| 2025 | EMNLP | Graders Should Cheat: Privileged Information Enables Expert-Level Automated Evaluations. | Jin Peng Zhou, Sbastien M. R. Arnold, Nan Ding, Kilian Q. Weinberger, Nan Hua, Fei Sha |
| 2025 | ICASSP | Diff4Steer: Steerable Diffusion Prior for Generative Music Retrieval with Semantic Guidance. | Xuchan Bao, Judith Yue Li, Zhong Yi Wan, Kun Su, Timo I. Denk, Joonseok Lee, Dima Kuzmin, Fei Sha |
| 2024 | AAAI | V2Meow: Meowing to the Visual Beat via Video-to-Music Generation. | Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, Timo I. Denk |
| 2024 | ICML | DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. | Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Nez |
| 2024 | NAACL | A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models. | Tiwalayo Eisape, Michael Henry Tessler, Ishita Dasgupta, Fei Sha, Sjoerd van Steenkiste, Tal Linzen |
| 2024 | NAACL | The Impact of Depth on Compositional Generalization in Transformer Language Models. | Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta, Fei Sha, Dan Garrette, Tal Linzen |
| 2023 | ACL | FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference. | Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, Nicholas FitzGerald, Sumit Sanghai, Fei Sha, William W. Cohen |
| 2023 | ICCV | Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories. | Thomas Mensink, Jasper R. R. Uijlings, Llus Castrejn, Arushi Goel, Felipe Cadar, Howard Zhou, Fei Sha, Andr Arajo, Vittorio Ferrari |
| 2023 | ICLR | Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. | Zhong Yi Wan, Leonardo Zepeda-Nez, Anudhyan Boral, Fei Sha |
| 2023 | ICML | User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. | Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Nez |
| 2023 | ICML | Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute. | Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Joshua Ainslie, Sumit Sanghai, Fei Sha, William W. Cohen |
| 2022 | AISTATS | Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. | Sbastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha |
| 2022 | COLING | Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing. | Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha |
| 2022 | EMNLP | Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing. | Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova |
| 2022 | ICLR | Possibility Before Utility: Learning And Using Hierarchical Affordances. | Robby Costales, Shariq Iqbal, Fei Sha |
| 2022 | ICLR | Mention Memory: incorporating textual knowledge into Transformers through entity mention attention. | Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Fei Sha, William W. Cohen |
| 2022 | ISIT | Quickest Detection of the Change of Community via Stochastic Block Models. | Fei Sha, Ruizhi Zhang |
| 2022 | NAACL | Improving Compositional Generalization with Latent Structure and Data Augmentation. | Linlu Qiu, Peter Shaw, Panupong Pasupat, Pawel Krzysztof Nowak, Tal Linzen, Fei Sha, Kristina Toutanova |
| 2021 | AISTATS | When MAML Can Adapt Fast and How to Assist When It Cannot. | Sbastien M. R. Arnold, Shariq Iqbal, Fei Sha |
| 2021 | EACL | DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections. | Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein |
| 2021 | EMNLP | Visually Grounded Concept Composition. | Bowen Zhang, Hexiang Hu, Linlu Qiu, Peter Shaw, Fei Sha |
| 2021 | EMNLP | Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? | Linlu Qiu, Hexiang Hu, Bowen Zhang, Peter Shaw, Fei Sha |
| 2021 | ICDAR | CoMSum and SIBERT: A Dataset and Neural Model for Query-Based Multi-document Summarization. | Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, Eugene Ie |
| 2021 | ICML | Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. | Shariq Iqbal, Christian A. Schrder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha |
| 2021 | NAACL | ReadTwice: Reading Very Large Documents with Memories. | Yury Zemlyanskiy, Joshua Ainslie, Michiel de Jong, Philip Pham, Ilya Eckstein, Fei Sha |
| 2020 | ACL | BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps. | Wang Zhu, Hexiang Hu, Jiacheng Chen, Zhiwei Deng, Vihan Jain, Eugene Ie, Fei Sha |
| 2020 | AISTATS | Amortized Inference of Variational Bounds for Learning Noisy-OR. | Yiming Yan, Melissa Ailem, Fei Sha |
| 2020 | CVPR | Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions. | Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha |
| 2020 | EMNLP | Learning to Represent Image and Text with Denotation Graph. | Bowen Zhang, Hexiang Hu, Vihan Jain, Eugene Ie, Fei Sha |
| 2019 | HRI | A Bayesian Theory of Mind Approach to Nonverbal Communication. | Jin Joo Lee, Fei Sha, Cynthia Breazeal |
| 2019 | ICML | Actor-Attention-Critic for Multi-Agent Reinforcement Learning. | Shariq Iqbal, Fei Sha |
| 2019 | IJCAI | Hyper-parameter Tuning under a Budget Constraint. | Zhiyun Lu, Liyu Chen, Chao-Kai Chiang, Fei Sha |
| 2018 | AISTATS | Robust Active Label Correction. | Jan Kremer, Fei Sha, Christian Igel |
| 2018 | COLING | Multi-Task Learning for Sequence Tagging: An Empirical Study. | Soravit Changpinyo, Hexiang Hu, Fei Sha |
| 2018 | CoNLL | Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner. | Yury Zemlyanskiy, Fei Sha |
| 2018 | CVPR | Cross-Dataset Adaptation for Visual Question Answering. | Wei-Lun Chao, Hexiang Hu, Fei Sha |
| 2018 | CVPR | Learning Answer Embeddings for Visual Question Answering. | Hexiang Hu, Wei-Lun Chao, Fei Sha |
| 2018 | ECCV | Retrospective Encoders for Video Summarization. | Ke Zhang, Kristen Grauman, Fei Sha |
| 2018 | ECCV | Cross-Modal and Hierarchical Modeling of Video and Text. | Bowen Zhang, Hexiang Hu, Fei Sha |
| 2018 | EMNLP | A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images. | Melissa Ailem, Bowen Zhang, Aurlien Bellet, Pascal Denis, Fei Sha |
| 2018 | ICDCS | Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT. | Tarek F. Abdelzaher, Nora Ayanian, Tamer Basar, Suhas N. Diggavi, Jana Diesner, Deepak Ganesan, Ramesh Govindan, Susmit Jha, Tancrde Lepoint, Benjamin M. Marlin, Klara Nahrstedt, David M. Nicol, Raj Rajkumar, Stephen Russell, Sanjit A. Seshia, Fei Sha, Prashant J. Shenoy, Mani B. Srivastava, Gaurav S. Sukhatme, Ananthram Swami, Paulo Tabuada, Don Towsley, Nitin H. Vaidya, Venugopal V. Veeravalli |
| 2018 | NAACL | Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets. | Wei-Lun Chao, Hexiang Hu, Fei Sha |
| 2017 | AAAI | Attention Correctness in Neural Image Captioning. | Chenxi Liu, Junhua Mao, Fei Sha, Alan L. Yuille |
| 2017 | CVPR | FastMask: Segment Multi-scale Object Candidates in One Shot. | Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao, Fei Sha |
| 2017 | ICCV | Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning. | Soravit Changpinyo, Wei-Lun Chao, Fei Sha |
| 2016 | AAAI | Metric Learning for Ordinal Data. | Yuan Shi, Wenzhe Li, Fei Sha |
| 2016 | CVPR | Synthesized Classifiers for Zero-Shot Learning. | Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha |
| 2016 | CVPR | Summary Transfer: Exemplar-Based Subset Selection for Video Summarization. | Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman |
| 2016 | ECCV | An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild. | Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, Fei Sha |
| 2016 | ECCV | Video Summarization with Long Short-Term Memory. | Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman |
| 2016 | ICASSP | A comparison between deep neural nets and kernel acoustic models for speech recognition. | Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurlien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha |
| 2015 | AISTATS | Similarity Learning for High-Dimensional Sparse Data. | Kuan Liu, Aurlien Bellet, Fei Sha |
| 2015 | ICML | Exponential Integration for Hamiltonian Monte Carlo. | Wei-Lun Chao, Justin Solomon, Dominik L. Michels, Fei Sha |
| 2015 | ISRR | Active Multi-view Object Recognition and Online Feature Selection. | Christian Potthast, Andreas Breitenmoser, Fei Sha, Gaurav S. Sukhatme |
| 2015 | UAI | Large-Margin Determinantal Point Processes. | Wei-Lun Chao, Boqing Gong, Kristen Grauman, Fei Sha |
| 2015 | SDM | A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning. | Aurlien Bellet, Yingyu Liang, Alireza Bagheri Garakani, Maria-Florina Balcan, Fei Sha |
| 2014 | AAAI | Sparse Compositional Metric Learning. | Yuan Shi, Aurlien Bellet, Fei Sha |
| 2014 | CVPR | Decorrelating Semantic Visual Attributes by Resisting the Urge to Share. | Dinesh Jayaraman, Fei Sha, Kristen Grauman |
| 2014 | ICASSP | Discriminative non-negative matrix factorization for single-channel speech separation. | Zi Wang, Fei Sha |
| 2014 | ICML | Marginalized Denoising Auto-encoders for Nonlinear Representations. | Minmin Chen, Kilian Q. Weinberger, Fei Sha, Yoshua Bengio |
| 2014 | ICML | Demystifying Information-Theoretic Clustering. | Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo |
| 2014 | ICML | Two-Stage Metric Learning. | Jun Wang, Ke Sun, Fei Sha, Stphane Marchand-Maillet, Alexandros Kalousis |
| 2013 | CVPR | Deformable Spatial Pyramid Matching for Fast Dense Correspondences. | Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman |
| 2013 | ICML | Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. | Boqing Gong, Kristen Grauman, Fei Sha |
| 2013 | ICML | Analogy-preserving Semantic Embedding for Visual Object Categorization. | Sung Ju Hwang, Kristen Grauman, Fei Sha |
| 2012 | AAAI | Learning the Kernel Matrix with Low-Rank Multiplicative Shaping. | Tomer Levinboim, Fei Sha |
| 2012 | CIKM | From sBoW to dCoT marginalized encoders for text representation. | Zhixiang Eddie Xu, Minmin Chen, Kilian Q. Weinberger, Fei Sha |
| 2012 | CVPR | Geodesic flow kernel for unsupervised domain adaptation. | Boqing Gong, Yuan Shi, Fei Sha, Kristen Grauman |
| 2012 | ICML | Marginalized Denoising Autoencoders for Domain Adaptation. | Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha |
| 2012 | ICML | Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation. | Yuan Shi, Fei Sha |
| 2012 | Interspeech | Predicting Likability of Speakers with Gaussian Processes. | Dingchao Lu, Fei Sha |
| 2012 | SENSYS | Cloud-enabled privacy-preserving collaborative learning for mobile sensing. | Bin Liu, Yurong Jiang, Fei Sha, Ramesh Govindan |
| 2011 | CVPR | Sharing features between objects and their attributes. | Sung Ju Hwang, Fei Sha, Kristen Grauman |
| 2011 | ICASSP | Speech recognitionwith segmental conditional random fields: A summary of the JHU CLSP 2010 Summer Workshop. | Geoffrey Zweig, Patrick Nguyen, Dirk Van Compernolle, Kris Demuynck, Les E. Atlas, Pascal Clark, Gregory Sell, Meihong Wang, Fei Sha, Hynek Hermansky, Damianos Karakos, Aren Jansen, Samuel Thomas, Sivaram G. S. V. S., Samuel R. Bowman, Justine T. Kao |
| 2011 | ICML | Learning with Whom to Share in Multi-task Feature Learning. | Zhuoliang Kang, Kristen Grauman, Fei Sha |
| 2011 | MASCOTS | A Study of Web Services Performance Prediction: A Client's Perspective. | Leslie Cheung, Leana Golubchik, Fei Sha |
| 2009 | ASRU | Large-margin feature adaptation for automatic speech recognition. | Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul |
| 2009 | ICML | Matrix updates for perceptron training of continuous density hidden Markov models. | Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul |
| 2009 | Interspeech | A fast online algorithm for large margin training of continuous density hidden Markov models. | Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul |
| 2009 | SIGMOD | Robust web extraction: an approach based on a probabilistic tree-edit model. | Nilesh N. Dalvi, Philip Bohannon, Fei Sha |
| 2007 | ICASSP | Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models. | Fei Sha, Lawrence K. Saul |
| 2007 | ICCV | Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. | Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik |
| 2007 | ICML | Regression on manifolds using kernel dimension reduction. | Jens Nilsson, Fei Sha, Michael I. Jordan |
| 2007 | IDA | Multiplicative Updates for | Fei Sha, Y. Albert Park, Lawrence K. Saul |
| 2006 | ICASSP | Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition. | Fei Sha, Lawrence K. Saul |
| 2005 | ICML | Analysis and extension of spectral methods for nonlinear dimensionality reduction. | Fei Sha, Lawrence K. Saul |
| 2004 | ICASSP | Multiband statistical learning for f | Fei Sha, John Ashley Burgoyne, Lawrence K. Saul |
| 2004 | ICML | Learning a kernel matrix for nonlinear dimensionality reduction. | Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul |
| 2003 | COLT | Multiplicative Updates for Large Margin Classifiers. | Fei Sha, Lawrence K. Saul, Daniel D. Lee |
| 2003 | Interspeech | Statistical signal processing with nonnegativity constraints. | Lawrence K. Saul, Fei Sha, Daniel D. Lee |
| 2003 | NAACL | Shallow Parsing with Conditional Random Fields. | Fei Sha, Fernando C. N. Pereira |
| 2000 | ICDE | A Semi-Structured Data Cartridge for Relational Databases. | Fei Sha, Georges Gardarin, Laurent Nmirovski |
| 1999 | ER | XML-based Components for Federating Multiple Heterogeneous Data Sources. | Georges Gardarin, Fei Sha, Tuyet-Tram Dang-Ngoc |
| 1999 | VLDB | Miro Web: Integrating Multiple Data Sources through Semistructured Data Types. | Luc Bouganim, Tatiana Chan-Sine-Ying, Tuyet-Tram Dang-Ngoc, Jean-Luc Darroux, Georges Gardarin, Fei Sha |
| 1997 | ER | Using Conceptual Modeling and Intelligent Agents to Integrate Semi-structured Documents in Federated Databases. | Georges Gardarin, Fei Sha |
| 1996 | VLDB | Calibrating the Query Optimizer Cost Model of IRO-DB, an Object-Oriented Federated Database System. | Georges Gardarin, Fei Sha, Zhao-Hui Tang |