Performance Analysis of an Access Control Strategy in Integrated Networks. - Tülin Atmaca; Guy Pujolle; Fei Sha (Comput. Networks ISDN Syst., 1992)

Calibrating the Query Optimizer Cost Model of IRO-DB, an Object-Oriented Federated Database System. - Georges Gardarin; Fei Sha; Zhao-Hui Tang (VLDB, 1996)

Using Conceptual Modeling and Intelligent Agents to Integrate Semi-structured Documents in Federated Databases. - Georges Gardarin; Fei Sha (Conceptual Modeling, 1997)

Managing Semistructured Data in Object-Relational DBMS. - Fei Sha; Georges Gardarin; Laurent Némirovski (Proc. 15èmes Journées Bases de Données Avancées, BDA, 1999)

XML-based Components for Federating Multiple Heterogeneous Data Sources. - Georges Gardarin; Fei Sha; Tuyet-Tram Dang-Ngoc (ER, 1999)

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 (VLDB, 1999)

A Semi-Structured Data Cartridge for Relational Databases. - Fei Sha; Georges Gardarin; Laurent Némirovski (ICDE, 2000)

Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. - Fei Sha; Lawrence K. Saul; Daniel D. Lee (NIPS, 2002)

Multiplicative Updates for Large Margin Classifiers. - Fei Sha; Lawrence K. Saul; Daniel D. Lee (COLT, 2003)

Statistical signal processing with nonnegativity constraints. - Lawrence K. Saul; Fei Sha; Daniel D. Lee (INTERSPEECH, 2003)

Shallow Parsing with Conditional Random Fields. - Fei Sha; Fernando C. N. Pereira (HLT-NAACL, 2003)

Multiband statistical learning for f0 estimation in speech. - Fei Sha; John Ashley Burgoyne; Lawrence K. Saul (ICASSP (5), 2004)

Learning a kernel matrix for nonlinear dimensionality reduction. - Kilian Q. Weinberger; Fei Sha; Lawrence K. Saul (ICML, 2004)

Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization. - Fei Sha; Lawrence K. Saul (NIPS, 2004)

Analysis and extension of spectral methods for nonlinear dimensionality reduction. - Fei Sha; Lawrence K. Saul (ICML, 2005)

Spectral Methods for Dimensionality Reduction. - Lawrence K. Saul; Kilian Q. Weinberger; Fei Sha; Jihun Ham; Daniel D. Lee (Semi-Supervised Learning, 2006)

Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition. - Fei Sha; Lawrence K. Saul (ICASSP (1), 2006)

Large Margin Hidden Markov Models for Automatic Speech Recognition. - Fei Sha; Lawrence K. Saul (NIPS, 2006)

Graph Laplacian Regularization for Large-Scale Semidefinite Programming. - Kilian Q. Weinberger; Fei Sha; Qihui Zhu; Lawrence K. Saul (NIPS, 2006)

Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models. - Fei Sha; Lawrence K. Saul (ICASSP (4), 2007)

Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. - Andrea Frome; Yoram Singer; Fei Sha; Jitendra Malik (ICCV, 2007)

Regression on manifolds using kernel dimension reduction. - Jens Nilsson 002; Fei Sha; Michael I. Jordan (ICML, 2007)

Multiplicative Updates for L1-Regularized Linear and Logistic Regression. - Fei Sha; Y. Albert Park; Lawrence K. Saul (IDA, 2007)

Multiplicative Updates for Nonnegative Quadratic Programming. - Fei Sha; Yuanqing Lin; Lawrence K. Saul; Daniel D. Lee (Neural Comput., 2007)

DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. - Simon Lacoste-Julien; Fei Sha; Michael I. Jordan (NIPS, 2008)

Large-margin feature adaptation for automatic speech recognition. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (ASRU, 2009)

Matrix updates for perceptron training of continuous density hidden Markov models. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (ICML, 2009)

A fast online algorithm for large margin training of continuous density hidden Markov models. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (INTERSPEECH, 2009)

Robust web extraction: an approach based on a probabilistic tree-edit model. - Nilesh N. Dalvi; Philip Bohannon; Fei Sha (SIGMOD Conference, 2009)

Invited Talk Abstracts. - Yi Ma; Fei Sha; Lawrence Carin; Gilad Lerman; Neil D. Lawrence (AAAI Fall Symposium: Manifold Learning and Its Applications, 2010)

Unsupervised Kernel Dimension Reduction. - Meihong Wang; Fei Sha; Michael I. Jordan (NIPS, 2010)

Active site prediction using evolutionary and structural information. - Sriram Sankararaman; Fei Sha; Jack F. Kirsch; Michael I. Jordan; Kimmen Sjölander (Bioinform., 2010)

Locally Linear Denoising on Image Manifolds. - Dian Gong; Fei Sha; Gérard G. Medioni (AISTATS, 2010)

Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (IEEE J. Sel. Top. Signal Process., 2010)

Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]. - Kilian Q. Weinberger; Fei Sha; Lawrence K. Saul (IEEE Signal Process. Mag., 2010)

Metric learning for reinforcement learning agents. - Matthew E. Taylor; Brian Kulis; Fei Sha (AAMAS, 2011)

Sharing features between objects and their attributes. - Sung Ju Hwang; Fei Sha; Kristen Grauman (CVPR, 2011)

Application of moment invariants in checking the overall dimension of the head cover. - Xu Li; Fei Sha (EMEIT, 2011)

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 G. Karakos; Aren Jansen; et al. (ICASSP, 2011)

Learning with Whom to Share in Multi-task Feature Learning. - Zhuoliang Kang; Kristen Grauman; Fei Sha (ICML, 2011)

A Study of Web Services Performance Prediction: A Client's Perspective. - Leslie Cheung; Leana Golubchik; Fei Sha (MASCOTS, 2011)

Online learning of large margin hidden Markov models for automatic speech recognition. - Lawrence K. Saul; Chih-Chieh Cheng; Fei Sha (MLSLP, 2011)

Learning a Tree of Metrics with Disjoint Visual Features. - Sung Ju Hwang; Kristen Grauman; Fei Sha (NIPS, 2011)

Rapid Feature Learning with Stacked Linear Denoisers - Zhixiang Eddie Xu; Kilian Q. Weinberger; Fei Sha (CoRR, 2011)

Learning Discriminative Metrics via Generative Models and Kernel Learning - Yuan Shi; Yung-Kyun Noh; Fei Sha; Daniel D. Lee (CoRR, 2011)

Information Theoretical Clustering via Semidefinite Programming. - Meihong Wang; Fei Sha (AISTATS, 2011)

Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional Features. - Junping Zhang; Ben Tan; Fei Sha; Li He (IEEE Trans. Intell. Transp. Syst., 2011)

Learning the Kernel Matrix with Low-Rank Multiplicative Shaping. - Tomer Levinboim; Fei Sha (AAAI, 2012)

From sBoW to dCoT marginalized encoders for text representation. - Zhixiang Eddie Xu; Minmin Chen; Kilian Q. Weinberger; Fei Sha (CIKM, 2012)

Geodesic flow kernel for unsupervised domain adaptation. - Boqing Gong; Yuan Shi; Fei Sha; Kristen Grauman (CVPR, 2012)

Marginalized Denoising Autoencoders for Domain Adaptation. - Minmin Chen; Zhixiang Eddie Xu; Kilian Q. Weinberger; Fei Sha (ICML, 2012)

Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation. - Yuan Shi; Fei Sha (ICML, 2012)

Predicting Likability of Speakers with Gaussian Processes. - Dingchao Lu; Fei Sha (INTERSPEECH, 2012)

Semantic Kernel Forests from Multiple Taxonomies. - Sung Ju Hwang; Kristen Grauman; Fei Sha (NIPS, 2012)

Non-linear Metric Learning. - Dor Kedem; Stephen Tyree; Kilian Q. Weinberger; Fei Sha; Gert R. G. Lanckriet (NIPS, 2012)

Cloud-enabled privacy-preserving collaborative learning for mobile sensing. - Bin Liu 004; Yurong Jiang; Fei Sha; Ramesh Govindan (SenSys, 2012)

Deformable Spatial Pyramid Matching for Fast Dense Correspondences. - Jaechul Kim; Ce Liu 001; Fei Sha; Kristen Grauman (CVPR, 2013)

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. - Boqing Gong; Kristen Grauman; Fei Sha (ICML (1), 2013)

Analogy-preserving Semantic Embedding for Visual Object Categorization. - Sung Ju Hwang; Kristen Grauman; Fei Sha (ICML (3), 2013)

Similarity Component Analysis. - Soravit Changpinyo; Kuan Liu; Fei Sha (NIPS, 2013)

Reshaping Visual Datasets for Domain Adaptation. - Boqing Gong; Kristen Grauman; Fei Sha (NIPS, 2013)

An alternative text representation to TF-IDF and Bag-of-Words - Zhixiang Eddie Xu; Minmin Chen; Kilian Q. Weinberger; Fei Sha (CoRR, 2013)

Sparse Compositional Metric Learning. - Yuan Shi; Aurélien Bellet; Fei Sha (AAAI, 2014)

Decorrelating Semantic Visual Attributes by Resisting the Urge to Share. - Dinesh Jayaraman; Fei Sha; Kristen Grauman (CVPR, 2014)

Discriminative non-negative matrix factorization for single-channel speech separation. - Zi Wang; Fei Sha (ICASSP, 2014)

Marginalized Denoising Auto-encoders for Nonlinear Representations. - Minmin Chen; Kilian Q. Weinberger; Fei Sha; Yoshua Bengio (ICML, 2014)

Demystifying Information-Theoretic Clustering. - Greg Ver Steeg; Aram Galstyan; Fei Sha; Simon DeDeo (ICML, 2014)

Two-Stage Metric Learning. - Jun Wang 017; Ke Sun 001; Fei Sha; Stéphane Marchand-Maillet; Alexandros Kalousis (ICML, 2014)

Diverse Sequential Subset Selection for Supervised Video Summarization. - Boqing Gong; Wei-Lun Chao; Kristen Grauman; Fei Sha (NIPS, 2014)

Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning. - Aurélien Bellet; Yingyu Liang; Alireza Bagheri Garakani; Maria-Florina Balcan; Fei Sha (CoRR, 2014)

How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets. - Zhiyun Lu; Avner May; Kuan Liu; Alireza Bagheri Garakani; Dong Guo; Aurélien Bellet; Linxi Fan; Michael Collins 001; Brian Kingsbury; Michael Picheny; Fei Sha (CoRR, 2014)

Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition. - Boqing Gong; Kristen Grauman; Fei Sha (Int. J. Comput. Vis., 2014)

Similarity Learning for High-Dimensional Sparse Data. - Kuan Liu; Aurélien Bellet; Fei Sha (AISTATS, 2015)

Designing a socially assistive robot for personalized number concepts learning in preschool children. - Caitlyn Clabaugh; Gisele Ragusa; Fei Sha; Maja J. Mataric (ICDL-EPIROB, 2015)

Exponential Integration for Hamiltonian Monte Carlo. - Wei-Lun Chao; Justin Solomon 001; Dominik L. Michels; Fei Sha (ICML, 2015)

Active Multi-view Object Recognition and Online Feature Selection. - Christian Potthast; Andreas Breitenmoser; Fei Sha; Gaurav S. Sukhatme (ISRR (2), 2015)

A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning. - Aurélien Bellet; Yingyu Liang; Alireza Bagheri Garakani; Maria-Florina Balcan; Fei Sha (SDM, 2015)

Large-Margin Determinantal Point Processes. - Wei-Lun Chao; Boqing Gong; Kristen Grauman; Fei Sha (UAI, 2015)

Aligning where to see and what to tell: image caption with region-based attention and scene factorization. - Junqi Jin; Kun Fu 002; Runpeng Cui; Fei Sha; Changshui Zhang (CoRR, 2015)

Marginalizing stacked linear denoising autoencoders. - Minmin Chen; Kilian Q. Weinberger; Zhixiang Eddie Xu; Fei Sha (J. Mach. Learn. Res., 2015)

The improvement of page sorting algorithm for music users in Nutch. - Ying Li; Fei Sha; Shujuan Wang; Tao Hu (ICIS, 2016)

Metric Learning for Ordinal Data. - Yuan Shi; Wenzhe Li; Fei Sha (AAAI, 2016)

Synthesized Classifiers for Zero-Shot Learning. - Soravit Changpinyo; Wei-Lun Chao; Boqing Gong; Fei Sha (CVPR, 2016)

