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ORCIDInderjit S. Dhillon
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- affiliation: University of Texas at Austin, USA
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2020 – today
- 2024
[j49]Haoya Li
, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon:
Accelerating Primal-Dual Methods for Regularized Markov Decision Processes. SIAM J. Optim. 34(1): 764-789 (2024)- [c167]Cho-Jui Hsieh, Si Si, Felix Yu, Inderjit S. Dhillon:
Automatic Engineering of Long Prompts. ACL (Findings) 2024: 10672-10685 - [c166]Sai Surya Duvvuri, Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S. Dhillon:
Combining Axes Preconditioners through Kronecker Approximation for Deep Learning. ICLR 2024 - [c165]Nilesh Gupta, Devvrit, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon:
Dual-Encoders for Extreme Multi-label Classification. ICLR 2024 - [c164]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Two-stage LLM Fine-tuning with Less Specialization and More Generalization. ICLR 2024 - [i80]Rudrajit Das, Naman Agarwal, Sujay Sanghavi, Inderjit S. Dhillon:
Towards Quantifying the Preconditioning Effect of Adam. CoRR abs/2402.07114 (2024) - [i79]Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong:
Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR abs/2406.11206 (2024) - [i78]Anish Acharya, Inderjit S. Dhillon, Sujay Sanghavi:
Geometric Median (GM) Matching for Robust Data Pruning. CoRR abs/2406.17188 (2024) - [i77]Ruochen Wang, Si Si, Felix Yu, Dorothea Wiesmann, Cho-Jui Hsieh, Inderjit S. Dhillon:
Large Language Models are Interpretable Learners. CoRR abs/2406.17224 (2024) - 2023
- [j48]Haoya Li, Samarth Gupta, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon:
Approximate Newton Policy Gradient Algorithms. SIAM J. Sci. Comput. 45(5): 2585- (2023) - [c163]Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei:
Sample Efficiency of Data Augmentation Consistency Regularization. AISTATS 2023: 3825-3853 - [c162]Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon:
A Computationally Efficient Sparsified Online Newton Method. NeurIPS 2023 - [c161]Jui-Nan Yen, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh:
Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization. NeurIPS 2023 - [c160]Patrick H. Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. WWW 2023: 3225-3235 - [i76]Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain:
MatFormer: Nested Transformer for Elastic Inference. CoRR abs/2310.07707 (2023) - [i75]Ramnath Kumar, Anshul Mittal, Nilesh Gupta, Aditya Kusupati, Inderjit S. Dhillon, Prateek Jain:
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval. CoRR abs/2310.08891 (2023) - [i74]Nilesh Gupta, Devvrit Khatri, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon:
Efficacy of Dual-Encoders for Extreme Multi-Label Classification. CoRR abs/2310.10636 (2023) - [i73]Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon:
A Computationally Efficient Sparsified Online Newton Method. CoRR abs/2311.10085 (2023) - [i72]Cho-Jui Hsieh, Si Si, Felix X. Yu, Inderjit S. Dhillon:
Automatic Engineering of Long Prompts. CoRR abs/2311.10117 (2023) - 2022
- [j47]Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon:
PECOS: Prediction for Enormous and Correlated Output Spaces. J. Mach. Learn. Res. 23: 98:1-98:32 (2022) - [j46]Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo, Sujay Sanghavi, Inderjit S. Dhillon:
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. IEEE Trans. Parallel Distributed Syst. 33(11): 2727-2739 (2022) - [c159]Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. AISTATS 2022: 11145-11168 - [c158]Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon:
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. ICLR 2022 - [c157]Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi:
Linear Bandit Algorithms with Sublinear Time Complexity. ICML 2022: 25241-25260 - [c156]Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, Cho-Jui Hsieh:
CAT: Customized Adversarial Training for Improved Robustness. IJCAI 2022: 673-679 - [c155]Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
Extreme Zero-Shot Learning for Extreme Text Classification. NAACL-HLT 2022: 5455-5468 - [c154]Devvrit, Aditya Sinha, Inderjit S. Dhillon, Prateek Jain:
S3GC: Scalable Self-Supervised Graph Clustering. NeurIPS 2022 - [c153]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces. NeurIPS 2022 - [c152]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR 2022: 791-802 - [c151]Rudrajit Das, Anish Acharya, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Faster non-convex federated learning via global and local momentum. UAI 2022: 496-506 - [c150]Philip A. Etter, Kai Zhong, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon:
Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees. WWW 2022: 452-461 - [i71]Haoya Li, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon:
Accelerating Primal-dual Methods for Regularized Markov Decision Processes. CoRR abs/2202.10506 (2022) - [i70]Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei:
Sample Efficiency of Data Augmentation Consistency Regularization. CoRR abs/2202.12230 (2022) - [i69]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR abs/2204.10936 (2022) - [i68]Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael G. Rabbat, Inderjit S. Dhillon:
Positive Unlabeled Contrastive Learning. CoRR abs/2206.01206 (2022) - [i67]Patrick H. Chen, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. CoRR abs/2206.11408 (2022) - [i66]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
End-to-End Learning to Index and Search in Large Output Spaces. CoRR abs/2210.08410 (2022) - [i65]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Preserving In-Context Learning ability in Large Language Model Fine-tuning. CoRR abs/2211.00635 (2022) - 2021
- [c149]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. AAAI 2021: 8732-8740 - [c148]Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, Inderjit S. Dhillon:
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification. CIKM 2021: 3717-3726 - [c147]Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel N. Hill, Inderjit S. Dhillon:
Top-k eXtreme Contextual Bandits with Arm Hierarchy. ICML 2021: 9422-9433 - [c146]Wei-Cheng Chang, Daniel L. Jiang, Hsiang-Fu Yu, Choon-Hui Teo, Jiong Zhang, Kai Zhong, Kedarnath Kolluri, Qie Hu, Nikhil Shandilya, Vyacheslav Ievgrafov, Japinder Singh, Inderjit S. Dhillon:
Extreme Multi-label Learning for Semantic Matching in Product Search. KDD 2021: 2643-2651 - [c145]Nishant Yadav, Rajat Sen, Daniel N. Hill, Arya Mazumdar, Inderjit S. Dhillon:
Session-Aware Query Auto-completion using Extreme Multi-Label Ranking. KDD 2021: 3835-3844 - [c144]Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon:
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification. NeurIPS 2021: 7267-7280 - [c143]Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Label Disentanglement in Partition-based Extreme Multilabel Classification. NeurIPS 2021: 15359-15369 - [c142]Patrick H. Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
DRONE: Data-aware Low-rank Compression for Large NLP Models. NeurIPS 2021: 29321-29334 - [i64]Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon:
Top-k eXtreme Contextual Bandits with Arm Hierarchy. CoRR abs/2102.07800 (2021) - [i63]Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi:
Linear Bandit Algorithms with Sublinear Time Complexity. CoRR abs/2103.02729 (2021) - [i62]Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon, Sujay Sanghavi:
Combinatorial Bandits without Total Order for Arms. CoRR abs/2103.02741 (2021) - [i61]Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, Inderjit S. Dhillon:
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification. CoRR abs/2106.00730 (2021) - [i60]Philip A. Etter, Kai Zhong, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon:
Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees. CoRR abs/2106.02697 (2021) - [i59]Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon:
DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning. CoRR abs/2106.07094 (2021) - [i58]Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. CoRR abs/2106.08882 (2021) - [i57]Wei-Cheng Chang, Daniel L. Jiang, Hsiang-Fu Yu, Choon-Hui Teo, Jiong Zhang, Kai Zhong, Kedarnath Kolluri, Qie Hu, Nikhil Shandilya, Vyacheslav Ievgrafov, Japinder Singh, Inderjit S. Dhillon:
Extreme Multi-label Learning for Semantic Matching in Product Search. CoRR abs/2106.12657 (2021) - [i56]Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Label Disentanglement in Partition-based Extreme Multilabel Classification. CoRR abs/2106.12751 (2021) - [i55]Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon:
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification. CoRR abs/2110.00685 (2021) - [i54]Haoya Li, Samarth Gupta, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon:
Quasi-Newton policy gradient algorithms. CoRR abs/2110.02398 (2021) - [i53]Reese Pathak, Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, Inderjit S. Dhillon:
Cluster-and-Conquer: A Framework For Time-Series Forecasting. CoRR abs/2110.14011 (2021) - [i52]Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon:
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. CoRR abs/2111.00064 (2021) - [i51]Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
Extreme Zero-Shot Learning for Extreme Text Classification. CoRR abs/2112.08652 (2021) - 2020
- [c141]Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
Learning to Encode Position for Transformer with Continuous Dynamical Model. ICML 2020: 6327-6335 - [c140]Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon:
Extreme Multi-label Classification from Aggregated Labels. ICML 2020: 8752-8762 - [c139]Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong, Yiming Yang, Inderjit S. Dhillon:
Taming Pretrained Transformers for Extreme Multi-label Text Classification. KDD 2020: 3163-3171 - [c138]Joyce Jiyoung Whang, Yeonsung Jung, Seonggoo Kang, Dongho Yoo, Inderjit S. Dhillon:
Scalable Anti-TrustRank with Qualified Site-level Seeds for Link-based Web Spam Detection. WWW (Companion Volume) 2020: 593-602 - [e2]Inderjit S. Dhillon, Dimitris S. Papailiopoulos, Vivienne Sze:
Proceedings of the Third Conference on Machine Learning and Systems, MLSys 2020, Austin, TX, USA, March 2-4, 2020. mlsys.org 2020 [contents] - [i50]Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, Cho-Jui Hsieh:
CAT: Customized Adversarial Training for Improved Robustness. CoRR abs/2002.06789 (2020) - [i49]Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
Learning to Encode Position for Transformer with Continuous Dynamical Model. CoRR abs/2003.09229 (2020) - [i48]Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon:
Extreme Multi-label Classification from Aggregated Labels. CoRR abs/2004.00198 (2020) - [i47]Joyce Jiyoung Whang, Inderjit S. Dhillon:
Non-Exhaustive, Overlapping Co-Clustering: An Extended Analysis. CoRR abs/2004.11530 (2020) - [i46]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. CoRR abs/2009.12947 (2020) - [i45]Hsiang-Fu Yu, Kai Zhong, Inderjit S. Dhillon:
PECOS: Prediction for Enormous and Correlated Output Spaces. CoRR abs/2010.05878 (2020) - [i44]Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo, Sujay Sanghavi, Inderjit S. Dhillon:
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization. CoRR abs/2011.10643 (2020) - [i43]Devvrit, Minhao Cheng, Cho-Jui Hsieh, Inderjit S. Dhillon:
Voting based ensemble improves robustness of defensive models. CoRR abs/2011.14031 (2020) - [i42]Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon:
Improved Convergence Rates for Non-Convex Federated Learning with Compression. CoRR abs/2012.04061 (2020) - [i41]Nishant Yadav, Rajat Sen, Daniel N. Hill, Arya Mazumdar, Inderjit S. Dhillon:
Session-Aware Query Auto-completion using Extreme Multi-label Ranking. CoRR abs/2012.07654 (2020)
2010 – 2019
- 2019
- [j45]Joyce Jiyoung Whang, Yangyang Hou, David F. Gleich, Inderjit S. Dhillon:
Non-Exhaustive, Overlapping Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41(11): 2644-2659 (2019) - [c137]Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit S. Dhillon:
Online Embedding Compression for Text Classification Using Low Rank Matrix Factorization. AAAI 2019: 6196-6203 - [c136]Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon:
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models. AISTATS 2019: 935-943 - [c135]Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables. AISTATS 2019: 2641-2649 - [c134]Inderjit S. Dhillon:
Abstract of the Keynotes. IC3 2019: 1-2 - [c133]Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, Cho-Jui Hsieh:
The Limitations of Adversarial Training and the Blind-Spot Attack. ICLR (Poster) 2019 - [c132]Qi Lei, Jinfeng Yi, Roman Vaculín, Lingfei Wu, Inderjit S. Dhillon:
Similarity Preserving Representation Learning for Time Series Clustering. IJCAI 2019: 2845-2851 - [c131]Qi Lei, Lingfei Wu, Pin-Yu Chen, Alex Dimakis, Inderjit S. Dhillon, Michael Witbrock:
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification. SysML 2019 - [c130]Rajat Sen, Hsiang-Fu Yu, Inderjit S. Dhillon:
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting. NeurIPS 2019: 4838-4847 - [c129]Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon:
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks. NeurIPS 2019: 5996-6006 - [c128]Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon:
Provable Non-linear Inductive Matrix Completion. NeurIPS 2019: 11435-11445 - [c127]Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis:
Primal-Dual Block Generalized Frank-Wolfe. NeurIPS 2019: 13866-13875 - [c126]Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis:
Inverting Deep Generative models, One layer at a time. NeurIPS 2019: 13910-13919 - [i40]Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, Cho-Jui Hsieh:
The Limitations of Adversarial Training and the Blind-Spot Attack. CoRR abs/1901.04684 (2019) - [i39]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i38]Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong, Yiming Yang, Inderjit S. Dhillon:
A Modular Deep Learning Approach for Extreme Multi-label Text Classification. CoRR abs/1905.02331 (2019) - [i37]Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon:
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks. CoRR abs/1905.03381 (2019) - [i36]Rajat Sen, Hsiang-Fu Yu, Inderjit S. Dhillon:
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting. CoRR abs/1905.03806 (2019) - [i35]Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis:
Primal-Dual Block Frank-Wolfe. CoRR abs/1906.02436 (2019) - [i34]Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis:
Inverting Deep Generative models, One layer at a time. CoRR abs/1906.07437 (2019) - [i33]Vikas K. Garg, Inderjit S. Dhillon, Hsiang-Fu Yu:
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure. CoRR abs/1908.10408 (2019) - 2018
- [j44]Kai-Yang Chiang, Inderjit S. Dhillon, Cho-Jui Hsieh:
Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations. J. Mach. Learn. Res. 19: 76:1-76:35 (2018) - [c125]Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane S. Boning, Inderjit S. Dhillon:
Towards Fast Computation of Certified Robustness for ReLU Networks. ICML 2018: 5273-5282 - [c124]Jiong Zhang, Qi Lei, Inderjit S. Dhillon:
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization. ICML 2018: 5801-5809 - [c123]Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon:
Learning Long Term Dependencies via Fourier Recurrent Units. ICML 2018: 5810-5818 - [c122]Po-Wei Wang, Huan Zhang, Vijai Mohan, Inderjit S. Dhillon, J. Zico Kolter:
Realtime Query Completion via Deep Language Models. eCOM@SIGIR 2018 - [i32]Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon:
Learning Long Term Dependencies via Fourier Recurrent Units. CoRR abs/1803.06585 (2018) - [i31]Jiong Zhang, Qi Lei, Inderjit S. Dhillon:
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization. CoRR abs/1803.09327 (2018) - [i30]Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane S. Boning, Inderjit S. Dhillon, Luca Daniel:
Towards Fast Computation of Certified Robustness for ReLU Networks. CoRR abs/1804.09699 (2018) - [i29]Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon:
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks. CoRR abs/1805.10477 (2018) - [i28]Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit S. Dhillon:
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization. CoRR abs/1811.00641 (2018) - [i27]Qi Lei, Lingfei Wu, Pin-Yu Chen, Alexandros G. Dimakis, Inderjit S. Dhillon, Michael Witbrock:
Discrete Attacks and Submodular Optimization with Applications to Text Classification. CoRR abs/1812.00151 (2018) - 2017
- [j43]Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Memory Efficient Kernel Approximation. J. Mach. Learn. Res. 18: 20:1-20:32 (2017) - [j42]Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Cost-Sensitive Learning with Noisy Labels. J. Mach. Learn. Res. 18: 155:1-155:33 (2017) - [j41]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Partial Hard Thresholding. IEEE Trans. Inf. Theory 63(5): 3029-3038 (2017) - [c121]Hsiang-Fu Yu, Hsin-Yuan Huang, Inderjit S. Dhillon, Chih-Jen Lin:
A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information. AAAI 2017: 2845-2851 - [c120]Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Rank Aggregation and Prediction with Item Features. AISTATS 2017: 748-756 - [c119]Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon:
Fast Classification with Binary Prototypes. AISTATS 2017: 1255-1263 - [c118]Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon:
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. AISTATS 2017: 1514-1522 - [c117]Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon:
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. AISTATS 2017: 1550-1559 - [c116]Joyce Jiyoung Whang, Inderjit S. Dhillon:
Non-Exhaustive, Overlapping Co-Clustering. CIKM 2017: 2367-2370 - [c115]Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon