, Michael Jordan

NeurIPSW 2022 Valid Inference After Causal Discovery Paula Gradu, Tijana Zrnic, Yixin Wang, Michael Jordan

ICML 2021 A Proxy Variable View of Shared Confounding Yixin Wang, David Blei

NeurIPS 2021 Learning Equilibria in Matching Markets from Bandit Feedback Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt

NeurIPS 2021 Posterior Collapse and Latent Variable Non-Identifiability Yixin Wang, David M. Blei, John P. Cunningham

NeurIPS 2020 Point Process Models for Sequence Detection in High-Dimensional Neural Spike Trains Alex Williams, Anthony Degleris, Yixin Wang, Scott Linderman

MLHC 2019 The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak

NeurIPS 2019 Using Embeddings to Correct for Unobserved Confounding in Networks Victor Veitch, Yixin Wang, David Blei

NeurIPS 2019 Variational Bayes Under Model Misspecification Yixin Wang, David Blei

ICML 2018 Black Box FDR Wesley Tansey, Yixin Wang, David Blei, Raul Rabadan

ICML 2017 Evaluating Bayesian Models with Posterior Dispersion Indices Alp Kucukelbir, Yixin Wang, David M. Blei

ICML 2017 Robust Probabilistic Modeling with Bayesian Data Reweighting Yixin Wang, Alp Kucukelbir, David M. Blei