Summary Transfer: Exemplar-Based Subset Selection for Video Summarization. - Ke Zhang 028; Wei-Lun Chao; Fei Sha; Kristen Grauman (CVPR, 2016)

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 (ECCV (2), 2016)

Video Summarization with Long Short-Term Memory. - Ke Zhang 028; Wei-Lun Chao; Fei Sha; Kristen Grauman (ECCV (7), 2016)

A comparison between deep neural nets and kernel acoustic models for speech recognition. - Zhiyun Lu; Dong Guo; Alireza Bagheri Garakani; Kuan Liu; Avner May; Aurélien Bellet; Linxi Fan; Michael Collins 001; Brian Kingsbury; Michael Picheny; Fei Sha (ICASSP, 2016)

Supervised Word Mover's Distance. - Gao Huang 001; Chuan Guo 001; Matt J. Kusner; Yu Sun 020; Fei Sha; Kilian Q. Weinberger (NIPS, 2016)

Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task. - Nan Ding 002; Sebastian Goodman; Fei Sha; Radu Soricut (CoRR, 2016)

Recalling Holistic Information for Semantic Segmentation. - Hexiang Hu; Zhiwei Deng; Guang-Tong Zhou; Fei Sha; Greg Mori (CoRR, 2016)

FastMask: Segment Object Multi-scale Candidates in One Shot. - Hexiang Hu; Shiyi Lan; Yuning Jiang 001; Zhimin Cao; Fei Sha (CoRR, 2016)

Active multi-view object recognition: A unifying view on online feature selection and view planning. - Christian Potthast; Andreas Breitenmoser; Fei Sha; Gaurav S. Sukhatme (Robotics Auton. Syst., 2016)

Attention Correctness in Neural Image Captioning. - Chenxi Liu 001; Junhua Mao; Fei Sha; Alan L. Yuille (AAAI, 2017)

FastMask: Segment Multi-scale Object Candidates in One Shot. - Hexiang Hu; Shiyi Lan; Yuning Jiang 001; Zhimin Cao; Fei Sha (CVPR, 2017)

Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning. - Soravit Changpinyo; Wei-Lun Chao; Fei Sha (ICCV, 2017)

An Empirical Study on The Properties of Random Bases for Kernel Methods. - Maximilian Alber; Pieter-Jan Kindermans; Kristof Schütt; Klaus-Robert Müller; Fei Sha (NIPS, 2017)

LabelBank: Revisiting Global Perspectives for Semantic Segmentation. - Hexiang Hu; Zhiwei Deng; Guang-Tong Zhou; Fei Sha; Greg Mori (CoRR, 2017)

Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts. - Kun Fu 002; Junqi Jin; Runpeng Cui; Fei Sha; Changshui Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation. - Boqing Gong; Kristen Grauman; Fei Sha (Domain Adaptation in Computer Vision Applications, 2017)

Robust Active Label Correction. - Jan Kremer; Fei Sha; Christian Igel (AISTATS, 2018)

Multi-Task Learning for Sequence Tagging: An Empirical Study. - Soravit Changpinyo; Hexiang Hu; Fei Sha (COLING, 2018)

Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner. - Yury Zemlyanskiy; Fei Sha (CoNLL, 2018)

Cross-Dataset Adaptation for Visual Question Answering. - Wei-Lun Chao; Hexiang Hu; Fei Sha (CVPR, 2018)

Learning Answer Embeddings for Visual Question Answering. - Hexiang Hu; Wei-Lun Chao; Fei Sha (CVPR, 2018)

Retrospective Encoders for Video Summarization. - Ke Zhang 028; Kristen Grauman; Fei Sha (ECCV (8), 2018)

Cross-Modal and Hierarchical Modeling of Video and Text. - Bowen Zhang 002; Hexiang Hu; Fei Sha (ECCV (13), 2018)

A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images. - Melissa Ailem; Bowen Zhang 002; Aurélien Bellet; Pascal Denis; Fei Sha (EMNLP, 2018)

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; Tancrède Lepoint; Benjamin M. Marlin; Klara Nahrstedt; David M. Nicol; et al. (ICDCS, 2018)

Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets. - Wei-Lun Chao; Hexiang Hu; Fei Sha (NAACL-HLT, 2018)

