2023
"Tensor methods in deep learning"
Yannis Panagakis, Jean Kossaifi, Grigorios G Chrysos, James Oldfield, Taylor Patti, Mihalis A Nicolaou, Anima Anandkumar, Stefanos Zafeiriou
Appeared at Signal Processing and Machine Learning Theory
"Towards Large Language Models as Copilots for Theorem Proving in Lean"
Peiyang Song, Kaiyu Yang, Anima Anandkumar
Appeared at the 3rd Workshop on Mathematical Reasoning and AI at NeurIPS 2023
"EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification"
Benyamin Haghi, Lin Ma, Sahin Lale, Anima Anandkumar, Azita Emami
Appeared at IEEE Biomedical Circuits and Systems (BioCAS) 2023
"Eureka: Human-Level Reward Design via Coding Large Language Models"
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar
"DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies"
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Christopher Bishop, Max Welling, Tie-Yan Liu, Christian Bodnar, Johannes Brandsetter, Wessel Bruinsma, Chan Cao, Yuan-Jyue Chen, Peggy Dai, Patrick Garvan, Liang He, Elizabeth Heider, Pipi Hu, Peiran Jin, Fusong Ju, Yatao Li, Chang Liu, Renqian Luo, Qi Meng, Frank Noe, Tao Qin, Janwei Zhu, Bin Shao, Yu Shi, Wenlei Shi, Gregor Simm, Megan Stanley, Lixin Sun, Yue Wang, Tong Wang, Zun Wang, Lijun Wu, Yingce Xia, Leo Xia, Shufang Xie, Shuxin Zheng, Jianwei Zhu, Pete Luferenko, Divya Kumar, Jonathan Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M Mann, Michael Irvin, J Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Thomas Gibbs, Ian Foster, James J Davis, Michael E Papka, Thomas Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi Hanson, Thomas E Potok, Lupo Pasini, Kate Evans, Dan Lu, Dalton Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin Aji, Angela Dalton, Michael Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Anima Anandkumar, Rick Stevens
"Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs"
Jean Kossaifi, Nikola Kovachki, Kamyar Azizzadenesheli, Anima Anandkumar
"Neural Operators for Accelerating Scientific Simulations and Design"
Kamyar Azzizadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar
"Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces"
Miguel Liu-Schiaffini, Clare E. Singer, Nikola Kovachki, Tapio Schneider, Kamyar Azizzadenesheli, Anima Anandkumar
"Real-time high-resolution CO2 geological storage prediction using nested Fourier neural operators"
Gege Wen, Zongyi Li, Qirui Long, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson
Appeared at Energy & Environmental Science
"Tensor methods in deep learning"
Yannis Panagakis, Jean Kossaifi, Grigorios G Chrysos, James Oldfield, Taylor Patti, Mihalis A Nicolaou, Anima Anandkumar, Stefanos Zafeiriou
Appeared at the Book of Signal Processing and Machine Learning Theory
"Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints"
Taylor L. Patti, Jean Kossaifi, Anima Anandkumar, Susanne F. Yelin
"LeanDojo: Theorem Proving with Retrieval-Augmented Language Models"
Kaiyu Yang, Aidan M. Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar
"InRank: Incremental Low-Rank Learning"
Jiawei Zhao, Yifei Zhang, Beidi Chen, Florian Schäfer, Anima Anandkumar
"Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials"
Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhiming Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang
"Fast Training of Diffusion Models with Masked Transformers"
Hongkai Zheng, Weili Nie, Arash Vahdat, Anima Anandkumar
"ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators"
Sungduk Yu, Walter M Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C Will, Tom Beucler, Bryce E Harrop, Benjamin R Hillman, Andrea M Jenney, Savannah L Ferretti, Nana Liu, Anima Anandkumar, Noah D Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P Abernathey, Fiaz Ahmed, David C Bader, Pierre Baldi, Elizabeth A Barnes, Gunnar Behrens, Christopher S Bretherton, Julius JM Busecke, Peter M Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J Lutsko, Po-Lun Ma, Griffin Mooers, J David Neelin, David A Randall, Sara Shamekh, Akshay Subramaniam, Mark A Taylor, Nathan M Urban, Janni Yuval, Guang J Zhang, Tian Zheng, Michael S Pritchard
"Physics-Informed Neural Approaches for Multiscale Molecular Modeling and Design"
Qiao, Zhuoran
PhD Dissertation 2023
"Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere"
Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar
"Thompson Sampling for Partially Observable Linear-Quadratic Control"
Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi
Appeared at 2023 American Control Conference (ACC)
"Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo"
Haque Ishfaq, Qingfeng Lan, Pan Xu, A Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
"Voyager: An Open-Ended Embodied Agent with Large Language Models"
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar
"Pretraining Neural-Networks with Neural-Fly for Rapid Online Learning"
Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
Appeared at ICRA2023 Workshop on Pretraining for Robotics (PT4R)
"Shall we pretrain autoregressive language models with retrieval? a comprehensive study"
Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
"Distributionally Robust Policy Gradient for Offline Contextual Bandits"
Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Anima Anandkumar
Appeared at International Conference on Artificial Intelligence and Statistics
"Neural Operators for Solving PDEs and Inverse Design"
Anima Anandkumar
Appeared at Proceedings of the 2023 International Symposium on Physical Design
"Efficient and Large-Scale Semidefinite Programming with Quantum Neural Networks"
Taylor Patti, Jean Kossaifi, Anima Anandkumar, Susanne Yelin
Appeared at Bulletin of the American Physical Society
"AI-aided Geometric Design of Anti-infection Catheters"
Tingtao Zhou, Xuan Wan, Daniel Zhengyu Huang, Zongyi Li, Zhiwei Peng, Anima Anandkumar, John F. Brady, Paul W. Sternberg, Chiara Daraio
"A vision transformer for decoding surgeon activity from surgical videos"
Dani Kiyasseh, Runzhuo Ma, Taseen F. Haque, Brian J. Miles, Christian Wagner, Daniel A. Donoho, Animashree Anandkumar, Andrew J. Hung
Appeared at Nature Biomedical Engineering
"Human visual explanations mitigate bias in AI-based assessment of surgeon skills"
Dani Kiyasseh, Jasper Laca, Taseen F. Haque, Maxwell Otiato, Brian J. Miles, Christian Wagner, Daniel A. Donoho, Quoc-Dien Trinh, Animashree Anandkumar, Andrew J. Hung
Appeared at Nature Partner Journals Digital Medicine
"A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons"
Dani Kiyasseh, Jasper Laca, Taseen F. Haque, Brian J. Miles, Christian Wagner, Daniel A. Donoho, Animashree Anandkumar, Andrew J. Hung
