Sha, Fei

72 publications

NeurIPSW 2024 Can Language Models Perform Implicit Bayesian Inference over User Preference States? Linlu Qiu, Fei Sha, Kelsey R Allen, Yoon Kim, Tal Linzen, Sjoerd van Steenkiste

ICML 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

AAAI 2024 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

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

ICCV 2023 Encyclopedic VQA: Visual Questions About Detailed Properties of Fine-Grained Categories Thomas Mensink, Jasper Uijlings, Lluis Castrejon, Arushi Goel, Felipe Cadar, Howard Zhou, Fei Sha, André Araujo, Vittorio Ferrari

ICLR 2023 Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics for Advection-Dominated Systems Zhong Yi Wan, Leonardo Zepeda-Nunez, Anudhyan Boral, Fei Sha

NeurIPS 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, Yi-Fan Chen, John Anderson, Fei Sha

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 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-Nunez

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

NeurIPS 2022 ALMA: Hierarchical Learning for Composite Multi-Agent Tasks Shariq Iqbal, Robby Costales, 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 Possibility Before Utility: Learning and Using Hierarchical Affordances Robby Costales, Shariq Iqbal, Fei Sha

AISTATS 2021 When MAML Can Adapt Fast and How to Assist When It Cannot Sébastien Arnold, Shariq Iqbal, Fei Sha

NeurIPSW 2021 HyperPINN: Learning Parameterized Differential Equations with Physics-Informed Hypernetworks Filipe de Avila Belbute-Peres, Yi-fan Chen, Fei Sha

ICML 2021 Randomized Entity-Wise Factorization for Multi-Agent Reinforcement Learning Shariq Iqbal, Christian A Schroeder De Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha

AISTATS 2020 Amortized Inference of Variational Bounds for Learning Noisy-or Yiming Yan, Melissa Ailem, Fei Sha

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

IJCAI 2019 Hyper-Parameter Tuning Under a Budget Constraint Zhiyun Lu, Liyu Chen, Chao-Kai Chiang, Fei Sha

JMLR 2019 Kernel Approximation Methods for Speech Recognition Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha

ECCV 2018 Cross-Modal and Hierarchical Modeling of Video and Text Bowen Zhang, Hexiang Hu, Fei Sha

ECCV 2018 Retrospective Encoders for Video Summarization Ke Zhang, Kristen Grauman, Fei Sha

AISTATS 2018 Robust Active Label Correction Jan Kremer, Fei Sha, Christian Igel

NeurIPS 2018 Synthesized Policies for Transfer and Adaptation Across Tasks and Environments Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha

NeurIPS 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

AAAI 2017 Attention Correctness in Neural Image Captioning Chenxi Liu, Junhua Mao, Fei Sha, Alan L. Yuille

CVPR 2017 FastMask: Segment Multi-Scale Object Candidates in One Shot Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao, Fei Sha

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

ECCV 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

AAAI 2016 Metric Learning for Ordinal Data Yuan Shi, Wenzhe Li, Fei Sha

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

NeurIPS 2016 Supervised Word Mover's Distance Gao Huang, Chuan Guo, Matt J Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger

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

ECCV 2016 Video Summarization with Long Short-Term Memory Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman

ICML 2015 Exponential Integration for Hamiltonian Monte Carlo Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei Sha

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

JMLR 2015 Marginalizing Stacked Linear Denoising Autoencoders Minmin Chen, Kilian Q. Weinberger, Zhixiang Xu, Fei Sha

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

CVPR 2014 Decorrelating Semantic Visual Attributes by Resisting the Urge to Share Dinesh Jayaraman, Fei Sha, Kristen Grauman

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

NeurIPS 2014 Diverse Sequential Subset Selection for Supervised Video Summarization Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha

ICML 2014 Marginalized Denoising Auto-Encoders for Nonlinear Representations Minmin Chen, Kilian Weinberger, Fei Sha, Yoshua Bengio

AAAI 2014 Sparse Compositional Metric Learning Yuan Shi, Aurélien Bellet, Fei Sha

ICML 2014 Two-Stage Metric Learning Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis

ICML 2013 Analogy-Preserving Semantic Embedding for Visual Object Categorization Sung Ju Hwang, Kristen Grauman, Fei Sha

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

CVPR 2013 Deformable Spatial Pyramid Matching for Fast Dense Correspondences Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman

NeurIPS 2013 Reshaping Visual Datasets for Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha

NeurIPS 2013 Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha

CVPR 2012 Geodesic Flow Kernel for Unsupervised Domain Adaptation Boqing Gong, Yuan Shi, Fei Sha, Kristen Grauman

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

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

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

NeurIPS 2012 Non-Linear Metric Learning Dor Kedem, Stephen Tyree, Fei Sha, Gert R. Lanckriet, Kilian Q. Weinberger

NeurIPS 2012 Semantic Kernel Forests from Multiple Taxonomies Sung Ju Hwang, Kristen Grauman, Fei Sha

AISTATS 2011 Information Theoretical Clustering via Semidefinite Programming Meihong Wang, Fei Sha

NeurIPS 2011 Learning a Tree of Metrics with Disjoint Visual Features Kristen Grauman, Fei Sha, Sung Ju Hwang

ICML 2011 Learning with Whom to Share in Multi-Task Feature Learning Zhuoliang Kang, Kristen Grauman, Fei Sha

CVPR 2011 Sharing Features Between Objects and Their Attributes Sung Ju Hwang, Fei Sha, Kristen Grauman

AISTATS 2010 Locally Linear Denoising on Image Manifolds Dian Gong, Fei Sha, Gérard Medioni

NeurIPS 2010 Unsupervised Kernel Dimension Reduction Meihong Wang, Fei Sha, Michael I. Jordan

ICML 2009 Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul

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

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

ICML 2007 Regression on Manifolds Using Kernel Dimension Reduction Jens Nilsson, Fei Sha, Michael I. Jordan

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

NeurIPS 2006 Large Margin Hidden Markov Models for Automatic Speech Recognition Fei Sha, Lawrence K. Saul

ICML 2005 Analysis and Extension of Spectral Methods for Nonlinear Dimensionality Reduction Fei Sha, Lawrence K. Saul

ICML 2004 Learning a Kernel Matrix for Nonlinear Dimensionality Reduction Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul

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

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

NeurIPS 2002 Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines Fei Sha, Lawrence K. Saul, Daniel D. Lee