Synthesize Policies for Transfer and Adaptation across Tasks and Environments. - Hexiang Hu; Liyu Chen; Boqing Gong; Fei Sha (NeurIPS, 2018)

Toward an Internet of Battlefield Things: A Resilience Perspective. - Tarek F. Abdelzaher; Nora Ayanian; Tamer Basar; Suhas N. Diggavi; Jana Diesner; Deepak Ganesan; Ramesh Govindan; Susmit Jha; Tancrède Lepoint; Benjamin M. Marlin; Klara Nahrstedt; David M. Nicol; et al. (Computer, 2018)

Learning Embedding Adaptation for Few-Shot Learning. - Han-Jia Ye; Hexiang Hu; De-Chuan Zhan; Fei Sha (CoRR, 2018)

A Bayesian Theory of Mind Approach to Nonverbal Communication. - Jin Joo Lee; Fei Sha; Cynthia Breazeal (HRI, 2019)

Actor-Attention-Critic for Multi-Agent Reinforcement Learning. - Shariq Iqbal; Fei Sha (ICML, 2019)

Hyper-parameter Tuning under a Budget Constraint. - Zhiyun Lu; Liyu Chen; Chao-Kai Chiang; Fei Sha (IJCAI, 2019)

Hyper-parameter Tuning under a Budget Constraint. - Zhiyun Lu; Chao-Kai Chiang; Fei Sha (CoRR, 2019)

Synthesized Policies for Transfer and Adaptation across Tasks and Environments. - Hexiang Hu; Liyu Chen; Boqing Gong; Fei Sha (CoRR, 2019)

Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning. - Shariq Iqbal; Fei Sha (CoRR, 2019)

Learning Classifier Synthesis for Generalized Few-Shot Learning. - Han-Jia Ye; Hexiang Hu; De-Chuan Zhan; Fei Sha (CoRR, 2019)

Neural Theorem Provers Do Not Learn Rules Without Exploration. - Michiel de Jong; Fei Sha (CoRR, 2019)

Topic Augmented Generator for Abstractive Summarization. - Melissa Ailem; Bowen Zhang 002; Fei Sha (CoRR, 2019)

Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning. - Sébastien M. R. Arnold; Shariq Iqbal; Fei Sha (CoRR, 2019)

Kernel Approximation Methods for Speech Recognition. - Avner May; Alireza Bagheri Garakani; Zhiyun Lu; Dong Guo; Kuan Liu; Aurélien Bellet; Linxi Fan; Michael Collins 001; Daniel Hsu 001; Brian Kingsbury; Michael Picheny; Fei Sha (J. Mach. Learn. Res., 2019)

BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps. - Wang Zhu 001; Hexiang Hu; Jiacheng Chen; Zhiwei Deng; Vihan Jain; Eugene Ie; Fei Sha (ACL, 2020)

Amortized Inference of Variational Bounds for Learning Noisy-OR. - Yiming Yan; Melissa Ailem; Fei Sha (AISTATS, 2020)

Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions. - Han-Jia Ye; Hexiang Hu; De-Chuan Zhan; Fei Sha (CVPR, 2020)

Learning to Represent Image and Text with Denotation Graph. - Bowen Zhang 002; Hexiang Hu; Vihan Jain; Eugene Ie; Fei Sha (EMNLP (1), 2020)

Visual Storytelling via Predicting Anchor Word Embeddings in the Stories. - Bowen Zhang 002; Hexiang Hu; Fei Sha (CoRR, 2020)

AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Böhmer; Shimon Whiteson; Fei Sha (CoRR, 2020)

Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation. - Zhiyun Lu; Eugene Ie; Fei Sha (CoRR, 2020)

AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization. - Sayali Kulkarni; Sheide Chammas; Wan Zhu; Fei Sha; Eugene Ie (CoRR, 2020)

A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus. - Bowen Zhang 002; Hexiang Hu; Joonseok Lee; Ming Zhao; Sheide Chammas; Vihan Jain; Eugene Ie; Fei Sha (CoRR, 2020)

Classifier and Exemplar Synthesis for Zero-Shot Learning. - Soravit Changpinyo; Wei-Lun Chao; Boqing Gong; Fei Sha (Int. J. Comput. Vis., 2020)

When MAML Can Adapt Fast and How to Assist When It Cannot. - Sébastien M. R. Arnold; Shariq Iqbal; Fei Sha (AISTATS, 2021)

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 (EACL, 2021)

Visually Grounded Concept Composition. - Bowen Zhang 002; Hexiang Hu; Linlu Qiu; Peter Shaw 004; Fei Sha (EMNLP (Findings), 2021)

Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? - Linlu Qiu; Hexiang Hu; Bowen Zhang 002; Peter Shaw 004; Fei Sha (EMNLP (1), 2021)

CoMSum and SIBERT: A Dataset and Neural Model for Query-Based Multi-document Summarization. - Sayali Kulkarni; Sheide Chammas; Wan Zhu; Fei Sha; Eugene Ie (ICDAR (2), 2021)

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson; Fei Sha (ICML, 2021)

ReadTwice: Reading Very Large Documents with Memories. - Yury Zemlyanskiy; Joshua Ainslie; Michiel de Jong; Philip Pham; Ilya Eckstein; Fei Sha (NAACL-HLT, 2021)

Embedding Adaptation is Still Needed for Few-Shot Learning. - Sébastien M. R. Arnold; Fei Sha (CoRR, 2021)

HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks. - Filipe de Avila Belbute-Peres; Yi-Fan Chen; Fei Sha (CoRR, 2021)

Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks. - Wang Zhu 001; Peter Shaw 004; Tal Linzen; Fei Sha (CoRR, 2021)

Co-training Transformer with Videos and Images Improves Action Recognition. - Bowen Zhang 002; Jiahui Yu; Christopher Fifty; Wei Han; Andrew M. Dai; Ruoming Pang; Fei Sha (CoRR, 2021)

Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. - Sébastien M. R. Arnold; Pierre L'Ecuyer; Liyu Chen; Yi-Fan Chen; Fei Sha (AISTATS, 2022)

Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing. - Yury Zemlyanskiy; Michiel de Jong; Joshua Ainslie; Panupong Pasupat; Peter Shaw 004; Linlu Qiu; Sumit Sanghai; Fei Sha (COLING, 2022)

Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing. - Linlu Qiu; Peter Shaw 004; Panupong Pasupat; Tianze Shi; Jonathan Herzig; Emily Pitler; Fei Sha; Kristina Toutanova (EMNLP, 2022)

Possibility Before Utility: Learning And Using Hierarchical Affordances. - Robby Costales; Shariq Iqbal; Fei Sha (ICLR, 2022)

Mention Memory: incorporating textual knowledge into Transformers through entity mention attention. - Michiel de Jong; Yury Zemlyanskiy; Nicholas FitzGerald; Fei Sha; William W. Cohen (ICLR, 2022)

Quickest Detection of the Change of Community via Stochastic Block Models. - Fei Sha; Ruizhi Zhang 001 (ISIT, 2022)

Improving Compositional Generalization with Latent Structure and Data Augmentation. - Linlu Qiu; Peter Shaw 004; Panupong Pasupat; Pawel Krzysztof Nowak; Tal Linzen; Fei Sha; Kristina Toutanova (NAACL-HLT, 2022)

ALMA: Hierarchical Learning for Composite Multi-Agent Tasks. - Shariq Iqbal; Robby Costales; Fei Sha (NeurIPS, 2022)

Adversarially robust subspace learning in the spiked covariance model. - Fei Sha; Ruizhi Zhang 001 (Stat. Anal. Data Min., 2022)

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 (ACL (Findings), 2023)

Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories. - Thomas Mensink; Jasper R. R. Uijlings; Lluís Castrejón; Arushi Goel; Felipe Cadar; Howard Zhou; Fei Sha; André Araújo 001; Vittorio Ferrari (ICCV, 2023)

Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. - Zhong Yi Wan; Leonardo Zepeda-Núñez; Anudhyan Boral; Fei Sha (ICLR, 2023)

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-Núñez (ICML, 2023)

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 (ICML, 2023)

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations. - Anudhyan Boral; Zhong Yi Wan; Leonardo Zepeda-Núñez; James Lottes; Qing Wang 032; Yi-Fan Chen; John Anderson; Fei Sha (NeurIPS, 2023)

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models. - Zhong Yi Wan; Ricardo Baptista; Anudhyan Boral; Yi-Fan Chen; John Anderson; Fei Sha; Leonardo Zepeda-Núñez (NeurIPS, 2023)

Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL. - Sébastien M. R. Arnold; Fei Sha (CoRR, 2023)

V2Meow: Meowing to the Visual Beat via 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 (CoRR, 2023)

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations. - Anudhyan Boral; Zhong Yi Wan; Leonardo Zepeda-Núñez; James Lottes; Qing Wang 032; Yi-Fan Chen; John Robert Anderson; Fei Sha (CoRR, 2023)

SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models. - Lizao Li; Rob Carver; Ignacio Lopez-Gomez; Fei Sha; John Anderson (CoRR, 2023)

WeatherBench 2: A benchmark for the next generation of data-driven global weather models. - Stephan Rasp 001; Stephan Hoyer; Alexander Merose; Ian Langmore; Peter W. Battaglia; Tyler Russell; Alvaro Sanchez-Gonzalez; Vivian Yang; Rob Carver; Shreya Agrawal; Matthew Chantry; Zied Ben Bouallegue; et al. (CoRR, 2023)

The Impact of Depth and Width on Transformer Language Model Generalization. - Jackson Petty; Sjoerd van Steenkiste; Ishita Dasgupta 001; Fei Sha; Dan Garrette; Tal Linzen (CoRR, 2023)

Drinking From a Firehose: Continual Learning With Web-Scale Natural Language. - Hexiang Hu; Ozan Sener; Fei Sha; Vladlen Koltun (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

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 (AAAI, 2024)

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-Núñez (ICML, 2024)

A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models. - Tiwalayo Eisape; Michael Henry Tessler; Ishita Dasgupta 001; Fei Sha; Sjoerd van Steenkiste; Tal Linzen (NAACL-HLT, 2024)

The Impact of Depth on Compositional Generalization in Transformer Language Models. - Jackson Petty; Sjoerd van Steenkiste; Ishita Dasgupta 001; Fei Sha; Dan Garrette; Tal Linzen (NAACL-HLT, 2024)

A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data. - Benedikt Barthel Sorensen; Leonardo Zepeda-Núñez; Ignacio Lopez-Gomez; Zhong Yi Wan; Rob Carver; Fei Sha; Themistoklis P. Sapsis (CoRR, 2024)

Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme. - Shantanu Shahane; Sheide Chammas; Deniz A. Bezgin; Aaron B. Buhendwa; Steffen J. Schmidt; Nikolaus A. Adams; Spencer H. Bryngelson; Yi-Fan Chen; Qing Wang 032; Fei Sha; Leonardo Zepeda-Núñez (CoRR, 2024)

Generative AI for fast and accurate Statistical Computation of Fluids. - Roberto Molinaro; Samuel Lanthaler; Bogdan Raonic; Tobias Rohner; Victor Armegioiu; Zhong Yi Wan; Fei Sha; Siddhartha Mishra; Leonardo Zepeda-Núñez (CoRR, 2024)

Dynamical-generative downscaling of climate model ensembles. - Ignacio Lopez-Gomez; Zhong Yi Wan; Leonardo Zepeda-Núñez; Tapio Schneider; John R. Anderson; Fei Sha (CoRR, 2024)

Statistical Downscaling via High-Dimensional Distribution Matching with Generative Models. - Zhong Yi Wan; Ignacio Lopez-Gomez; Rob Carver; Tapio Schneider; John R. Anderson; Fei Sha; Leonardo Zepeda-Núñez (CoRR, 2024)

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 (ICASSP, 2025)

Graders should cheat: privileged information enables expert-level automated evaluations. - Jin Peng Zhou; Sébastien M. R. Arnold; Nan Ding 002; Kilian Q. Weinberger; Nan Hua; Fei Sha (CoRR, 2025)

Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models. - Linlu Qiu; Fei Sha; Kelsey R. Allen; Yoon Kim; Tal Linzen; Sjoerd van Steenkiste (CoRR, 2025)

Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025. - Nitya Thakkar; Mert Yüksekgönül; Jake Silberg; Animesh Garg; Nanyun Peng 001; Fei Sha; Rose Yu; Carl Vondrick; James Zou 001 (CoRR, 2025)