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Author Information
Anish Acharya (UT Austin)
Sujay Sanghavi (UT Austin)
Li Jing (Facebook)
Bhargav Bhushanam (Facebook)
Michael Rabbat (Facebook)
Dhruv Choudhary (Facebook Inc.)
Inderjit Dhillon (UT Austin & Amazon)
Inderjit Dhillon is the Gottesman Family Centennial Professor of Computer Science and Mathematics at UT Austin, where he is also the Director of the ICES Center for Big Data Analytics. His main research interests are in big data, machine learning, network analysis, linear algebra and optimization. He received his B.Tech. degree from IIT Bombay, and Ph.D. from UC Berkeley. Inderjit has received several awards, including the ICES Distinguished Research Award, the SIAM Outstanding Paper Prize, the Moncrief Grand Challenge Award, the SIAM Linear Algebra Prize, the University Research Excellence Award, and the NSF Career Award. He has published over 160 journal and conference papers, and has served on the Editorial Board of the Journal of Machine Learning Research, the IEEE Transactions of Pattern Analysis and Machine Intelligence, Foundations and Trends in Machine Learning and the SIAM Journal for Matrix Analysis and Applications. Inderjit is an ACM Fellow, an IEEE Fellow, a SIAM Fellow and an AAAS Fellow.
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2023 : UCB Provably Learns From Inconsistent Human Feedback »
Shuo Yang · Tongzheng Ren · Inderjit Dhillon · Sujay Sanghavi -
2023 : Contextual Set Selection Under Human Feedback With Model Misspecification »
Shuo Yang · Rajat Sen · Sujay Sanghavi -
2023 : Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity »
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2023 Poster: Beyond Uniform Lipschitz Condition in Differentially Private Optimization »
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2023 Poster: Understanding Self-Distillation in the Presence of Label Noise »
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2022 Poster: Federated Learning with Partial Model Personalization »
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2022 Poster: Asymptotically-Optimal Gaussian Bandits with Side Observations »
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2022 Spotlight: Federated Learning with Partial Model Personalization »
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2022 Spotlight: Asymptotically-Optimal Gaussian Bandits with Side Observations »
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2022 Poster: Linear Bandit Algorithms with Sublinear Time Complexity »
Shuo Yang · Tongzheng Ren · Sanjay Shakkottai · Eric Price · Inderjit Dhillon · Sujay Sanghavi -
2022 Spotlight: Linear Bandit Algorithms with Sublinear Time Complexity »
Shuo Yang · Tongzheng Ren · Sanjay Shakkottai · Eric Price · Inderjit Dhillon · Sujay Sanghavi -
2021 Poster: Barlow Twins: Self-Supervised Learning via Redundancy Reduction »
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2021 Spotlight: Barlow Twins: Self-Supervised Learning via Redundancy Reduction »
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2021 Poster: Top-k eXtreme Contextual Bandits with Arm Hierarchy »
Rajat Sen · Alexander Rakhlin · Lexing Ying · Rahul Kidambi · Dean Foster · Daniel Hill · Inderjit Dhillon -
2021 Spotlight: Top-k eXtreme Contextual Bandits with Arm Hierarchy »
Rajat Sen · Alexander Rakhlin · Lexing Ying · Rahul Kidambi · Dean Foster · Daniel Hill · Inderjit Dhillon -
2020 : Discussion Panel »
Krzysztof Dembczynski · Prateek Jain · Alina Beygelzimer · Inderjit Dhillon · Anna Choromanska · Maryam Majzoubi · Yashoteja Prabhu · John Langford -
2020 : Invited Talk 5 Q&A - Inderjit Dhillon »
Inderjit Dhillon -
2020 : Invited Talk 5 - Multi-Output Prediction: Theory and Practice - Inderjit Dhillon »
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2020 Poster: Learning to Encode Position for Transformer with Continuous Dynamical Model »
Xuanqing Liu · Hsiang-Fu Yu · Inderjit Dhillon · Cho-Jui Hsieh -
2020 Poster: On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings »
Mahmoud Assran · Michael Rabbat -
2020 Poster: Extreme Multi-label Classification from Aggregated Labels »
Yanyao Shen · Hsiang-Fu Yu · Sujay Sanghavi · Inderjit Dhillon -
2019 Poster: TarMAC: Targeted Multi-Agent Communication »
Abhishek Das · Theophile Gervet · Joshua Romoff · Dhruv Batra · Devi Parikh · Michael Rabbat · Joelle Pineau -
2019 Oral: TarMAC: Targeted Multi-Agent Communication »
Abhishek Das · Theophile Gervet · Joshua Romoff · Dhruv Batra · Devi Parikh · Michael Rabbat · Joelle Pineau -
2019 Poster: Stochastic Gradient Push for Distributed Deep Learning »
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2019 Poster: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling »
Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi · Felix Xinnan Yu · Daniel Holtmann-Rice · Dmitry Storcheus · Afshin Rostamizadeh · Sanjiv Kumar -
2019 Poster: Learning with Bad Training Data via Iterative Trimmed Loss Minimization »
Yanyao Shen · Sujay Sanghavi -
2019 Oral: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling »
Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi · Felix Xinnan Yu · Daniel Holtmann-Rice · Dmitry Storcheus · Afshin Rostamizadeh · Sanjiv Kumar -
2019 Oral: Stochastic Gradient Push for Distributed Deep Learning »
Mahmoud Assran · Nicolas Loizou · Nicolas Ballas · Michael Rabbat -
2019 Oral: Learning with Bad Training Data via Iterative Trimmed Loss Minimization »
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2018 Poster: Learning long term dependencies via Fourier recurrent units »
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2018 Poster: Towards Fast Computation of Certified Robustness for ReLU Networks »
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2018 Oral: Towards Fast Computation of Certified Robustness for ReLU Networks »
Tsui-Wei Weng · Huan Zhang · Hongge Chen · Zhao Song · Cho-Jui Hsieh · Luca Daniel · Duane Boning · Inderjit Dhillon -
2018 Oral: Learning long term dependencies via Fourier recurrent units »
Jiong Zhang · Yibo Lin · Zhao Song · Inderjit Dhillon -
2018 Poster: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
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2018 Oral: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
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2017 Poster: Gradient Boosted Decision Trees for High Dimensional Sparse Output »
Si Si · Huan Zhang · Sathiya Keerthi · Dhruv Mahajan · Inderjit Dhillon · Cho-Jui Hsieh -
2017 Talk: Gradient Boosted Decision Trees for High Dimensional Sparse Output »
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2017 Poster: Recovery Guarantees for One-hidden-layer Neural Networks »
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2017 Poster: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
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2017 Talk: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
Qi Lei · En-Hsu Yen · Chao-Yuan Wu · Inderjit Dhillon · Pradeep Ravikumar -
2017 Talk: Recovery Guarantees for One-hidden-layer Neural Networks »
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