Anandkumar, Animashree

54 publications

WACV 2024 Differentially Private Video Activity Recognition Zelun Luo, Yuliang Zou, Yijin Yang, Zane Durante, De-An Huang, Zhiding Yu, Chaowei Xiao, Li Fei-Fei, Animashree Anandkumar

NeurIPS 2023 ClimSim: A Large Multi-Scale Dataset for Hybrid Physics-ML Climate Emulation Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce Harrop, Benjamin Hillman, Andrea Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David Randall, Sara Shamekh, Mark Taylor, Nathan Urban, Janni Yuval, Guang Zhang, Mike Pritchard

AISTATS 2023 Distributionally Robust Policy Gradient for Offline Contextual Bandits Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Animashree Anandkumar

NeurIPS 2023 Geometry-Informed Neural Operator for Large-Scale 3D PDEs Zongyi Li, Nikola Kovachki, Chris Choy, Boyi Li, Jean Kossaifi, Shourya Otta, Mohammad Amin Nabian, Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, Animashree Anandkumar

NeurIPS 2023 LeanDojo: Theorem Proving with Retrieval-Augmented Language Models Kaiyu Yang, Aidan Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan J Prenger, Animashree Anandkumar

IJCAI 2023 Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach Haoxuan Wang, Zhiding Yu, Yisong Yue, Animashree Anandkumar, Anqi Liu, Junchi Yan

NeurIPS 2023 Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhi-Ming Ma, Omar Yaghi, Animashree Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang

AISTATS 2022 Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Animashree Anandkumar

ICML 2022 Diffusion Models for Adversarial Purification Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Animashree Anandkumar

ICML 2022 Langevin Monte Carlo for Contextual Bandits Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar

COLT 2022 Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Animashree Anandkumar, Babak Hassibi

ICML 2022 Understanding the Robustness in Vision Transformers Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, Jose M. Alvarez

AISTATS 2021 Active Learning Under Label Shift Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue

AAAI 2021 Deep Bayesian Quadrature Policy Optimization Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Animashree Anandkumar, Yisong Yue

ICML 2021 Image-Level or Object-Level? a Tale of Two Resampling Strategies for Long-Tailed Detection Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Animashree Anandkumar, Sanja Fidler, Jose M Alvarez

L4DC 2021 Robust Reinforcement Learning: A Constrained Game-Theoretic Approach Jing Yu, Clement Gehring, Florian Schäfer, Animashree Anandkumar

ICML 2021 SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Animashree Anandkumar

ICML 2021 Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar

ICML 2020 Angular Visual Hardness Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar

ICML 2020 Automated Synthetic-to-Real Generalization Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar

ICML 2020 Implicit Competitive Regularization in GANs Florian Schaefer, Hongkai Zheng, Animashree Anandkumar

UAI 2020 OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg

ICML 2020 Semi-Supervised StyleGAN for Disentanglement Learning Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit Patel, Animashree Anandkumar

ICML 2019 Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Animashree Anandkumar

ICLR 2019 Regularized Learning for Domain Adaptation Under Label Shifts Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar

ICLR 2018 Combining Symbolic Expressions and Black-Box Function Evaluations in Neural Programs Forough Arabshahi, Sameer Singh, Animashree Anandkumar

ICLR 2018 Deep Active Learning for Named Entity Recognition Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar

ICLR 2018 Learning from Noisy Singly-Labeled Data Ashish Khetan, Zachary C. Lipton, Animashree Anandkumar

ECCV 2018 Question Type Guided Attention in Visual Question Answering Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar

ICLR 2018 Stochastic Activation Pruning for Robust Adversarial Defense Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy D. Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar

ICML 2018 StrassenNets: Deep Learning with a Multiplication Budget Michael Tschannen, Aran Khanna, Animashree Anandkumar

ICML 2018 signSGD: Compressed Optimisation for Non-Convex Problems Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar

JMLR 2017 Analyzing Tensor Power Method Dynamics in Overcomplete Regime Animashree Anandkumar, Rong Ge, Majid Janzamin

COLT 2016 Efficient Approaches for Escaping Higher Order Saddle Points in Non-Convex Optimization Animashree Anandkumar, Rong Ge

COLT 2016 Open Problem: Approximate Planning of POMDPs in the Class of Memoryless Policies Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

COLT 2016 Reinforcement Learning of POMDPs Using Spectral Methods Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

COLT 2015 Learning Overcomplete Latent Variable Models Through Tensor Methods Animashree Anandkumar, Rong Ge, Majid Janzamin

JMLR 2015 Online Tensor Methods for Learning Latent Variable Models Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Animashree Anandkumar

JMLR 2015 When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Animashree Anandkumar, Daniel Hsu, Majid Janzamin, Sham Kakade

JMLR 2014 A Tensor Approach to Learning Mixed Membership Community Models Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade

JMLR 2014 High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models Majid Janzamin, Animashree Anandkumar

COLT 2014 Learning Sparsely Used Overcomplete Dictionaries Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

NeurIPS 2014 Non-Convex Robust PCA Praneeth Netrapalli, U N Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain

ICML 2014 Nonparametric Estimation of Multi-View Latent Variable Models Le Song, Animashree Anandkumar, Bo Dai, Bo Xie

JMLR 2014 Tensor Decompositions for Learning Latent Variable Models Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky

COLT 2013 A Tensor Spectral Approach to Learning Mixed Membership Community Models Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade

ICML 2013 Learning Linear Bayesian Networks with Latent Variables Animashree Anandkumar, Daniel Hsu, Adel Javanmard, Sham Kakade

COLT 2012 A Method of Moments for Mixture Models and Hidden Markov Models Animashree Anandkumar, Daniel Hsu, Sham M. Kakade

ICML 2012 High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains Majid Janzamin, Animashree Anandkumar

JMLR 2012 High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion Animashree Anandkumar, Vincent Y.F. Tan, Furong Huang, Alan S. Willsky

NeurIPS 2011 High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions Animashree Anandkumar, Vincent Tan, Alan S. Willsky

JMLR 2011 Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky

JMLR 2011 Learning Latent Tree Graphical Models Myung Jin Choi, Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky

NeurIPS 2011 Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang