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ORCIDSanjeev Arora
2020 – today
- 2025
[c123]Simran Kaur, Simon Park, Anirudh Goyal, Sanjeev Arora:
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning. ICLR 2025- [c122]Noam Razin, Sadhika Malladi, Adithya Bhaskar, Danqi Chen, Sanjeev Arora, Boris Hanin:
Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization. ICLR 2025 - [c121]Stanley Wei, Sadhika Malladi, Sanjeev Arora, Amartya Sanyal:
Provable unlearning in topic modeling and downstream tasks. ICLR 2025 - [i98]Simon Park, Abhishek Panigrahi, Yun Cheng, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs? CoRR abs/2501.02669 (2025) - [i97]Yikai Wu, Haoyu Zhao, Sanjeev Arora:
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set. CoRR abs/2502.03669 (2025) - [i96]Yong Lin, Shange Tang, Bohan Lyu, Jiayun Wu, Hongzhou Lin, Kaiyu Yang, Jia Li, Mengzhou Xia, Danqi Chen, Sanjeev Arora, Chi Jin:
Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving. CoRR abs/2502.07640 (2025) - [i95]Xingyu Zhu, Abhishek Panigrahi, Sanjeev Arora:
On the Power of Context-Enhanced Learning in LLMs. CoRR abs/2503.01821 (2025) - [i94]Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro:
Weak-to-Strong Generalization Even in Random Feature Networks, Provably. CoRR abs/2503.02877 (2025) - [i93]Noam Razin, Zixuan Wang, Hubert Strauss, Stanley Wei, Jason D. Lee, Sanjeev Arora:
What Makes a Reward Model a Good Teacher? An Optimization Perspective. CoRR abs/2503.15477 (2025) - 2024
- [c120]Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang:
A Quadratic Synchronization Rule for Distributed Deep Learning. ICLR 2024 - [c119]Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora:
SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models. ICLR 2024 - [c118]Alexis Chevalier, Jiayi Geng, Alexander Wettig, Howard Chen, Sebastian Mizera, Toni Annala, Max Jameson Aragon, Arturo Rodríguez Fanlo, Simon Frieder, Simon Machado, Akshara Prabhakar, Ellie Thieu, Jiachen T. Wang, Zirui Wang, Xindi Wu, Mengzhou Xia, Wenhan Xia, Jiatong Yu, Junjie Zhu, Zhiyong Jason Ren, Sanjeev Arora, Danqi Chen:
Language Models as Science Tutors. ICML 2024 - [c117]Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora:
Trainable Transformer in Transformer. ICML 2024 - [c116]Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, Danqi Chen:
LESS: Selecting Influential Data for Targeted Instruction Tuning. ICML 2024 - [c115]Sanjeev Arora
:
From Word-prediction to Complex Skills: Compositional Thinking and Metacognition in LLMs. KDD 2024: 1 - [c114]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo Jimenez Rezende, Yoshua Bengio, Michael C. Mozer, Sanjeev Arora:
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. NeurIPS 2024 - [c113]Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. NeurIPS 2024 - [c112]Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, Alexis Chevalier, Sanjeev Arora, Danqi Chen:
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs. NeurIPS 2024 - [c111]Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, Sanjeev Arora:
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty. NeurIPS 2024 - [c110]Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Can Models Learn Skill Composition from Examples? NeurIPS 2024 - [i92]Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, Danqi Chen:
LESS: Selecting Influential Data for Targeted Instruction Tuning. CoRR abs/2402.04333 (2024) - [i91]Alexis Chevalier, Jiayi Geng, Alexander Wettig, Howard Chen, Sebastian Mizera, Toni Annala, Max Jameson Aragon, Arturo Rodríguez Fanlo, Simon Frieder, Simon Machado, Akshara Prabhakar, Ellie Thieu, Jiachen T. Wang, Zirui Wang, Xindi Wu, Mengzhou Xia, Wenhan Jia, Jiatong Yu, Jun-Jie Zhu, Zhiyong Jason Ren, Sanjeev Arora, Danqi Chen:
Language Models as Science Tutors. CoRR abs/2402.11111 (2024) - [i90]Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. CoRR abs/2402.18540 (2024) - [i89]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora:
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR abs/2405.12205 (2024) - [i88]Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, Alexis Chevalier, Sanjeev Arora, Danqi Chen:
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs. CoRR abs/2406.18521 (2024) - [i87]Vedant Shah, Dingli Yu, Kaifeng Lyu, Simon Park, Nan Rosemary Ke, Michael Mozer, Yoshua Bengio, Sanjeev Arora, Anirudh Goyal:
AI-Assisted Generation of Difficult Math Questions. CoRR abs/2407.21009 (2024) - [i86]Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, Sanjeev Arora:
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty. CoRR abs/2408.14339 (2024) - [i85]Simran Kaur, Simon Park, Anirudh Goyal, Sanjeev Arora:
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning. CoRR abs/2408.14774 (2024) - [i84]Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Can Models Learn Skill Composition from Examples? CoRR abs/2409.19808 (2024) - [i83]Noam Razin, Sadhika Malladi, Adithya Bhaskar, Danqi Chen, Sanjeev Arora, Boris Hanin:
Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization. CoRR abs/2410.08847 (2024) - [i82]Stanley Wei, Sadhika Malladi, Sanjeev Arora, Amartya Sanyal:
Provable unlearning in topic modeling and downstream tasks. CoRR abs/2411.12600 (2024) - 2023
- [c109]Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora:
Do Transformers Parse while Predicting the Masked Word? EMNLP 2023: 16513-16542 - [c108]Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora:
Why (and When) does Local SGD Generalize Better than SGD? ICLR 2023 - [c107]Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora:
Understanding Influence Functions and Datamodels via Harmonic Analysis. ICLR 2023 - [c106]Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora:
A Kernel-Based View of Language Model Fine-Tuning. ICML 2023: 23610-23641 - [c105]Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora:
Task-Specific Skill Localization in Fine-tuned Language Models. ICML 2023: 27011-27033 - [c104]Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora:
Fine-Tuning Language Models with Just Forward Passes. NeurIPS 2023 - [i81]Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora:
Task-Specific Skill Localization in Fine-tuned Language Models. CoRR abs/2302.06600 (2023) - [i80]Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora:
Why (and When) does Local SGD Generalize Better than SGD? CoRR abs/2303.01215 (2023) - [i79]Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora:
Do Transformers Parse while Predicting the Masked Word? CoRR abs/2303.08117 (2023) - [i78]Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora:
Fine-Tuning Language Models with Just Forward Passes. CoRR abs/2305.17333 (2023) - [i77]Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora:
Trainable Transformer in Transformer. CoRR abs/2307.01189 (2023) - [i76]Sanjeev Arora, Anirudh Goyal:
A Theory for Emergence of Complex Skills in Language Models. CoRR abs/2307.15936 (2023) - [i75]Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang:
A Quadratic Synchronization Rule for Distributed Deep Learning. CoRR abs/2310.14423 (2023) - [i74]Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora:
Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models. CoRR abs/2310.17567 (2023) - [i73]Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal:
Unlearning via Sparse Representations. CoRR abs/2311.15268 (2023) - 2022
- [c103]Zhiyuan Li, Tianhao Wang, Sanjeev Arora:
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework. ICLR 2022 - [c102]Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora:
On Predicting Generalization using GANs. ICLR 2022 - [c101]Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi:
Understanding Gradient Descent on the Edge of Stability in Deep Learning. ICML 2022: 948-1024 - [c100]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML 2022: 19250-19286 - [c99]Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora:
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. NeurIPS 2022 - [c98]Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora:
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. NeurIPS 2022 - [c97]Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora:
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. NeurIPS 2022 - [c96]Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora:
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. NeurIPS 2022 - [i72]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR abs/2202.14037 (2022) - [i71]Zhou Lu, Wenhan Xia, Sanjeev Arora, Elad Hazan:
Adaptive Gradient Methods with Local Guarantees. CoRR abs/2203.01400 (2022) - [i70]Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi:
Understanding Gradient Descent on Edge of Stability in Deep Learning. CoRR abs/2205.09745 (2022) - [i69]Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora:
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. CoRR abs/2205.10287 (2022) - [i68]Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora:
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. CoRR abs/2206.07085 (2022) - [i67]Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora:
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. CoRR abs/2207.04036 (2022) - [i66]Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora:
Understanding Influence Functions and Datamodels via Harmonic Analysis. CoRR abs/2210.01072 (2022) - [i65]Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora:
A Kernel-Based View of Language Model Fine-Tuning. CoRR abs/2210.05643 (2022) - [i64]Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora:
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. CoRR abs/2211.02912 (2022) - 2021
- [j38]Sanjeev Arora:
Technical perspective: Why don't today's deep nets overfit to their training data? Commun. ACM 64(3): 106 (2021) - [c95]Zhiyuan Li, Yi Zhang, Sanjeev Arora:
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? ICLR 2021 - [c94]Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora:
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. ICLR 2021 - [c93]Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora:
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. NeurIPS 2021: 7232-7241 - [c92]Zhiyuan Li, Sadhika Malladi, Sanjeev Arora:
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs). NeurIPS 2021: 12712-12725 - [c91]Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora:
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. NeurIPS 2021: 12978-12991 - [c90]Sanjeev Arora:
Opening the Black Box of Deep Learning: Some Lessons and Take-aways. SIGMETRICS (Abstracts) 2021: 1 - [i63]Zhiyuan Li, Sadhika Malladi, Sanjeev Arora:
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs). CoRR abs/2102.12470 (2021) - [i62]Sanjeev Arora, Yi Zhang:
Rip van Winkle's Razor: A Simple Estimate of Overfit to Test Data. CoRR abs/2102.13189 (2021) - [i61]Zhiyuan Li, Tianhao Wang, Sanjeev Arora:
What Happens after SGD Reaches Zero Loss? -A Mathematical Framework. CoRR abs/2110.06914 (2021) - [i60]Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora:
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. CoRR abs/2110.13905 (2021) - [i59]Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora:
On Predicting Generalization using GANs. CoRR abs/2111.14212 (2021) - [i58]Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora:
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. CoRR abs/2112.00059 (2021) - 2020
- [c89]Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora:
TextHide: Tackling Data Privacy for Language Understanding Tasks. EMNLP (Findings) 2020: 1368-1382 - [c88]Sanjeev Arora:
The Quest for Mathematical Understanding of Deep Learning (Invited Talk). FSTTCS 2020: 1:1-1:1 - [c87]Zhiyuan Li, Sanjeev Arora:
An Exponential Learning Rate Schedule for Deep Learning. ICLR 2020 - [c86]Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu:
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. ICLR 2020 - [c85]Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi:
Provable Representation Learning for Imitation Learning via Bi-level Optimization. ICML 2020: 367-376 - [c84]Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora:
InstaHide: Instance-hiding Schemes for Private Distributed Learning. ICML 2020: 4507-4518 - [c83]Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora:
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. ICML 2020: 8512-8521 - [c82]Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora:
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. NeurIPS 2020 - [c81]Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora:
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. NeurIPS 2020 - [i57]Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora:
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. CoRR abs/2002.06668 (2020) - [i56]Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi:
Provable Representation Learning for Imitation Learning via Bi-level Optimization. CoRR abs/2002.10544 (2020) - [i55]Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora:
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. CoRR abs/2002.11172 (2020) - [i54]Yangsibo Huang, Yushan Su, Sachin Ravi, Zhao Song, Sanjeev Arora, Kai Li:
Privacy-preserving Learning via Deep Net Pruning. CoRR abs/2003.01876 (2020) - [i53]Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora:
InstaHide: Instance-hiding Schemes for Private Distributed Learning. CoRR abs/2010.02772 (2020) - [i52]Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora:
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. CoRR abs/2010.02916 (2020) - [i51]Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora:
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. CoRR abs/2010.03648 (2020) - [i50]Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora:
TextHide: Tackling Data Privacy in Language Understanding Tasks. CoRR abs/2010.06053 (2020) - [i49]Zhiyuan Li, Yi Zhang, Sanjeev Arora:
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? CoRR abs/2010.08515 (2020)
2010 – 2019
- 2019
- [c80]Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu:
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. ICLR (Poster) 2019 - [c79]Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu:
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. ICLR (Poster) 2019 - [c78]Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang:
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. ICML 2019: 322-332 - [c77]Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar:
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. ICML 2019: 5628-5637 - [c76]Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo:
Implicit Regularization in Deep Matrix Factorization. NeurIPS 2019: 7411-7422 - [c75]Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang:
On Exact Computation with an Infinitely Wide Neural Net. NeurIPS 2019: 8139-8148 - [c74]Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora:
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. NeurIPS 2019: 14574-14583 - [i48]Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang:
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. CoRR abs/1901.08584 (2019) - [i47]Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi:
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. CoRR abs/1902.09229 (2019) - [i46]Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang:
On Exact Computation with an Infinitely Wide Neural Net. CoRR abs/1904.11955 (2019) - [i45]Arushi Gupta, Sanjeev Arora:
A Simple Saliency Method That Passes the Sanity Checks. CoRR abs/1905.12152 (2019) - [i44]Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo:
Implicit Regularization in Deep Matrix Factorization. CoRR abs/1905.13655 (2019) - [i43]Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge:
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. CoRR abs/1906.06247 (2019) - [i42]Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu:
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. CoRR abs/1910.01663 (2019) - [i41]Zhiyuan Li, Sanjeev Arora:
An Exponential Learning Rate Schedule for Deep Learning. CoRR abs/1910.07454 (2019) - [i40]Zhiyuan Li, Ruosong Wang, Dingli Yu, Simon S. Du, Wei Hu, Ruslan Salakhutdinov, Sanjeev Arora:
Enhanced Convolutional Neural Tangent Kernels. CoRR abs/1911.00809 (2019) - 2018
- [j37]Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
Learning topic models - provably and efficiently. Commun. ACM 61(4): 85-93 (2018) - [j36]Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Christopher Baldassano, Janice Chen, Esther Yong, Christopher J. Honey, Uri Hasson, Peter J. Ramadge, Kenneth A. Norman, Sanjeev Arora:
Mapping between fMRI responses to movies and their natural language annotations. NeuroImage 180(Part): 223-231 (2018) - [j35]Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski:
Linear Algebraic Structure of Word Senses, with Applications to Polysemy. Trans. Assoc. Comput. Linguistics 6: 483-495 (2018) - [c73]Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora:
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. ACL (1) 2018: 12-22 - [c72]Sanjeev Arora, Wei Hu, Pravesh K. Kothari:
An Analysis of the t-SNE Algorithm for Data Visualization. COLT 2018: 1455-1462 - [c71]Sanjeev Arora, Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang:
Towards Provable Control for Unknown Linear Dynamical Systems. ICLR (Workshop) 2018 - [c70]Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli:
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. ICLR (Poster) 2018 - [c69]Sanjeev Arora, Andrej Risteski, Yi Zhang:
Do GANs learn the distribution? Some Theory and Empirics. ICLR (Poster) 2018 - [c68]Sanjeev Arora, Nadav Cohen, Elad Hazan:
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. ICML 2018: 244-253 - [c67]Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang:
Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML 2018: 254-263 - [i39]Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang:
Stronger generalization bounds for deep nets via a compression approach. CoRR abs/1802.05296 (2018) - [i38]Sanjeev Arora, Nadav Cohen, Elad Hazan:
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. CoRR abs/1802.06509 (2018) - [i37]Sanjeev Arora, Wei Hu, Pravesh K. Kothari:
An Analysis of the t-SNE Algorithm for Data Visualization. CoRR abs/1803.01768 (2018) - [i36]Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora:
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. CoRR abs/1805.05388 (2018) - [i35]Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu:
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. CoRR abs/1810.02281 (2018) - [i34]Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu:
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. CoRR abs/1812.03981 (2018) - 2017
- [c66]Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora:
On the Ability of Neural Nets to Express Distributions. COLT 2017: 1271-1296 - [c65]Sanjeev Arora, Yingyu Liang, Tengyu Ma:
A Simple but Tough-to-Beat Baseline for Sentence Embeddings. ICLR (Poster) 2017 - [c64]Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang:
Generalization and Equilibrium in Generative Adversarial Nets (GANs). ICML 2017: 224-232 - [c63]Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski:
Provable learning of noisy-OR networks. STOC 2017: 1057-1066 - [i33]Holden Lee, Rong Ge, Andrej Risteski, Tengyu Ma, Sanjeev Arora:
On the ability of neural nets to express distributions. CoRR abs/1702.07028 (2017) - [i32]Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang:
Generalization and Equilibrium in Generative Adversarial Nets (GANs). CoRR abs/1703.00573 (2017) - [i31]Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora:
Extending and Improving Wordnet via Unsupervised Word Embeddings. CoRR abs/1705.00217 (2017) - [i30]Sanjeev Arora, Andrej Risteski:
Provable benefits of representation learning. CoRR abs/1706.04601 (2017) - [i29]Sanjeev Arora, Yi Zhang:
Do GANs actually learn the distribution? An empirical study. CoRR abs/1706.08224 (2017) - [i28]Sanjeev Arora, Andrej Risteski, Yi Zhang:
Theoretical limitations of Encoder-Decoder GAN architectures. CoRR abs/1711.02651 (2017) - 2016
- [j34]Sanjeev Arora, Satyen Kale:
A Combinatorial, Primal-Dual Approach to Semidefinite Programs. J. ACM 63(2): 12:1-12:35 (2016) - [j33]Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra:
Computing a Nonnegative Matrix Factorization - Provably. SIAM J. Comput. 45(4): 1582-1611 (2016) - [j32]Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski:
A Latent Variable Model Approach to PMI-based Word Embeddings. Trans. Assoc. Comput. Linguistics 4: 385-399 (2016) - [c62]Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra:
Provable Algorithms for Inference in Topic Models. ICML 2016: 2859-2867 - [i27]Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski:
Linear Algebraic Structure of Word Senses, with Applications to Polysemy. CoRR abs/1601.03764 (2016) - [i26]Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra:
Provable Algorithms for Inference in Topic Models. CoRR abs/1605.08491 (2016) - [i25]Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Janice Chen, Esther Yong, Christopher J. Honey, Peter J. Ramadge, Kenneth A. Norman, Sanjeev Arora:
Mapping Between Natural Movie fMRI Responses and Word-Sequence Representations. CoRR abs/1610.03914 (2016) - [i24]Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski:
Provable learning of Noisy-or Networks. CoRR abs/1612.08795 (2016) - 2015
- [j31]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. Algorithmica 72(1): 215-236 (2015) - [j30]Sanjeev Arora, Vinay Kumar Nangia, Rajat Agrawal:
Making strategy process intelligent with business intelligence: an empirical investigation. Int. J. Data Anal. Tech. Strateg. 7(1): 77-95 (2015) - [j29]Sanjeev Arora, Boaz Barak, David Steurer:
Subexponential Algorithms for Unique Games and Related Problems. J. ACM 62(5): 42:1-42:25 (2015) - [c61]Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra:
Simple, Efficient, and Neural Algorithms for Sparse Coding. COLT 2015: 113-149 - [c60]Sanjeev Arora:
Overcoming Intractability in Unsupervised Learning (Invited Talk). STACS 2015: 1-1 - [i23]Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski:
Random Walks on Context Spaces: Towards an Explanation of the Mysteries of Semantic Word Embeddings. CoRR abs/1502.03520 (2015) - [i22]Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra:
Simple, Efficient, and Neural Algorithms for Sparse Coding. CoRR abs/1503.00778 (2015) - [i21]Sanjeev Arora, Yingyu Liang, Tengyu Ma:
Why are deep nets reversible: A simple theory, with implications for training. CoRR abs/1511.05653 (2015) - 2014
- [j28]Sanjeev Arora:
Thoughts on Paper Publishing in the Digital Age. Bull. EATCS 112 (2014) - [c59]Sanjeev Arora, Rong Ge, Ankur Moitra:
New Algorithms for Learning Incoherent and Overcomplete Dictionaries. COLT 2014: 779-806 - [c58]Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
Provable Bounds for Learning Some Deep Representations. ICML 2014: 584-592 - [c57]Prabhat Chand, Pratima Murthy, Vivek Gupta, Arun Kandasamy, Deepak Jayarajan, Lakshmanan Sethu, Vivek Benegal, Mathew Varghese, Miriam Komaromy, Sanjeev Arora:
Technology Enhanced Learning in Addiction Mental Health: Developing a Virtual Knowledge Network: NIMHANS ECHO. T4E 2014: 229-232 - [i20]Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
More Algorithms for Provable Dictionary Learning. CoRR abs/1401.0579 (2014) - 2013
- [c56]Sanjeev Arora, Rong Ge, Ali Kemal Sinop:
Towards a Better Approximation for Sparsest Cut? FOCS 2013: 270-279 - [c55]Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. ICML (2) 2013: 280-288 - [i19]Sanjeev Arora, Rong Ge, Ali Kemal Sinop:
Towards a better approximation for sparsest cut? CoRR abs/1304.3365 (2013) - [i18]Sanjeev Arora, Rong Ge, Ankur Moitra:
New Algorithms for Learning Incoherent and Overcomplete Dictionaries. CoRR abs/1308.6273 (2013) - [i17]Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
Provable Bounds for Learning Some Deep Representations. CoRR abs/1310.6343 (2013) - 2012
- [j27]Sanjeev Arora:
The Gödel Price 2013. Call for Nominations. Bull. EATCS 108: 17-21 (2012) - [j26]Sanjeev Arora, László Lovász, Ilan Newman, Yuval Rabani, Yuri Rabinovich, Santosh S. Vempala:
Local Versus Global Properties of Metric Spaces. SIAM J. Comput. 41(1): 250-271 (2012) - [j25]Sanjeev Arora, Constantinos Daskalakis, David Steurer:
Message-Passing Algorithms and Improved LP Decoding. IEEE Trans. Inf. Theory 58(12): 7260-7271 (2012) - [j24]Sanjeev Arora, Elad Hazan, Satyen Kale:
The Multiplicative Weights Update Method: a Meta-Algorithm and Applications. Theory Comput. 8(1): 121-164 (2012) - [c54]Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva:
Testing Permanent Oracles - Revisited. APPROX-RANDOM 2012: 362-373 - [c53]Sanjeev Arora, Rong Ge, Ankur Moitra:
Learning Topic Models - Going beyond SVD. FOCS 2012: 1-10 - [c52]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". NIPS 2012: 2384-2392 - [c51]Sanjeev Arora, Eunjee Song, Yoonjeong Kim:
Modified hierarchical privacy-aware role based access control model. RACS 2012: 344-347 - [c50]Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck:
Finding overlapping communities in social networks: toward a rigorous approach. EC 2012: 37-54 - [c49]Sanjeev Arora, Rong Ge, Ravindran Kannan, Ankur Moitra:
Computing a nonnegative matrix factorization - provably. STOC 2012: 145-162 - [i16]Sanjeev Arora, Rong Ge, Ankur Moitra:
Learning Topic Models - Going beyond SVD. CoRR abs/1204.1956 (2012) - [i15]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. CoRR abs/1206.5349 (2012) - [i14]Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva:
Testing Permanent Oracles -- Revisited. CoRR abs/1207.4783 (2012) - [i13]Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. CoRR abs/1212.4777 (2012) - [i12]Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva:
Testing Permanent Oracles - Revisited. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [j23]Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge:
Computational complexity and information asymmetry in financial products. Commun. ACM 54(5): 101-107 (2011) - [j22]Mikhail Alekhnovich, Sanjeev Arora, Iannis Tourlakis:
Towards Strong Nonapproximability Results in the Lovász-Schrijver Hierarchy. Comput. Complex. 20(4): 615-648 (2011) - [c48]Sanjeev Arora, Rong Ge:
New Tools for Graph Coloring. APPROX-RANDOM 2011: 1-12 - [c47]Masoud Naghedolfeizi, Sanjeev Arora, James E. Glover:
Visualizing conductive and convective heat transfer using thermographic techniques. FIE 2011: 3 - [c46]Sanjeev Arora, Rong Ge:
New Algorithms for Learning in Presence of Errors. ICALP (1) 2011: 403-415 - [c45]Sanjeev Arora:
Semidefinite Programming and Approximation Algorithms: A Survey. ISAAC 2011: 6-9 - [i11]Sanjeev Arora, James R. Lee, Sushant Sachdeva:
A Reformulation of the Arora-Rao-Vazirani Structure Theorem. CoRR abs/1102.1456 (2011) - [i10]Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra:
Computing a Nonnegative Matrix Factorization -- Provably. CoRR abs/1111.0952 (2011) - [i9]Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck:
Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach. CoRR abs/1112.1831 (2011) - 2010
- [j21]Sanjeev Arora, Elad Hazan, Satyen Kale:
O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n2) Time. SIAM J. Comput. 39(5): 1748-1771 (2010) - [c44]Sanjeev Arora, Boaz Barak, David Steurer:
Subexponential Algorithms for Unique Games and Related Problems. FOCS 2010: 563-572 - [c43]Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge:
Computational Complexity and Information Asymmetry in Financial Products (Extended Abstract). ICS 2010: 49-65 - [c42]Sanjeev Arora:
Semidefinite Programming and Approximation Algorithms: A Survey. SWAT 2010: 25 - [i8]Prahladh Harsha, Moses Charikar, Matthew Andrews, Sanjeev Arora, Subhash Khot, Dana Moshkovitz, Lisa Zhang, Ashkan Aazami, Dev Desai, Igor Gorodezky, Geetha Jagannathan, Alexander S. Kulikov, Darakhshan J. Mir, Alantha Newman, Aleksandar Nikolov, David Pritchard, Gwen Spencer:
Limits of Approximation Algorithms: PCPs and Unique Games (DIMACS Tutorial Lecture Notes). CoRR abs/1002.3864 (2010) - [i7]Sanjeev Arora, Rong Ge:
Learning Parities with Structured Noise. Electron. Colloquium Comput. Complex. TR10 (2010) - [i6]Sanjeev Arora, Russell Impagliazzo, William Matthews, David Steurer:
Improved Algorithms for Unique Games via Divide and Conquer. Electron. Colloquium Comput. Complex. TR10 (2010)
2000 – 2009
- 2009
- [b1]Sanjeev Arora, Boaz Barak:
Computational Complexity - A Modern Approach. Cambridge University Press 2009, ISBN 978-0-521-42426-4, pp. I-XXIV, 1-579 - [j20]Sanjeev Arora, Satish Rao, Umesh V. Vazirani:
Expander flows, geometric embeddings and graph partitioning. J. ACM 56(2): 5:1-5:37 (2009) - [c41]Sanjeev Arora, David Steurer, Avi Wigderson:
Towards a Study of Low-Complexity Graphs. ICALP (1) 2009: 119-131 - [c40]Sanjeev Arora, Constantinos Daskalakis, David Steurer:
Message passing algorithms and improved LP decoding. STOC 2009: 3-12 - 2008
- [j19]Sanjeev Arora, Satish Rao, Umesh V. Vazirani:
Geometry, flows, and graph-partitioning algorithms. Commun. ACM 51(10): 96-105 (2008) - [c39]Sanjeev Arora, Subhash Khot, Alexandra Kolla, David Steurer, Madhur Tulsiani, Nisheeth K. Vishnoi:
Unique games on expanding constraint graphs are easy: extended abstract. STOC 2008: 21-28 - 2007
- [j18]Sanjeev Arora, James R. Lee, Assaf Naor:
Fréchet Embeddings of Negative Type Metrics. Discret. Comput. Geom. 38(4): 726-739 (2007) - [c38]Sanjeev Arora, Satyen Kale:
A combinatorial, primal-dual approach to semidefinite programs. STOC 2007: 227-236 - 2006
- [j17]Sanjeev Arora, George Karakostas:
A 2 + epsilon approximation algorithm for the k-MST problem. Math. Program. 107(3): 491-504 (2006) - [j16]Sanjeev Arora, Béla Bollobás, László Lovász, Iannis Tourlakis:
Proving Integrality Gaps without Knowing the Linear Program. Theory Comput. 2(2): 19-51 (2006) - [c37]Sanjeev Arora, Elad Hazan, Satyen Kale:
A Fast Random Sampling Algorithm for Sparsifying Matrices. APPROX-RANDOM 2006: 272-279 - [c36]Sanjeev Arora, László Lovász, Ilan Newman, Yuval Rabani, Yuri Rabinovich, Santosh S. Vempala:
Local versus global properties of metric spaces. SODA 2006: 41-50 - [c35]Sanjeev Arora, Eden Chlamtac:
New approximation guarantee for chromatic number. STOC 2006: 215-224 - 2005
- [j15]Sanjeev Arora, Bernard Chazelle:
Is the thrill gone? Commun. ACM 48(8): 31-33 (2005) - [c34]Sanjeev Arora, Eli Berger, Elad Hazan, Guy Kindler, Muli Safra:
On Non-Approximability for Quadratic Programs. FOCS 2005: 206-215 - [c33]Sanjeev Arora, Elad Hazan, Satyen Kale:
Fast Algorithms for Approximate Semide.nite Programming using the Multiplicative Weights Update Method. FOCS 2005: 339-348 - [c32]Michael Alekhnovich, Sanjeev Arora, Iannis Tourlakis:
Towards strong nonapproximability results in the Lovasz-Schrijver hierarchy. STOC 2005: 294-303 - [c31]Sanjeev Arora, James R. Lee, Assaf Naor:
Euclidean distortion and the sparsest cut. STOC 2005: 553-562 - [i5]Sanjeev Arora, Eli Berger, Elad Hazan, Guy Kindler, Muli Safra:
On Non-Approximability for Quadratic Programs. Electron. Colloquium Comput. Complex. TR05 (2005) - 2004
- [j14]Sanjeev Arora, Kevin L. Chang:
Approximation Schemes for Degree-Restricted MST and Red-Blue Separation Problems. Algorithmica 40(3): 189-210 (2004) - [j13]Sanjeev Arora, Bo Brinkman:
A Randomized Online Algorithm for Bandwidth Utilization. J. Sched. 7(3): 187-194 (2004) - [c30]Sanjeev Arora, Elad Hazan, Satyen Kale:
0(sqrt (log n)) Approximation to SPARSEST CUT in Õ(n2) Time. FOCS 2004: 238-247 - [c29]Sanjeev Arora, Satish Rao, Umesh V. Vazirani:
Expander flows, geometric embeddings and graph partitioning. STOC 2004: 222-231 - [p1]Sanjeev Arora:
Exploring complexity through reductions. Computational Complexity Theory 2004: 101-126 - [i4]Mikhail Alecknovich, Sanjeev Arora, Iannis Tourlakis:
Towards strong nonapproximability results in the Lovasz-Schrijver hierarchy. Electron. Colloquium Comput. Complex. TR04 (2004) - 2003
- [j12]Sanjeev Arora, Madhu Sudan:
Improved Low-Degree Testing and its Applications. Comb. 23(3): 365-426 (2003) - [j11]Sanjeev Arora, Subhash Khot:
Fitting algebraic curves to noisy data. J. Comput. Syst. Sci. 67(2): 325-340 (2003) - [j10]Sanjeev Arora:
Approximation schemes for NP-hard geometric optimization problems: a survey. Math. Program. 97(1-2): 43-69 (2003) - [j9]Sanjeev Arora, George Karakostas:
Approximation Schemes for Minimum Latency Problems. SIAM J. Comput. 32(5): 1317-1337 (2003) - [c28]Sanjeev Arora:
Proving Integrality Gaps without Knowing the Linear Program. FCT 2003: 1 - [c27]Sanjeev Arora, Kevin L. Chang:
Approximation Schemes for Degree-Restricted MST and Red-Blue Separation Problem. ICALP 2003: 176-188 - [e1]Sanjeev Arora, Klaus Jansen, José D. P. Rolim, Amit Sahai:
Approximation, Randomization, and Combinatorial Optimization: Algorithms and Techniques, 6th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2003 and 7th International Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 2003, Princeton, NJ, USA, August 24-26, 2003, Proceedings. Lecture Notes in Computer Science 2764, Springer 2003, ISBN 3-540-40770-7 [contents] - [i3]Sanjeev Arora:
How NP got a new definition: a survey of probabilistically checkable proofs. CoRR cs.CC/0304038 (2003) - 2002
- [j8]Sanjeev Arora, Alan M. Frieze, Haim Kaplan:
A new rounding procedure for the assignment problem with applications to dense graph arrangement problems. Math. Program. 92(1): 1-36 (2002) - [c26]Sanjeev Arora, Béla Bollobás, László Lovász:
Proving Integrality Gaps without Knowing the Linear Program. FOCS 2002: 313-322 - [c25]Eric Allender, Sanjeev Arora, Michael J. Kearns, Cristopher Moore, Alexander Russell:
A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. NIPS 2002: 431-437 - [c24]Sanjeev Arora, Bo Brinkman:
A randomized online algorithm for bandwidth utilization. SODA 2002: 535-539 - [c23]Sanjeev Arora, Subhash Khot:
Fitting algebraic curves to noisy data. STOC 2002: 162-169 - 2001
- [c22]Sanjeev Arora:
Approximation Schemes for Geometric NP-Hard Problems: A Survey. FSTTCS 2001: 16-17 - [c21]Sanjeev Arora, Ravi Kannan:
Learning mixtures of arbitrary gaussians. STOC 2001: 247-257 - 2000
- [c20]Sanjeev Arora:
Approximation algorithms that take advice. APPROX 2000: 1 - [c19]Sanjeev Arora, George Karakostas:
A 2+epsilon approximation algorithm for the k-MST problem. SODA 2000: 754-759
1990 – 1999
- 1999
- [j7]Sanjeev Arora, David R. Karger, Marek Karpinski:
Polynomial Time Approximation Schemes for Dense Instances of NP-Hard Problems. J. Comput. Syst. Sci. 58(1): 193-210 (1999) - [c18]Susanne Albers, Sanjeev Arora, Sanjeev Khanna:
Page Replacement for General Caching Problems. SODA 1999: 31-40 - [c17]Sanjeev Arora, George Karakostas:
Approximation Schemes for Minimum Latency Problems. STOC 1999: 688-693 - 1998
- [j6]Sanjeev Arora, Shmuel Safra:
Probabilistic Checking of Proofs: A New Characterization of NP. J. ACM 45(1): 70-122 (1998) - [j5]Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, Mario Szegedy:
Proof Verification and the Hardness of Approximation Problems. J. ACM 45(3): 501-555 (1998) - [j4]Sanjeev Arora:
Polynomial Time Approximation Schemes for Euclidean Traveling Salesman and other Geometric Problems. J. ACM 45(5): 753-782 (1998) - [c16]Sanjeev Arora, Michelangelo Grigni, David R. Karger, Philip N. Klein, Andrzej Woloszyn:
A Polynomial-Time Approximation Scheme for Weighted Planar Graph TSP. SODA 1998: 33-41 - [c15]Sanjeev Arora, Prabhakar Raghavan, Satish Rao:
Approximation Schemes for Euclidean k-Medians and Related Problems. STOC 1998: 106-113 - [c14]Sanjeev Arora:
The Approximability of NP-hard Problems. STOC 1998: 337-348 - [i2]Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, Mario Szegedy:
Proof verification and the hardness of approximation problems. Electron. Colloquium Comput. Complex. TR98 (1998) - 1997
- [j3]Sanjeev Arora, László Babai, Jacques Stern, Z. Sweedyk:
The Hardness of Approximate Optima in Lattices, Codes, and Systems of Linear Equations. J. Comput. Syst. Sci. 54(2): 317-331 (1997) - [j2]Sanjeev Arora, Ronald Fagin:
On Winning Strategies in Ehrenfeucht-Fraïssé Games. Theor. Comput. Sci. 174(1-2): 97-121 (1997) - [c13]Sanjeev Arora:
Nearly linear time approximation schemes for Euclidean TSP and other geometric problems. Network Design: Connectivity and Facilities Location 1997: 1-2 - [c12]Sanjeev Arora:
Nearly Linear Time Approximation Schemes for Euclidean TSP and other Geometric Problems. FOCS 1997: 554-563 - [c11]Sanjeev Arora:
Nearly Linear Time Approximation Schemes for Euclidean TSP and Other Geometric Problems. RANDOM 1997: 55 - [c10]Sanjeev Arora, Madhu Sudan:
Improved Low-Degree Testing and its Applications. STOC 1997: 485-495 - [i1]Sanjeev Arora, Madhu Sudan:
Improved low-degree testing and its applications. Electron. Colloquium Comput. Complex. TR97 (1997) - 1996
- [j1]Sanjeev Arora, Frank Thomson Leighton, Bruce M. Maggs:
On-Line Algorithms for Path Selection in a Nonblocking Network. SIAM J. Comput. 25(3): 600-625 (1996) - [c9]Sanjeev Arora:
Polynomial Time Approximation Schemes for Euclidean TSP and Other Geometric Problems. FOCS 1996: 2-11 - [c8]Sanjeev Arora, Alan M. Frieze, Haim Kaplan:
A New Rounding Procedure for the Assignment Problem with Applications to Dense Graph Arrangement Problems. FOCS 1996: 21-30 - 1995
- [c7]Sanjeev Arora:
Reductions, Codes, PCPs, and Inapproximability. FOCS 1995: 404-413 - [c6]Sanjeev Arora, David R. Karger, Marek Karpinski:
Polynomial time approximation schemes for dense instances of NP-hard problems. STOC 1995: 284-293 - 1994
- [c5]Sanjeev Arora, Yuval Rabani, Umesh V. Vazirani:
Simulating quadratic dynamical systems is PSPACE-complete (preliminary version). STOC 1994: 459-467 - 1993
- [c4]Sanjeev Arora, László Babai, Jacques Stern, Z. Sweedyk:
The Hardness of Approximate Optimia in Lattices, Codes, and Systems of Linear Equations. FOCS 1993: 724-733 - 1992
- [c3]Sanjeev Arora, Shmuel Safra:
Probabilistic Checking of Proofs; A New Characterization of NP. FOCS 1992: 2-13 - [c2]Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, Mario Szegedy:
Proof Verification and Hardness of Approximation Problems. FOCS 1992: 14-23 - 1990
- [c1]Sanjeev Arora, Frank Thomson Leighton, Bruce M. Maggs:
On-line Algorithms for Path Selection in a Nonblocking Network (Extended Abstract). STOC 1990: 149-158
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