Appeared at Communications Medicine
"Robust Trajectory Prediction against Adversarial Attacks"
Yulong Cao, Danfei Xu, Xinshuo Weng, Zhuoqing Mao, Anima Anandkumar, Chaowei Xiao, Marco Pavone
Appeared at Conference on Robot Learning 2023
"Prismer: A Vision-Language Model with An Ensemble of Experts"
Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar
"Score-based Diffusion Models in Function Space"
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar
"VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion"
Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M. Alvarez, Sanja Fidler, Chen Feng, Anima Anandkumar
"Vision Transformers Are Good Mask Auto-Labelers"
Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, Jose M. Alvarez, Anima Anandkumar
"MimicPlay: Long-Horizon Imitation Learning by Watching Human Play"
Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar
"Fourier Neural Operator for Plasma Modelling"
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Anima Anandkumar, MAST Team
"AutoBiasTest: Controllable Sentence Generation for Automated and Open-Ended Social Bias Testing in Language Models"
Rafal Kocielnik, Shrimai Prabhumoye, Vivian Zhang, R. Michael Alvarez, Anima Anandkumar
"PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees"
Chulin Xie, De-An Huang, Wenda Chu, Daguang Xu, Chaowei Xiao, Bo Li, Anima Anandkumar
"I2SB: Image-to-Image Schrödinger Bridge"
Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A. Theodorou, Weili Nie, Anima Anandkumar
"A Text-guided Protein Design Framework"
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar
"Forecasting subcritical cylinder wakes with Fourier Neural Operators"
Peter I Renn, Cong Wang, Sahin Lale, Zongyi Li, Anima Anandkumar, Morteza Gharib
2022
"Surgical gestures as a method to quantify surgical performance and predict patient outcomes"
Runzhuo Ma, Ashwin Ramaswamy, Jiashu Xu, Loc Trinh, Dani Kiyasseh, Timothy N. Chu, Elyssa Y. Wong, Ryan S. Lee, Ivan Rodriguez, Gina DeMeo, Aditya Desai, Maxwell X. Otiato, Sidney I. Roberts, Jessica H. Nguyen, Jasper Laca, Yan Liu, Katarina Urbanova, Christian Wagner, Animashree Anandkumar, Jim C. Hu, Andrew J. Hung
Appeared at Nature Partner Journals Digital Medicine
"HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression"
Jiaqi Gu, Ben Keller, Jean Kossaifi, Anima Anandkumar, Brucek Khailany, David Z. Pan
"Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators"
Haydn Maust, Zongyi Li, Yixuan Wang, Daniel Leibovici, Oscar Bruno, Thomas Hou, Anima Anandkumar
"Machine Learning Accelerated PDE Backstepping Observers"
Yuanyuan Shi, Zongyi Li, Huan Yu, Drew Steeves, Anima Anandkumar, Miroslav Krstic
"Incremental Fourier Neural Operator"
Jiawei Zhao, Robert Joseph George, Yifei Zhang, Zongyi Li, Anima Anandkumar
"Fast Sampling of Diffusion Models via Operator Learning"
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2023
"MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training"
De-An Huang, Zhiding Yu, Anima Anandkumar
"Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions"
Rafal Kocielnik, Sara Kangaslahti, Shrimai Prabhumoye, Meena Hari, R. Michael Alvarez, Anima Anandkumar
"VIMA: General Robot Manipulation with Multimodal Prompts"
Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan
Appeared at International Conference on Machine Learning (ICML) 2023
"DensePure: Understanding Diffusion Models towards Adversarial Robustness"
Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song
"Accelerating Carbon Capture and Storage Modeling using Fourier Neural Operators"
Gege Wen, Zongyi Li, Qirui Long, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson
"1st Place Solution of The Robust Vision Challenge (RVC) 2022 Semantic Segmentation Track"
Junfei Xiao, Zhichao Xu, Shiyi Lan, Zhiding Yu, Alan Yuille, Anima Anandkumar
Appeared at the Robust Vision Challenge 2022 Semantic Segmentation Track
"Capturing fine-grained details for video-based automation of suturing skills assessment"
Andrew J. Hung, Richard Bao, Idris O. Sunmola, De-An Huang, Jessica H. Nguyen, Anima Anandkumar
Appeared at International Journal of Computer Assisted Radiology and Surgery
"Using Real-time Feedback To Improve Surgical Performance on a Robotic Tissue Dissection Task"
Jasper A. Laca, Rafal Kocielnik, Jessica H. Nguyen, Jonathan You, Ryan Tsang, Elyssa Y. Wong, Andrew Shtulman, Anima Anandkumar, Andrew J. Hunga
Appeared at European Urology Open Science
"Context Generation Improves Open Domain Question Answering"
Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
"GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics" Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan
ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research "Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models"
Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Anima Anandkumar
"Learning Dissipative Dynamics in Chaotic Systems"
Zongyi Li, Miguel Liu-Schiaffini, Nikola Kovachki, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2022
"Assessing the efficacy of dissection gestures in robotic surgery"
Daniel A. Inouye, Runzhuo Ma, Jessica H. Nguyen, Jasper Laca, Rafal Kocielnik, Anima Anandkumar, Andrew J. Hung
Appeared at Journal of Robotic Surgery 2022
"AdvDO: Realistic Adversarial Attacks for Trajectory Prediction"
Yulong Cao, Chaowei Xiao, Anima Anandkumar, Danfei Xu, Marco Pavone
"Retrieval-based Controllable Molecule Generation"
Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard Baraniuk, Anima Anandkumar
"PeRFception: Perception using Radiance Fields"
Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Christopher Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park
"FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators"
Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Animashree Anandkumar
Best Paper Award at the Platform for Advanced Scientific Computing (PASC) 2023
"Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry"
Zhuoran Qiao, Anders S. Christensen, Matthew Welborn, Frederick R. Manby, Anima Anandkumar, Thomas F. Miller III
Appeared at Proceedings of the National Academy of Sciences (PNAS)
"Quantification of Robotic Surgeries with Vision-Based Deep Learning"
Dani Kiyasseh, Runzhuo Ma, Taseen F. Haque, Jessica Nguyen, Christian Wagner, Animashree Anandkumar, Andrew J. Hung
"Fourier Neural Operator with Learned Deformations for PDEs on General Geometries"
Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar
"Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training"
Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren
"MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge" Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar
Outstanding Paper Award at Neural Information Processing Systems (NeurIPS) 2022 "Langevin Monte Carlo for Contextual Bandits"
Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2022
"Thompson Sampling Achieves Õ (√ T) Regret in Linear Quadratic Control"
Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi
Appeared at the 35th Annual Conference on Learning Theory (COLT) 2022
"Neural Scene Representation for Locomotion on Structured Terrain"
David Hoeller, Nikita Rudin, Christopher Choy, Animashree Anandkumar, Marco Hutter
Appeared at the IEEE Robotics and Automation Letters (RA-L) and IROS 2022
"Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits"
Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar
"Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions"
Huaizu Jiang, Xiaojian Ma, Weili Nie, Zhiding Yu, Yuke Zhu, Anima Anandkumar
Appeared at Conference on Computer Vision and Pattern Recognition (CVPR) 2022
"Diffusion Models for Adversarial Purification"
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2022
"Generative Adversarial Neural Operators"
Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar, Zachary E. Ross, Kamyar Azizzadenesheli
"Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds"
Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
Appeared at Science Robotics
"M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation"
Enze Xie, Zhiding Yu, Daquan Zhou, Jonah Philion, Anima Anandkumar, Sanja Fidler, Ping Luo, Jose M Alvarez
"Understanding The Robustness in Vision Transformers"
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng, Jose M Alvarez
Appeared at Neural Information Processing Systems (NeurIPS) 2022
"Expert Surgeons and Deep Learning Models Can Predict the Outcome of Surgical Hemorrhage from One Minute of Video"
Dhiraj J Pangal, Guillaume Kugener, Yichao Zhu, Aditya Sinha, Vyom Unadkat, David J Cote, Ben Strickland, Martin Rutkowski, Andrew Hung, Animashree Anandkumar, X.Y. Han, Vardan Papyan, Bozena Wrobel, Gabriel Zada, Daniel A Donoho
Appeared at Scientific Reports
"Disentangling Observed Causal Effects from Latent Confounders using Method of Moments"
Anqi Liu, Hao Liu, Tongxin Li, Saeed Karimi-Bidhendi, Yisong Yue, Anima Anandkumar
"RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning"
Xiaojian Ma, Weili Nie, Zhiding Yu, Huaizu Jiang, Chaowei Xiao, Yuke Zhu, Song-Chun Zhu, Anima Anandkumar
Appeared at International Conference on Learning Representations (ICLR) 2022
"Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications"
Guillaume Kugener, Dhiraj J Pangal, Tyler Cardinal, Casey Collet, Elizabeth Lechtholz-Zey, Sasha Lasky, Shivani Sundaram, Nicholas Markarian, Yichao Zhu, Arman Roshannai, Aditya Sinha, X Y Han, Vardan Papyan, Andrew Hung, Animashree Anandkumar, Bozena Wrobel, Gabriel Zada, Daniel A Donoho
Appeared at JAMA Network Open
"ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation"
Bokui Shen, Zhenyu Jiang, Christopher Choy, Leonidas J. Guibas, Silvio Savarese, Anima Anandkumar, Yuke Zhu
Appeared at Robotics: Science and Systems (RSS) 2022
"Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks"
Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren
Appeared at ACM/IEEE Design Automation Conference (DAC) 2022
"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators"
Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, Animashree Anandkumar
"FreeSOLO: Learning to Segment Objects without Annotations"
Xinlong Wang, Zhiding Yu, Shalini De Mello, Jan Kautz, Anima Anandkumar, Chunhua Shen, Jose M. Alvarez
Appeared at Conference on Computer Vision and Pattern Recognition (CVPR) 2022
"Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models"
Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro
"A learning-based multiscale method and its application to inelastic impact problems"
Burigede Liu, Nikola Kovachki, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Andrew M Stuart, Kaushik Bhattacharya
Appeared at Journal of the Mechanics and Physics of Solids
"OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation"
Josiah Wong, Viktor Makoviychuk, Anima Anandkumar, Yuke Zhu
Appeared at International Conference on Robotics and Automation (ICRA) 2022
"Panoptic SegFormer"
Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Tong Lu, Ping Luo
Appeared at Conference on Computer Vision and Pattern Recognition (CVPR) 2022
"Towards Reducing Labeling Cost in Deep Object Detection"
Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixe, Jose M. Alvarez
Appeared at Conference on Computer Vision and Pattern Recognition (CVPR) 2022
"Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers"
John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, Bryan Catanzaro
Appeared at International Conference on Learning Representations (ICLR) 2022
"U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow"
Gege Wen, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson
Appeared at Advances in Water Resources
2021
"KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems"
Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, Anima Anandkumar
"Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems"
Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar
Appeared at AISTATS 2022
"TensorLy-Quantum: Quantum Machine Learning with Tensor Methods"
Taylor L. Patti, Jean Kossaifi, Susanne F. Yelin, Anima Anandkumar
"The Relationship of Technical Skills and Cognitive Workload to Errors During Robotic Surgical Exercises"
Sidney I Roberts, Steven Yong Cen, Jessiica Nguyen, Laura C Perez, Luis G Medina, Runzhuo Ma, Sandra Marshall, Rafal Kocielnik, Anima Anandkumar, Andrew J Hung
Appeared at Journal of Endourology
"Model Learning Predictive Control in Nonlinear Dynamical Systems"
Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar
Appeared at IEEE Conference on Decision and Control (CDC) 2021
"Simulation Intelligence: Towards a New Generation of Scientific Methods"
Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
"Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases"
Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi, Anima Anandkumar, Bryan Catanzaro
"Stability Constrained Reinforcement Learning for Real-Time Voltage Control"
Jie Feng, Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman
"Use of surgical video–based automated performance metrics to predict blood loss and success of simulated vascular injury control in neurosurgery: a pilot study"
Dhiraj J Pangal, Guillaume Kugener, Tyler Cardinal, Elizabeth Lechtholz-Zey, Casey Collet, Sasha Lasky, Shivani Sundaram, Yichao Zhu, Arman Roshannai, Justin Chan, Aditya Sinha, Andrew J Hung, Animashree Anandkumar, Gabriel Zada, Daniel A Donoho
Appeared at Journal of Neurosurgery
"CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning"
Kevin Huang, Sahin Lale, Ugo Rosolia, Yuanyuan Shi, Anima Anandkumar
"Controllable and Compositional Generation with Latent-Space Energy-Based Models"
Weili Nie, Arash Vahdat, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2021
"Coupled Segmentation and Edge Learning via Dynamic Graph Propagation"
Zhiding Yu, Rui Huang, Wonmin Byeon, Sifei Liu, Guilin Liu, Thomas Breuel, Anima Anandkumar, Jan Kautz
Appeared at Neural Information Processing Systems (NeurIPS) 2021
"Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions"
Jiachen Sun, Yulong Cao, Christopher Choy, Zhiding Yu, Anima Anandkumar, Z. Morley Mao, Chaowei Xiao
Appeared at Neural Information Processing Systems (NeurIPS) 2021
"AugMax: Adversarial Composition of Random Augmentations for Robust Training"
Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang
Appeared at Neural Information Processing Systems (NeurIPS) 2021
"Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases"
Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi, Anima Anandkumar, Bryan Catanzaro
"Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects"
Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu
Appeared at International Conferenceon Robotics and Automation (ICRA) 2021
"LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update"
Jiawei Zhao, Steve Dai, Rangharajan Venkatesan, Ming-Yu Liu, Brucek Khailany, Bill Dally, Anima Anandkumar
Appeared at IEEE Transactions on Computers 2022
"ZerO Initialization: Initializing Neural Networks with only Zeros and Ones"
Jiawei Zhao, Florian Schäfer, Anima Anandkumar
Appeared at Transactions on Machine Learning Research (TMLR) 2022
"Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds"
Yujia Huang, Huan Zhang, Yuanyuan Shi, J Zico Kolter, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2021
"Physics-Informed Neural Operator for Learning Partial Differential Equations"
Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, Anima Anandkumar
"Polymatrix Competitive Gradient Descent"
Jeffrey Ma, Alistair Letcher, Florian Schäfer, Yuanyuan Shi, Anima Anandkumar
"#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol"
Abigail Dommer, Lorenzo Casalino, Fiona Kearns, Mia Rosenfeld, Nicholas Wauer, Surl-Hee Ahn, John Russo, Sofia Oliveira, Clare Morris, Anthony Bogetti, Anda Trifan, Alexander Brace, Terra Sztain, Austin Clyde, Heng Ma, Chakra Chennubhotla, Hyungro Lee, Matteo Turilli, Syma Khalid, Teresa Tamayo-Mendoza, Matthew Welborn, Anders Christensen, Daniel G. A. Smith, Zhuoran Qiao, Sai Krishna Sirumalla, Michael O’Connor, Frederick Manby, Anima Anandkumar, David Hardy, James Phillips, Abraham Stern, Josh Romero, David Clark, Mitchell Dorrell, Tom Maiden, Lei Huang, John McCalpin, Christopher Woods, Alan Gray, Matt Williams, Bryan Barker, Harinda Rajapaksha, Richard Pitts, Tom Gibbs, John Stone, Daniel Zuckerman, Adrian Mulholland, Thomas Miller III, Shantenu Jha, Arvind Ramanathan, Lillian Chong, Rommie Amaro
Finialist to the ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research"Reinforcement Learning in Factored Action Spaces using Tensor Decompositions"
Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2021, QTNML Workshop
"A learning-based multiscale method and its application to inelastic impact problems"
Burigede Liu, Nikola Kovachki, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Andrew Stuart, Kaushik Bhattacharya
Appeared at Journal of the Mechanics and Physics of Solids
"Long-Short Transformer: Efficient Transformers for Language and Vision"
Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro
Appeared at Neural Information Processing Systems (NeurIPS) 2021
"Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action"
Anda Trifan, Defne Gorgun, Zongyi Li, Alexander Brace, Maxim Zvyagin, Heng Ma, Austin Clyde, David Clark, Michael Salim, David J. Hardy, Tom Burnley, Lei Huang, John McCalpin, Murali Emani, Hyenseung Yoo, Junqi Yin, Aristeidis Tsaris, Vishal Subbiah, Tanveer Raza, Jessica Liu, Noah Trebesch, Geoffrey Wells, Venkatesh Mysore, Thomas Gibbs, James Phillips, S. Chakra Chennubhotla, Ian Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, John E. Stone, Emad Tajkhorshid, Sarah A. Harris, Arvind Ramanathan
Finialist to the ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research"Auditing AI models for Verified Deployment under Semantic Specifications"
Homanga Bharadhwaj, De-An Huang, Chaowei Xiao, Anima Anandkumar, Animesh Garg
"Deep Learning to Automate Technical Skills Assessment in Robotic Surgery"
Andrew J. Hung, Yan Liu, Animashree Anandkumar
Appeared at JAMA Surgery
"Fast Uncertainty Quantification for Deep Object Pose Estimation"
Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Anima Anandkumar, Yuke Zhu
Appeared at International Conference on Robotics and Automation (ICRA) 2021
"Contrastive Syn-to-Real Generalization"
Wuyang Chen, Zhiding Yu, Shalini De Mello, Sifei Liu, Jose M. Alvarez, Zhangyang Wang, Anima Anandkumar
Appeared at International Conference on Learning Representations (ICLR) 2021
"Self-Calibrating Neural Radiance Fields"
Yoonwoo Jeong, Seokjun Ahn, Christopher Choy, Anima Anandkumar, Minsu Cho, Jaesik Park
Appeared at International Conference on Computer Vision (ICCV) 2021
"A systematic review of virtual reality for the assessment of technical skills in neurosurgery"
Chan J, Pangal DJ, Cardinal T, Kugener G, Zhu Y, Roshannai A, Markarian N, Sinha A, Anandkumar A, Hung A, Zada G
Appeared at Journal of Neurosurgery
"Robust Reinforcement Learning: A Constrained Game-theoretic Approach"
Jing Yu, Clement Gehring, Florian Schäfer, Animashree Anandkumar
Appeared at Learning for Dynamics & Control Conference (L4DC) 2021
"Unsupervised Controllable Generation with Self-Training"
Grigorios G Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar
Appeared at the International Joint Conference on Neural Networks (IJCNN) 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, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2021
"Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition"
Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2021
"Dynamic Social Media Monitoring for Fast-Evolving Online Discussions"
Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, Anima Anandkumar
Appeared at Knowledge Discovery and Data Mining (KDD) 2021
"Tensor Methods in Computer Vision and Deep Learning"
Yannis Panagakis, Jean Kossaifi, Grigorios G. Chrysos, James Oldfield, Mihalis A. Nicolaou, Anima Anandkumar, Stefanos Zafeiriou
Appeared at Proceedings of the IEEE
"Variational Quantum Optimization with Multi-Basis Encodings"
Taylor L. Patti, Jean Kossaifi, Anima Anandkumar, Susanne F. Yelin
"OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy"
Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao, Michael B. O'Connor, Daniel G. A. Smith, Feizhi Ding, Peter J. Bygrave, Animashree Anandkumar, Matthew Welborn, Frederick R. Manby, Thomas F. Miller III
"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
Appeared at International Conference on Machine Learning (ICML) 2021
"UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry"
Zhuoran Qiao, Anders S. Christensen, Matthew Welborn, Frederick R. Manby, Anima Anandkumar, Thomas F. Miller III
"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers"
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo
Appeared at Neural Information Processing Systems (NeurIPS) 2021
"DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision"
Shiyi Lan, Zhiding Yu, Christopher Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry S. Davis, Anima Anandkumar
Appeared at International Conference on Computer Vision (ICCV) 2021
"Neural Operator: Learning Maps Between Function Spaces"
Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
"Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection"
Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez
Appeared at International Conference on Machine Learning (ICML) 2021
"Stability and Identification of Random Asynchronous Linear Time-Invariant Systems"
Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar
Appeared at Learning for Dynamics & Control Conference (L4DC) 2021
"Stable Online Control of Linear Time-Varying Systems"
Guannan Qu, Yuanyuan Shi, Sahin Lale, Anima Anandkumar, Adam Wierman
Appeared at Learning for Dynamics & Control Conference (L4DC) 2021
"Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems"
Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar
Appeared at Learning for Dynamics & Control Conference (L4DC) 2021
"Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles"
Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar
Appeared at Intelligent Vehicles Symposium 2021
"Physics-informed machine learning: case studies for weather and climate modelling"
K. Kashinath, M. Mustafa, A. Albert, J-L. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, R. Wang, A. Chattopadhyay, A. Singh, A. Manepalli, D. Chirila, R. Yu, R. Walters, B. White, H. Xiao, H. A. Tchelepi, P. Marcus, A. Anandkumar, P. Hassanzadeh and Prabhat
Appeared at Phil. Trans. R. Soc.
"Fourier Neural Operator for Parametric Partial Differential Equations"
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
Appeared at International Conference on Learning Representations (ICLR) 2021
"Active Learning under Label Shift"
Eric Zhao, Anqi Liu, Anima Anandkumar, Yisong Yue
Appeared at AISTATS 2021
"Competitive Policy Optimization"
Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
Appeared at UAI 2021
"Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting"
Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar
Appeared at ACC 2021
2020
"Automated Synthetic-to-Real Generalization"
Wuyang Chen, Zhiding Yu, Zhangyang Wang, Anima Anandkumar
Appeared at ICML 2020
"BONGARD-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning"
Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2020
"OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation"
Hongyu Ren, Yuke Zhu, Jure Leskovec, Anima Anandkumar, Animesh Garg
Appeared at UAI 2020
"Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces"
Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J. Bygrave, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III
Appeared at Neural Information Processing Systems (NeurIPS) 2020, ML4Molecules Workshop
"Distributionally Robust Learning for Unsupervised Domain Adaptation"
Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar
"Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion"
Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg
Appeared at Conference on Robot Learning (CoRL) 2020
"MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models"
Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
Appeared at Empirical Methods in Natural Language Processing (EMNLP) 2020
"Multipole Graph Neural Operator for Parametric Partial Differential Equations"
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2020
"Learning compositional functions via multiplicative weight updates"
Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue
Appeared at Neural Information Processing Systems (NeurIPS) 2020
"Causal Discovery in Physical Systems from Videos"
Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg
Appeared at Neural Information Processing Systems (NeurIPS) 2020
"Convolutional Tensor-Train LSTM for Spatio-Temporal Learning"
Jiahao Su, Wonmin Byeon, Furong Huang, Jan Kautz, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2020
"Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems"
Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2020
"Neural Networks with Recurrent Generative Feedback"
Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Y. Tsao, Anima Anandkumar
Appeared at Neural Information Processing Systems (NeurIPS) 2020
"Competitive Mirror Descent"
Florian Schäfer, Anima Anandkumar, Houman Owhadi
"Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery"
Francisco Luongo, Jessica H. Nguyen, Anima Anandkumar, Andrew J. Hung
Appeared at Journal of Surgery
"OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features"
Zhuoran Qiao, Matthew Welborn, Anima Anandkumar, Frederick R. Manby, Thomas F. Miller III
Appeared at Journal of Chemical Physics, editor's pick
"Robust Regression for Safe Exploration in Control"
Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
Appeared at Learning for Dynamics & Control Conference (L4DC) 2020
"Implicit competitive regularization in GANs"
Florian Schäfer, Hongkai Zheng, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2020
"Angular Visual Hardness"
Beidi Chen, Weiyang Liu, Animesh Garg, Zhiding Yu, Anshumali Shrivastava, Jan Kautz, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2020
"MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework"
Chiyu “Max” Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
Appeared at SC 2020
"Deep Bayesian Quadrature Policy Optimization"
Akella Ravi Tej, Kamyar Azizzadeneshel, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue
Appeared at AAAI 2020
"Policy Gradient in Partially Observable Environments:Approximation and Convergence"
Kamyar Azizzadeneshel, Yisong Yue, Anima Anandkumar
"Learning Causal State Representations of Partially Observable Environments"
Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello
"Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems"
Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
Appeared at IEEE Robotics and Automation
"Turbulence forecasting via Neural ODE"
Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro, Tan Minh Nguyen, Balasubramanya T. Nadiga, Juan A. Saenz, Michael Chertkov, Animesh Garg, Anima Anandkumar, Andreas Dengel, Richard Baraniuk, David P. Schmidt
"Regret Minimization in Partially Observable Linear Quadratic Control"
Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar
"Neural Operator: Graph Kernel Network for Partial Differential Equations"
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
"Semi-Supervised StyleGAN for Disentanglement Learning"
Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debhath, Anjul Patney, Ankit B. Patel, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2020
"Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates"
Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R. Michael Alvarez, Anima Anandkumar
"Spectral Learning on Matrices and Tensors"
Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
Foundations and TrendsR in Machine Learning, 12(5-6):393–536, 2019
2019
"InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers"
Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar
"Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data"
Francesca Baldini, Anima Anandkumar, Richard M. Murray
Appeared at ACC 2020
"Triply Robust Off-Policy Evaluation"
Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue
"Tree Stack Memory Units"
Forough Arabshahi, Zhichu Lu, Sameer Singh, Anima Anandkumar
"Guaranteed Scalable Learning of Latent Tree Models"
Furong Huang, Niranjan Uma Naresh, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar
Appeared at UAI 2019
"Large Scale Cloud Deployment of Spectral Topic Modeling"
Chris Swierczewski, Sravan Bodapati, Anurag Beniwal, David Leen, Anima Anandkumar
Appeared at Knowledge Discovery and Data Mining (KDD) 2019, ParLearning Workshop, Anchorage, Alaska, USA
"Directivity Modes of Earthquake Populations with Unsupervised Learning"
Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli, Anima Anandkumar
Appeared at Journal of Geophysical Research - Solid Earth
"Robust Deep Networks with Randomized Tensor Regression Layers"
Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Adrian Bulat, Anima Anandkumar, Ioanna Tzoulaki, Paul Matthews
"Competitive Gradient Descent"
Florian Schäfer, Anima Anandkumar
Appeared at Neural Information Processing Systems (Neural Information Processing Systems (NeurIPS)) 2019
"Open Vocabulary Learning on Source Code with a Graph–Structured Cache"
Milan Cvitkovic, Badal Singh, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) (ICML) 2019
"Stochastic Linear Bandits with Hidden Low Rank Structure"
Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi
"Multi-dimensional Tensor Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations"
Yang Shi, Anima Anandkumar
"Active Learning with Partial Feedback"
Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan
Appeared at International Conference on Learning Representations (ICLR) 2019
"Regularized Learning for Domain Adaptation under Label Shifts"
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar
Appeared at International Conference on Learning Representations (ICLR) 2019
"Neural Lander: Stable Drone Landing Control using Learned Dynamics"
Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung
Appeared at International Conference on Robotics and Automation (ICRA) 2019
"signSGD is Communication Efficient and Byzantine Fault Tolerant"
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar
Appeared at International Conference on Learning Representations (ICLR) 2019
2018
"What’s in a name? The need to nip NIPS"
Daniela M. Witten,Elana J. Fertig, Anima Anandkumar, Jeff Dean
Appeared at Neural Information Processing Systems (NeurIPS) 2018 workshop: Critiquing and Correcting Trends in Machine Learning
"Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning"
Nhat Ho, Tan Nguyen, Ankit Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk
"Trust Region Policy Optimization of POMDPs"
Kamyar Azizzadenesheli, Manish Kumar Bera, Animashree Anandkumar
Under review
"Surprising Negative Results for Generative Adversarial Tree Search"
Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Emma Brunskill , Zachary C. Lipton, Animashree Anandkumar
Under review
"Deep Learning On Code with an Unbounded Vocabulary"
Milan Cvitkovic, Badal Singh, Anima Anandkumar
Appeared at FLoC 2018, Machine Learning for Programming Workshop, Oxford, UK
"StrassenNets: Deep learning with a multiplication budget"
Michael Tschannen, Aran Khanna, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2018, Stockholm, Sweden
"Born Again Neural Networks"
Tommaso Furlanello, Zack Chase Lipton, Laurent Itti, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2018, Stockholm, Sweden
"signSGD: Compressed Optimisation for Non-Convex Problems"
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar
Appeared at International Conference on Machine Learning (ICML) 2018, Stockholm, Sweden
"TensorLy: Tensor Learning in Python"
Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic
"Probabilistic FastText for Multi-Sense Word Embeddings"
Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar
Appeared at ACL 2018, Melbourne, Australia
"Question Type Guided Attention in Visual Question Answering"
Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar
Appeared at ECCV 2018, Munich, Germany
"Compression by the signs: distributed learning is a two-way street"
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar
Appeared at ICLR 2018, Vancouver, Canada as a Workshop paper
"Stochastic Activation Pruning for Robust Adversarial Defense"
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar
Appeared at ICLR 2018, Vancouver, Canada
"Learning From Noisy Singly-labeled Data"
Ashish Khetan, Zachary C. Lipton, Anima Anandkumar
Appeared at ICLR 2018, Vancouver, Canada
"Deep Active Learning for Named Entity Recognition"
Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Anima Anandkumar
Appeared at ICLR 2018, Vancouver, Canada
"Combining Symbolic Expressions and Black-Box Function Evaluations In Neural Programs"
Forough Arabshahi, Sameer Singh, Animashree Anandkumar
Appeared at ICLR 2018, Vancouver, Canada. Also appeared at NIPS 2017, MLtrain workshop, Long Beach, CA.
2017
-
"Efficient Exploration through Bayesian Deep Q-Networks"
Kamyar Azizzadenesheli, Animashree Anandkumar
Appeared at NIPS 2017, Deep RL Workshop, Long Beach, California
-
"Long-term forecasting using tensor-train RNNs"
Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
-
"Tensor Regression Networks"
Jean Kossaifi, Zack Chase Lipton, Aran Khanna, Tommaso Furlanello, Anima Anandkumar
-
"Compact Tensor Pooling for Visual Question Answering"
Yang Shi, Tommaso Furlanello, Anima Anandkumar
Appeared at CVPR 2017 VQA Workshop, Honolulu, Hawaii
-
"Analyzing tensor power method dynamics in overcomplete regime"
Anima Anandkumar, Rong Ge, Majid Janzamin
Appeared at JMLR
-
"A clustering approach to learning sparsely used overcomplete dictionaries"
Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
Appeared at IEEE Transactions on Information Theory 2017
-
"Tensor Contraction Layers for Parsimonious Deep Nets"
Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar
Appeared at CVPR 2017, Workshop, Honolulu, Hawaii
-
"Spectral Latent Dirichlet Allocation model on Spark"
Furong Huang, Jencir Lee, and Anima Anandkumar
-
"Reinforcement Learning in Rich-Observation MDPs using Spectral Methods"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at RLDM 2017, Ann Arbor, Michigan, USA
-
"Homotopy Analysis for Tensor PCA"
Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi
Appeared at COLT 2017, Amsterdam, Netherlands
-
"Spectral Methods for Correlated Topic Models"
Forough Arabshahi, Animashree Anandkumar
Appeared at the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54:1439-1447, 2017.
2016
-
"Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization"
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, and Praneeth Netrapalli
Appeared at SIAM J. Optim., 26(4), 2016.
-
"Experimental results: Reinforcement Learning of POMDPs using Spectral Methods"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at NIPS 2016, Barcelona, Spain, Workshop on Deep RL
-
"Online and Differentially-Private Tensor Decomposition"
Yining Wang, Anima Anandkumar
Appeared at NIPS 2016, Barcelona, Spain
-
"Tensor Contractions with Extended BLAS Kernels on CPU and GPU"
Yang Shi, U.N. Niranjan, Anima Anandkumar, Cris Cecka
Appeared at HiPC2016, Hyderabad, India, December 2016
-
"PhD Thesis: Stochastic Optimization in High Dimension"
Hanie Sedghi, 2016
-
"PhD Thesis: Discovery of Latent Factors in High-dimensional Data Using Tensor Methods"
Furong Huang, 2016
-
"PhD Thesis: Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods"
Majid Janzamin, 2016
-
"Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at COLT 2016, New York, USA
-
"Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition"
Furong Huang, A. Anandkumar
Appeared at ACL 2016
-
"Training Input-Output Recurrent Neural Networks through Spectral Methods"
H. Sedghi, A. Anandkumar
-
"Reinforcement Learning of POMDPs using Spectral Methods"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at COLT 2016, New York, USA
-
"Efficient approaches for escaping higher order saddle points in non-convex optimization"
Anima Anandkumar, Rong Ge
Appeared at COLT 2016, New York, USA
-
"Provable Tensor Methods for Learning Mixtures of Generalized Linear Models"
H. Sedghi, M. Janzamin, A. Anandkumar
Appeared at AISTATS 2016
-
"Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations"
Anima Anandkumar, Prateek Jain, Yang Shi, U.N. Niranjan
Appeared at AISTATS 2016
2015
-
"Feast at Play: Feature ExtrAction using Score function Tensors"
Majid Janzamin, Hanie Sedghi, U.N. Niranjan, Anima Anandkumar
Appeared at NIPS 2015
-
"Convolutional Dictionary Learning through Tensor Factorization"
Furong Huang, Anima Anandkumar
Appeared at NIPS 2015
-
"Scalable Latent Tree Model and its Application to Health Analytics"
F. Huang, U. N. Niranjan, J. Perros, R. Chen, J. Sun, A. Anandkumar
Appeared at NIPS 2015
-
"Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model"
F. Huang, A. Anandkumar, C. Borgs, J. Chayes, E. Fraenkel, M. Hawrylycz, E. Lein, A. Ingrosso, S. Turaga
Appeared at NIPS 2015
-
"Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models]"
Forough Arabshahi, Furong Huang, Anima Anandkumar, Carter T. Butts and Sean M. Fitzhugh
Appeared at International Conference on Data Mining (ICDM), IEEE 2015
-
"Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models"
Tejaswi Nimmagadda, Anima Anandkumar
Appeared at SUNw 2015
-
"Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Method"
Majid Janzamin, Hanie Sedghi, Anima Anandkumar
-
"Fast and Guaranteed Tensor Decomposition via Sketching"
Yining Wang, Hsiao-Yu Tung, Alex Smola, Anima Anandkuma
Appeared at NIPS 2015
-
"A Scale Mixture Perspective of Multiplicative Noise in Neural Networks"
Eric Nalisnick, Anima Anandkumar, Padhraic Smyth
-
"Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods"
A. Anandkumar, H. Sedghi
2014
-
"Online Tensor Methods for Learning Latent Variable Models"
F. Huang, U. N. Niranjan, M. U. Hakeem, A. Anandkumar
Appeared at JMLR 2014
-
"Score Function Features for Discriminative Learning: Matrix and Tensor Framework"
M. Janzamin, H. Sedghi, A. Anandkumar
Appeared at JMLR 2014
-
"Provable Methods for Training Neural Networks with Sparse Connectivity"
H. Sedghi, A. Anandkumar
Appeared at NIPS Deep Learning Workshop. Dec. 2014
-
"PAnalyzing Tensor Power Method Dynamics in Overcomplete Regime"
A. Anandkumar, R. Ge, M. Janzamin
-
"Non-convex Robust PCA"
P. Netrapalli, U. N. Niranjan, S. Sanghavi, A. Anandkumar, P. Jain.Â
An abridged version appears in NIPS 2014
-
"Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods"
A. Anandkumar, R. Ge, M. Janzamin
An abridge version appears in COLT 2015
-
"Multi-Step Stochastic ADMM in High Dimensions: Applications in Sparse Optimization and Noisy Matrix Decomposition"
H. Sedghi, A. Anandkumar, E. Jonckheere
Appeared at NIPS 2014
-
"Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates"
A. Anandkumar, R. Ge, M. Janzamin
Appeared at NIPS 2014
-
"Nonparametric Estimation of Multi-View Latent Variable Models"
L. Song, A. Anandkumar, B. Dai, B. Xie
Appeared at International Conference on Machine Learning (ICML) 2014
-
"Tensor Decompositions for Learning Latent Variable Models"
A. Anandkumar, R. Ge, D. Hsu, S.M. Kakade and M. Telgarsky.Â
Appeared at Journal of Machine Learning Research 2014
2013
- Learning Loopy Graphical Models with Latent Variables: Efficient Methods and Guarantees, Ann. Statist., 41(2), 2013. [PDF] supplement slides code
- Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization, 2013. [PDF] slides
- Exact Recovery of Sparsely Used Overcomplete Dictionaries, 2013. [PDF]
- When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity, 2013. [PDF]
- A Tensor Spectral Approach to Learning Mixed Membership Community Models, COLT 2013. [PDF] slides
- Learning Latent Bayesian Networks and Topic Models Under Expansion Constraints, International Conference on Machine Learning (ICML) 2013. [PDF]
- Fast, Concurrent and Distributed Load Balancing under Switching Costs and Imperfect Observations, IEEE INFOCOM 2013. [PDF]
2012
- High-Dimensional Structure Learning of Ising Models: Local Separation Criterion, Ann. Statist. 40(3), 2012. [PDF] supplement code slides
- Learning Linear Bayesian Networks with Latent Variables, 2012. [PDF]
- Feedback Message Passing for Inference in Gaussian Graphical Models, IEEE Trans. on Signal Processing, 60(8), 2012. [PDF]
- High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains, International Conference on Machine Learning (ICML) 2012. [PDF] [PDF] slides
- A Method of Moments for Mixture Models and Hidden Markov Models, COLT 2012. [PDF] [PDF]
- Learning High-Dimensional Mixtures of Graphical Models, NIPS 2012. [PDF] [PDF]
- Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation, NIPS 2012. [PDF] [PDF]
2011
- High-Dimensional Gaussian Graphical Model Selection: Walk-Summability and Local Separation Criterion, JMLR 13:229-337, 2012. NIPS 2011. [PDF] [PDF] talk slides
- High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions, NIPS 2011. [PDF]
- Spectral Methods for Learning Multivariate Latent Tree Structure, NIPS 2011. [PDF]
- Robust Rate Maximization Game Under Bounded Channel Uncertainty, IEEE Trans. Vehicular Technology, 60:9, 2011. [PDF]
- Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing, IEEE ISIT 2011. [PDF]
- Topology Discovery of Sparse Random Graphs With Few Participants, ACM SIGMETRICS 2011. [PDF] [PDF] slides
- Learning Latent Tree Graphical Models, JMLR 2011. [PDF] project homepage Allerton version Allerton slides seminar slides
- Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates, JMLR 2011. [PDF] Allerton version Allerton slides
- Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret, IEEE JSAC 2011. [PDF]
- Energy-Latency Tradeoff for In-Network Function Computation in Random Networks, IEEE INFOCOM 2011. [PDF] slides
- Index-Based Sampling Policies for Tracking Dynamic Networks under Sampling Constraints, IEEE INFOCOM 2011. [PDF] supplemental material
- A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures, IEEE Trans. Information Theory,57:3, 2011. [PDF]
2010
- Scaling Laws for Random Spatial Graphical Models, IEEE ISIT 2010. [PDF] slides
- Error Exponents for Composite Hypothesis Testing of Markov Forest Distributions, IEEE ISIT 2010. [PDF] slides proofs
- Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures, IEEE Trans. Signal Processing, 58:5, 2010. [PDF] slides
- Robust Rate Maximization Game Under Bounded Channel Uncertainty, IEEE ICASSP 2010. [PDF]
- Opportunistic Spectrum Access with Multiple Users: Learning under Competition, IEEE INFOCOM 2010. [PDF] slides
- Seeing Through Black Boxes : Tracking Transactions through Queues under Monitoring Resource Constraints, Elsevier Performance Evaluation 2010. [PDF]
2009
- Energy Scaling Laws for Distributed Inference in Random Fusion Networks, IEEE JSAC, 27:7, 2009. [PDF]
- Selectively Retrofitting Monitoring in Distributed Systems, Workshop on MAMA 2009. [PDF]
- Detection Error Exponent for Spatially Dependent Samples in Random Networks, IEEE ISIT 2009. [PDF]
- Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference, IEEE INFOCOM 2009. [PDF] tech report
- Detection of Gauss-Markov random fields with nearest-neighbor dependency, IEEE Trans. Information Theory, 55:2, 2009. [PDF]
2008 and Earlier
- Optimal Node Density for Detection in Energy Constrained Random Networks, IEEE Trans. Signal Processing, 56:10, 2008. [PDF]
- Distributed Estimation Via Random Access, IEEE Trans. Information Theory, 54:7, 2008. [PDF]
- Tracking in a Spaghetti Bowl: Monitoring Transactions Using Footprints, ACM SIGMETRICS 2008. [PDF]
- Minimum Cost Data Aggregation with Localized Processing for Statistical Inference, IEEE INFOCOM 2008. [PDF]
- A Large Deviation Analysis of Detection over Multi-Access Channels with Random Number of Sensors, ICASSP 2006 (Best Paper Award). [PDF]