Sanjeev Arora - RankMe

281 papers A* 96A 2B 2C 1Journal 153Unranked 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms. CoRR Haoyu Zhao, Ziran Yang, Jiawei Li, Deyuan He, Zenan Li, Chi Jin, Venugopal V. Veeravalli, Aarti Gupta, Sanjeev Arora
2026	J	`jnl`	Contextual Drag: How Errors in the Context Affect LLM Reasoning. CoRR Yun Cheng, Xingyu Zhu, Haoyu Zhao, Sanjeev Arora
2026	J	`jnl`	Escaping the Cognitive Well: Efficient Competition Math with Off-the-Shelf Models. CoRR Xingyu Dang, Rohit Agarwal, Rodrigo Porto, Anirudh Goyal, Liam H. Fowl, Sanjeev Arora
2026	J	`jnl`	Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set. Trans. Mach. Learn. Res. Yikai Wu, Haoyu Zhao, Sanjeev Arora
2025	J	`jnl`	AdaptMI: Adaptive Skill-based In-context Math Instruction for Small Language Models. CoRR Yinghui He, Abhishek Panigrahi, Yong Lin, Sanjeev Arora
2025	J	`jnl`	Advancing Science- and Evidence-based AI Policy. CoRR Rishi Bommasani, Sanjeev Arora, Jennifer T. Chayes, Yejin Choi, Mariano-Florentino Cuellar, Li Fei-Fei, Daniel E. Ho, Dan Jurafsky, Sanmi Koyejo, Hima Lakkaraju, Arvind Narayanan, Alondra Nelson, Emma Pierson, Joelle Pineau, Scott R. Singer, Gaël Varoquaux, Suresh Venkatasubramanian, Ion Stoica, Percy Liang, Dawn Song
2025	A*	`conf`	Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs? ICML Simon Park, Abhishek Panigrahi, Yun Cheng, Dingli Yu, Anirudh Goyal, Sanjeev Arora
2025	J	`jnl`	Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs? CoRR Simon Park, Abhishek Panigrahi, Yun Cheng, Dingli Yu, Anirudh Goyal, Sanjeev Arora
2025	J	`jnl`	Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction. CoRR Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui-Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, Jiayun Wu, Jiri Gesi, Ximing Lu, David Acuna, Kaiyu Yang, Hongzhou Lin, Yejin Choi, Danqi Chen, Sanjeev Arora, Chi Jin
2025	J	`jnl`	Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving. CoRR Yong Lin, Shange Tang, Bohan Lyu, Jiayun Wu, Hongzhou Lin, Kaiyu Yang, Jia Li, Mengzhou Xia, Danqi Chen, Sanjeev Arora, Chi Jin
2025	J	`jnl`	How Does RL Post-training Induce Skill Composition? A Case Study on Countdown. CoRR Simon Park, Simran Kaur, Sanjeev Arora
2025	J	`jnl`	Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving on Inequalities. CoRR Haoyu Zhao, Yihan Geng, Shange Tang, Yong Lin, Bohan Lyu, Hongzhou Lin, Chi Jin, Sanjeev Arora
2025	A*	`conf`	Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning. ICLR Simran Kaur, Simon Park, Anirudh Goyal, Sanjeev Arora
2025	J	`jnl`	LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? CoRR Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He, Dongruixuan Li, Stanley Wei, Hangyi Hao, Jianzhu Yao, Peiyao Sheng, Zixuan Wang, Wenhao Chai, Aleksandra Korolova, Peter Henderson, Sanjeev Arora, Pramod Viswanath, Jingbo Shang, Saining Xie
2025	J	`jnl`	Low Compute Unlearning via Sparse Representations. Trans. Mach. Learn. Res. Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Curtis Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal
2025	J	`jnl`	Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors. CoRR Aniket Didolkar, Nicolas Ballas, Sanjeev Arora, Anirudh Goyal
2025	J	`jnl`	OCA: A Shiny web application for transparent overload compensation in higher education. SoftwareX Dawit Aberra, Xiangyan Zeng, Chunhua Dong Mahon, Sanjeev Arora
2025	J	`jnl`	On the Impossibility of Retrain Equivalence in Machine Unlearning. CoRR Jiatong Yu, Yinghui He, Anirudh Goyal, Sanjeev Arora
2025	A*	`conf`	On the Power of Context-Enhanced Learning in LLMs. ICML Xingyu Zhu, Abhishek Panigrahi, Sanjeev Arora
2025	J	`jnl`	On the Power of Context-Enhanced Learning in LLMs. CoRR Xingyu Zhu, Abhishek Panigrahi, Sanjeev Arora
2025	A*	`conf`	Provable unlearning in topic modeling and downstream tasks. ICLR Stanley Wei, Sadhika Malladi, Sanjeev Arora, Amartya Sanyal
2025	J	`jnl`	Rethinking Thinking Tokens: LLMs as Improvement Operators. CoRR Lovish Madaan, Aniket Didolkar, Suchin Gururangan, John Quan, Ruan Silva, Ruslan Salakhutdinov, Manzil Zaheer, Sanjeev Arora, Anirudh Goyal
2025	J	`jnl`	Skill-Targeted Adaptive Training. CoRR Yinghui He, Abhishek Panigrahi, Yong Lin, Sanjeev Arora
2025	A*	`conf`	Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization. ICLR Noam Razin, Sadhika Malladi, Adithya Bhaskar, Danqi Chen, Sanjeev Arora, Boris Hanin
2025	J	`jnl`	Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set. CoRR Yikai Wu, Haoyu Zhao, Sanjeev Arora
2025	A*	`conf`	Weak-to-Strong Generalization Even in Random Feature Networks, Provably. ICML Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro
2025	J	`jnl`	Weak-to-Strong Generalization Even in Random Feature Networks, Provably. CoRR Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro
2025	J	`jnl`	What Makes a Reward Model a Good Teacher? An Optimization Perspective. CoRR Noam Razin, Zixuan Wang, Hubert Strauss, Stanley Wei, Jason D. Lee, Sanjeev Arora
2025	J	`jnl`	Why is Your Language Model a Poor Implicit Reward Model? CoRR Noam Razin, Yong Lin, Jiarui Yao, Sanjeev Arora
2024	A*	`conf`	A Quadratic Synchronization Rule for Distributed Deep Learning. ICLR Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang
2024	J	`jnl`	AI-Assisted Generation of Difficult Math Questions. CoRR Vedant Shah, Dingli Yu, Kaifeng Lyu, Simon Park, Nan Rosemary Ke, Michael Mozer, Yoshua Bengio, Sanjeev Arora, Anirudh Goyal
2024	A*	`conf`	Can Models Learn Skill Composition from Examples? NeurIPS Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora
2024	J	`jnl`	Can Models Learn Skill Composition from Examples? CoRR Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora
2024	A*	`conf`	CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs. NeurIPS 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
2024	J	`jnl`	CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs. CoRR 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
2024	A*	`conf`	ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty. NeurIPS Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, Sanjeev Arora
2024	J	`jnl`	ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty. CoRR Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, Sanjeev Arora
2024	A*	`conf`	From Word-prediction to Complex Skills: Compositional Thinking and Metacognition in LLMs. KDD Sanjeev Arora
2024	J	`jnl`	Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning. CoRR Simran Kaur, Simon Park, Anirudh Goyal, Sanjeev Arora
2024	A*	`conf`	Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. NeurIPS Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, Sanjeev Arora
2024	J	`jnl`	Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. CoRR Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, Sanjeev Arora
2024	A*	`conf`	LESS: Selecting Influential Data for Targeted Instruction Tuning. ICML Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, Danqi Chen
2024	J	`jnl`	LESS: Selecting Influential Data for Targeted Instruction Tuning. CoRR Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, Danqi Chen
2024	A*	`conf`	Language Models as Science Tutors. ICML 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
2024	J	`jnl`	Language Models as Science Tutors. CoRR 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
2024	A*	`conf`	Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. NeurIPS Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo Jimenez Rezende, Yoshua Bengio, Michael C. Mozer, Sanjeev Arora
2024	J	`jnl`	Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora
2024	J	`jnl`	Provable unlearning in topic modeling and downstream tasks. CoRR Stanley Wei, Sadhika Malladi, Sanjeev Arora, Amartya Sanyal
2024	A*	`conf`	SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models. ICLR Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora
2024	A*	`conf`	Trainable Transformer in Transformer. ICML Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora
2024	J	`jnl`	Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization. CoRR Noam Razin, Sadhika Malladi, Adithya Bhaskar, Danqi Chen, Sanjeev Arora, Boris Hanin
2023	A*	`conf`	A Kernel-Based View of Language Model Fine-Tuning. ICML Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora
2023	J	`jnl`	A Quadratic Synchronization Rule for Distributed Deep Learning. CoRR Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang
2023	J	`jnl`	A Theory for Emergence of Complex Skills in Language Models. CoRR Sanjeev Arora, Anirudh Goyal
2023	A*	`conf`	Do Transformers Parse while Predicting the Masked Word? EMNLP Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
2023	J	`jnl`	Do Transformers Parse while Predicting the Masked Word? CoRR Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
2023	A*	`conf`	Fine-Tuning Language Models with Just Forward Passes. NeurIPS Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora
2023	J	`jnl`	Fine-Tuning Language Models with Just Forward Passes. CoRR Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora
2023	J	`jnl`	Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models. CoRR Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora
2023	A*	`conf`	Task-Specific Skill Localization in Fine-tuned Language Models. ICML Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora
2023	J	`jnl`	Task-Specific Skill Localization in Fine-tuned Language Models. CoRR Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora
2023	J	`jnl`	Trainable Transformer in Transformer. CoRR Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora
2023	A*	`conf`	Understanding Influence Functions and Datamodels via Harmonic Analysis. ICLR Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora
2023	J	`jnl`	Unlearning via Sparse Representations. CoRR Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal
2023	A*	`conf`	Why (and When) does Local SGD Generalize Better than SGD? ICLR Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora
2023	J	`jnl`	Why (and When) does Local SGD Generalize Better than SGD? CoRR Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora
2022	J	`jnl`	A Kernel-Based View of Language Model Fine-Tuning. CoRR Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora
2022	J	`jnl`	Adaptive Gradient Methods with Local Guarantees. CoRR Zhou Lu, Wenhan Xia, Sanjeev Arora, Elad Hazan
2022	A*	`conf`	Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. NeurIPS Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora
2022	J	`jnl`	Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. CoRR Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora
2022	A*	`conf`	New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. NeurIPS Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora
2022	J	`jnl`	New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. CoRR Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora
2022	A*	`conf`	On Predicting Generalization using GANs. ICLR Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora
2022	A*	`conf`	On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. NeurIPS Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora
2022	J	`jnl`	On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. CoRR Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora
2022	A*	`conf`	Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
2022	J	`jnl`	Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
2022	J	`jnl`	Understanding Gradient Descent on Edge of Stability in Deep Learning. CoRR Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi
2022	A*	`conf`	Understanding Gradient Descent on the Edge of Stability in Deep Learning. ICML Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi
2022	J	`jnl`	Understanding Influence Functions and Datamodels via Harmonic Analysis. CoRR Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora
2022	A*	`conf`	Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. NeurIPS Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora
2022	J	`jnl`	Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. CoRR Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora
2022	A*	`conf`	What Happens after SGD Reaches Zero Loss? --A Mathematical Framework. ICLR Zhiyuan Li, Tianhao Wang, Sanjeev Arora
2021	A*	`conf`	A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. ICLR Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora
2021	A*	`conf`	Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. NeurIPS Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora
2021	J	`jnl`	Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. CoRR Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora
2021	A*	`conf`	Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. NeurIPS Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora
2021	J	`jnl`	Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. CoRR Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora
2021	J	`jnl`	On Predicting Generalization using GANs. CoRR Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora
2021	A*	`conf`	On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs). NeurIPS Zhiyuan Li, Sadhika Malladi, Sanjeev Arora
2021	J	`jnl`	On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs). CoRR Zhiyuan Li, Sadhika Malladi, Sanjeev Arora
2021	—	`conf`	Opening the Black Box of Deep Learning: Some Lessons and Take-aways. SIGMETRICS (Abstracts) Sanjeev Arora
2021	J	`jnl`	Rip van Winkle's Razor: A Simple Estimate of Overfit to Test Data. CoRR Sanjeev Arora, Yi Zhang
2021	J	`jnl`	Technical perspective: Why don't today's deep nets overfit to their training data? Commun. ACM Sanjeev Arora
2021	J	`jnl`	What Happens after SGD Reaches Zero Loss? -A Mathematical Framework. CoRR Zhiyuan Li, Tianhao Wang, Sanjeev Arora
2021	A*	`conf`	Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? ICLR Zhiyuan Li, Yi Zhang, Sanjeev Arora
2020	J	`jnl`	A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. CoRR Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora
2020	A*	`conf`	A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. ICML Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
2020	J	`jnl`	A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. CoRR Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
2020	A*	`conf`	An Exponential Learning Rate Schedule for Deep Learning. ICLR Zhiyuan Li, Sanjeev Arora
2020	A*	`conf`	Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. ICLR Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
2020	A*	`conf`	InstaHide: Instance-hiding Schemes for Private Distributed Learning. ICML Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora
2020	J	`jnl`	InstaHide: Instance-hiding Schemes for Private Distributed Learning. CoRR Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora
2020	A*	`conf`	Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. NeurIPS Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora
2020	J	`jnl`	Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. CoRR Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora
2020	J	`jnl`	Privacy-preserving Learning via Deep Net Pruning. CoRR Yangsibo Huang, Yushan Su, Sachin Ravi, Zhao Song, Sanjeev Arora, Kai Li
2020	A*	`conf`	Provable Representation Learning for Imitation Learning via Bi-level Optimization. ICML Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi
2020	J	`jnl`	Provable Representation Learning for Imitation Learning via Bi-level Optimization. CoRR Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi
2020	A*	`conf`	Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. NeurIPS Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora
2020	J	`jnl`	Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. CoRR Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora
2020	—	`conf`	TextHide: Tackling Data Privacy for Language Understanding Tasks. EMNLP (Findings) Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora
2020	J	`jnl`	TextHide: Tackling Data Privacy in Language Understanding Tasks. CoRR Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora
2020	—	`conf`	The Quest for Mathematical Understanding of Deep Learning (Invited Talk). FSTTCS Sanjeev Arora
2020	J	`jnl`	Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? CoRR Zhiyuan Li, Yi Zhang, Sanjeev Arora
2019	—	`conf`	A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. ICLR (Poster) Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu
2019	J	`jnl`	A Simple Saliency Method That Passes the Sanity Checks. CoRR Arushi Gupta, Sanjeev Arora
2019	A*	`conf`	A Theoretical Analysis of Contrastive Unsupervised Representation Learning. ICML Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar
2019	J	`jnl`	A Theoretical Analysis of Contrastive Unsupervised Representation Learning. CoRR Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi
2019	J	`jnl`	An Exponential Learning Rate Schedule for Deep Learning. CoRR Zhiyuan Li, Sanjeev Arora
2019	J	`jnl`	Enhanced Convolutional Neural Tangent Kernels. CoRR Zhiyuan Li, Ruosong Wang, Dingli Yu, Simon S. Du, Wei Hu, Ruslan Salakhutdinov, Sanjeev Arora
2019	A*	`conf`	Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. NeurIPS Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora
2019	J	`jnl`	Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. CoRR Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge
2019	A*	`conf`	Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. ICML Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019	J	`jnl`	Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. CoRR Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019	J	`jnl`	Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. CoRR Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
2019	A*	`conf`	Implicit Regularization in Deep Matrix Factorization. NeurIPS Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo
2019	J	`jnl`	Implicit Regularization in Deep Matrix Factorization. CoRR Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo
2019	A*	`conf`	On Exact Computation with an Infinitely Wide Neural Net. NeurIPS Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
2019	J	`jnl`	On Exact Computation with an Infinitely Wide Neural Net. CoRR Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
2019	—	`conf`	Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. ICLR (Poster) Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
2018	—	`conf`	A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. ICLR (Poster) Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
2018	J	`jnl`	A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. CoRR Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu
2018	—	`conf`	A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. ACL (1) Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
2018	J	`jnl`	A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. CoRR Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
2018	A*	`conf`	An Analysis of the t-SNE Algorithm for Data Visualization. COLT Sanjeev Arora, Wei Hu, Pravesh K. Kothari
2018	J	`jnl`	An Analysis of the t-SNE Algorithm for Data Visualization. CoRR Sanjeev Arora, Wei Hu, Pravesh K. Kothari
2018	—	`conf`	Do GANs learn the distribution? Some Theory and Empirics. ICLR (Poster) Sanjeev Arora, Andrej Risteski, Yi Zhang
2018	J	`jnl`	Learning topic models - provably and efficiently. Commun. ACM Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2018	J	`jnl`	Linear Algebraic Structure of Word Senses, with Applications to Polysemy. Trans. Assoc. Comput. Linguistics Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
2018	J	`jnl`	Mapping between fMRI responses to movies and their natural language annotations. NeuroImage 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
2018	A*	`conf`	On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. ICML Sanjeev Arora, Nadav Cohen, Elad Hazan
2018	J	`jnl`	On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. CoRR Sanjeev Arora, Nadav Cohen, Elad Hazan
2018	A*	`conf`	Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
2018	J	`jnl`	Stronger generalization bounds for deep nets via a compression approach. CoRR Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
2018	J	`jnl`	Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. CoRR Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
2018	—	`conf`	Towards Provable Control for Unknown Linear Dynamical Systems. ICLR (Workshop) Sanjeev Arora, Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
2017	—	`conf`	A Simple but Tough-to-Beat Baseline for Sentence Embeddings. ICLR (Poster) Sanjeev Arora, Yingyu Liang, Tengyu Ma
2017	J	`jnl`	Do GANs actually learn the distribution? An empirical study. CoRR Sanjeev Arora, Yi Zhang
2017	J	`jnl`	Extending and Improving Wordnet via Unsupervised Word Embeddings. CoRR Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora
2017	A*	`conf`	Generalization and Equilibrium in Generative Adversarial Nets (GANs). ICML Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
2017	J	`jnl`	Generalization and Equilibrium in Generative Adversarial Nets (GANs). CoRR Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
2017	A*	`conf`	On the Ability of Neural Nets to Express Distributions. COLT Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora
2017	J	`jnl`	On the ability of neural nets to express distributions. CoRR Holden Lee, Rong Ge, Andrej Risteski, Tengyu Ma, Sanjeev Arora
2017	J	`jnl`	Provable benefits of representation learning. CoRR Sanjeev Arora, Andrej Risteski
2017	A*	`conf`	Provable learning of noisy-OR networks. STOC Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski
2017	J	`jnl`	Theoretical limitations of Encoder-Decoder GAN architectures. CoRR Sanjeev Arora, Andrej Risteski, Yi Zhang
2016	J	`jnl`	A Combinatorial, Primal-Dual Approach to Semidefinite Programs. J. ACM Sanjeev Arora, Satyen Kale
2016	J	`jnl`	A Latent Variable Model Approach to PMI-based Word Embeddings. Trans. Assoc. Comput. Linguistics Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
2016	J	`jnl`	Computing a Nonnegative Matrix Factorization - Provably. SIAM J. Comput. Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra
2016	J	`jnl`	Linear Algebraic Structure of Word Senses, with Applications to Polysemy. CoRR Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
2016	J	`jnl`	Mapping Between Natural Movie fMRI Responses and Word-Sequence Representations. CoRR Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Janice Chen, Esther Yong, Christopher J. Honey, Peter J. Ramadge, Kenneth A. Norman, Sanjeev Arora
2016	A*	`conf`	Provable Algorithms for Inference in Topic Models. ICML Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
2016	J	`jnl`	Provable Algorithms for Inference in Topic Models. CoRR Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
2016	J	`jnl`	Provable learning of Noisy-or Networks. CoRR Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski
2015	J	`jnl`	Making strategy process intelligent with business intelligence: an empirical investigation. Int. J. Data Anal. Tech. Strateg. Sanjeev Arora, Vinay Kumar Nangia, Rajat Agrawal
2015	A	`conf`	Overcoming Intractability in Unsupervised Learning (Invited Talk). STACS Sanjeev Arora
2015	J	`jnl`	Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. Algorithmica Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
2015	J	`jnl`	Random Walks on Context Spaces: Towards an Explanation of the Mysteries of Semantic Word Embeddings. CoRR Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
2015	A*	`conf`	Simple, Efficient, and Neural Algorithms for Sparse Coding. COLT Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra
2015	J	`jnl`	Simple, Efficient, and Neural Algorithms for Sparse Coding. CoRR Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra
2015	J	`jnl`	Subexponential Algorithms for Unique Games and Related Problems. J. ACM Sanjeev Arora, Boaz Barak, David Steurer
2015	J	`jnl`	Why are deep nets reversible: A simple theory, with implications for training. CoRR Sanjeev Arora, Yingyu Liang, Tengyu Ma
2014	J	`jnl`	More Algorithms for Provable Dictionary Learning. CoRR Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma
2014	A*	`conf`	New Algorithms for Learning Incoherent and Overcomplete Dictionaries. COLT Sanjeev Arora, Rong Ge, Ankur Moitra
2014	A*	`conf`	Provable Bounds for Learning Some Deep Representations. ICML Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma
2014	—	`conf`	Technology Enhanced Learning in Addiction Mental Health: Developing a Virtual Knowledge Network: NIMHANS ECHO. T4E Prabhat Chand, Pratima Murthy, Vivek Gupta, Arun Kandasamy, Deepak Jayarajan, Lakshmanan Sethu, Vivek Benegal, Mathew Varghese, Miriam Komaromy, Sanjeev Arora
2014	J	`jnl`	Thoughts on Paper Publishing in the Digital Age. Bull. EATCS Sanjeev Arora
2013	—	`conf`	A Practical Algorithm for Topic Modeling with Provable Guarantees. ICML (2) Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2013	J	`jnl`	New Algorithms for Learning Incoherent and Overcomplete Dictionaries. CoRR Sanjeev Arora, Rong Ge, Ankur Moitra
2013	J	`jnl`	Provable Bounds for Learning Some Deep Representations. CoRR Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma
2013	A*	`conf`	Towards a Better Approximation for Sparsest Cut? FOCS Sanjeev Arora, Rong Ge, Ali Kemal Sinop
2013	J	`jnl`	Towards a better approximation for sparsest cut? CoRR Sanjeev Arora, Rong Ge, Ali Kemal Sinop
2012	—	`conf`	"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". NIPS Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
2012	J	`jnl`	A Practical Algorithm for Topic Modeling with Provable Guarantees CoRR Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2012	A*	`conf`	Computing a nonnegative matrix factorization - provably. STOC Sanjeev Arora, Rong Ge, Ravindran Kannan, Ankur Moitra
2012	A*	`conf`	Finding overlapping communities in social networks: toward a rigorous approach. EC Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck
2012	J	`jnl`	Learning Topic Models - Going beyond SVD CoRR Sanjeev Arora, Rong Ge, Ankur Moitra
2012	A*	`conf`	Learning Topic Models - Going beyond SVD. FOCS Sanjeev Arora, Rong Ge, Ankur Moitra
2012	J	`jnl`	Local Versus Global Properties of Metric Spaces. SIAM J. Comput. Sanjeev Arora, László Lovász, Ilan Newman, Yuval Rabani, Yuri Rabinovich, Santosh S. Vempala
2012	J	`jnl`	Message-Passing Algorithms and Improved LP Decoding. IEEE Trans. Inf. Theory Sanjeev Arora, Constantinos Daskalakis, David Steurer
2012	J	`jnl`	Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders CoRR Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
2012	—	`conf`	Testing Permanent Oracles - Revisited. APPROX-RANDOM Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva
2012	J	`jnl`	Testing Permanent Oracles - Revisited. Electron. Colloquium Comput. Complex. Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva
2012	J	`jnl`	Testing Permanent Oracles -- Revisited CoRR Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva
2012	J	`jnl`	The Gödel Price 2013. Call for Nominations. Bull. EATCS Sanjeev Arora
2012	J	`jnl`	The Multiplicative Weights Update Method: a Meta-Algorithm and Applications. Theory Comput. Sanjeev Arora, Elad Hazan, Satyen Kale
2011	J	`jnl`	A Reformulation of the Arora-Rao-Vazirani Structure Theorem CoRR Sanjeev Arora, James R. Lee, Sushant Sachdeva
2011	J	`jnl`	Computational complexity and information asymmetry in financial products. Commun. ACM Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge
2011	J	`jnl`	Computing a Nonnegative Matrix Factorization -- Provably CoRR Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra
2011	J	`jnl`	Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach CoRR Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck
2011	—	`conf`	New Algorithms for Learning in Presence of Errors. ICALP (1) Sanjeev Arora, Rong Ge
2011	—	`conf`	New Tools for Graph Coloring. APPROX-RANDOM Sanjeev Arora, Rong Ge
2011	B	`conf`	Semidefinite Programming and Approximation Algorithms: A Survey. ISAAC Sanjeev Arora
2011	J	`jnl`	Towards Strong Nonapproximability Results in the Lovász-Schrijver Hierarchy. Comput. Complex. Mikhail Alekhnovich, Sanjeev Arora, Iannis Tourlakis
2011	C	`conf`	Visualizing conductive and convective heat transfer using thermographic techniques. FIE Masoud Naghedolfeizi, Sanjeev Arora, James E. Glover
2010	A	`conf`	Computational Complexity and Information Asymmetry in Financial Products (Extended Abstract). ICS Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge
2010	J	`jnl`	Improved Algorithms for Unique Games via Divide and Conquer. Electron. Colloquium Comput. Complex. Sanjeev Arora, Russell Impagliazzo, William Matthews, David Steurer
2010	J	`jnl`	Learning Parities with Structured Noise. Electron. Colloquium Comput. Complex. Sanjeev Arora, Rong Ge
2010	J	`jnl`	Limits of Approximation Algorithms: PCPs and Unique Games (DIMACS Tutorial Lecture Notes) CoRR 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
2010	J	`jnl`	O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n SIAM J. Comput. Sanjeev Arora, Elad Hazan, Satyen Kale
2010	—	`conf`	Semidefinite Programming and Approximation Algorithms: A Survey. SWAT Sanjeev Arora
2010	A*	`conf`	Subexponential Algorithms for Unique Games and Related Problems. FOCS Sanjeev Arora, Boaz Barak, David Steurer
2009	—	`book`	Computational Complexity - A Modern Approach. Sanjeev Arora, Boaz Barak
2009	J	`jnl`	Expander flows, geometric embeddings and graph partitioning. J. ACM Sanjeev Arora, Satish Rao, Umesh V. Vazirani
2009	A*	`conf`	Message passing algorithms and improved LP decoding. STOC Sanjeev Arora, Constantinos Daskalakis, David Steurer
2009	—	`conf`	Towards a Study of Low-Complexity Graphs. ICALP (1) Sanjeev Arora, David Steurer, Avi Wigderson
2008	J	`jnl`	Geometry, flows, and graph-partitioning algorithms. Commun. ACM Sanjeev Arora, Satish Rao, Umesh V. Vazirani
2008	A*	`conf`	Unique games on expanding constraint graphs are easy: extended abstract. STOC Sanjeev Arora, Subhash Khot, Alexandra Kolla, David Steurer, Madhur Tulsiani, Nisheeth K. Vishnoi
2007	A*	`conf`	A combinatorial, primal-dual approach to semidefinite programs. STOC Sanjeev Arora, Satyen Kale
2007	J	`jnl`	Fréchet Embeddings of Negative Type Metrics. Discret. Comput. Geom. Sanjeev Arora, James R. Lee, Assaf Naor
2006	J	`jnl`	A 2 + Math. Program. Sanjeev Arora, George Karakostas
2006	—	`conf`	A Fast Random Sampling Algorithm for Sparsifying Matrices. APPROX-RANDOM Sanjeev Arora, Elad Hazan, Satyen Kale
2006	A*	`conf`	Local versus global properties of metric spaces. SODA Sanjeev Arora, László Lovász, Ilan Newman, Yuval Rabani, Yuri Rabinovich, Santosh S. Vempala
2006	A*	`conf`	New approximation guarantee for chromatic number. STOC Sanjeev Arora, Eden Chlamtac
2006	J	`jnl`	Proving Integrality Gaps without Knowing the Linear Program. Theory Comput. Sanjeev Arora, Béla Bollobás, László Lovász, Iannis Tourlakis
2005	A*	`conf`	Euclidean distortion and the sparsest cut. STOC Sanjeev Arora, James R. Lee, Assaf Naor
2005	A*	`conf`	Fast Algorithms for Approximate Semide.nite Programming using the Multiplicative Weights Update Method. FOCS Sanjeev Arora, Elad Hazan, Satyen Kale
2005	J	`jnl`	Is the thrill gone? Commun. ACM Sanjeev Arora, Bernard Chazelle
2005	J	`jnl`	On Non-Approximability for Quadratic Programs Electron. Colloquium Comput. Complex. Sanjeev Arora, Eli Berger, Elad Hazan, Guy Kindler, Muli Safra
2005	A*	`conf`	On Non-Approximability for Quadratic Programs. FOCS Sanjeev Arora, Eli Berger, Elad Hazan, Guy Kindler, Muli Safra
2005	A*	`conf`	Towards strong nonapproximability results in the Lovasz-Schrijver hierarchy. STOC Michael Alekhnovich, Sanjeev Arora, Iannis Tourlakis
2004	A*	`conf`	0(sqrt (log n)) Approximation to SPARSEST CUT in Õ(n FOCS Sanjeev Arora, Elad Hazan, Satyen Kale
2004	J	`jnl`	A Randomized Online Algorithm for Bandwidth Utilization. J. Sched. Sanjeev Arora, Bo Brinkman
2004	J	`jnl`	Approximation Schemes for Degree-Restricted MST and Red-Blue Separation Problems. Algorithmica Sanjeev Arora, Kevin L. Chang
2004	A*	`conf`	Expander flows, geometric embeddings and graph partitioning. STOC Sanjeev Arora, Satish Rao, Umesh V. Vazirani
2004	—	`ch.`	Exploring complexity through reductions. Computational Complexity Theory Sanjeev Arora
2004	J	`jnl`	Towards strong nonapproximability results in the Lovasz-Schrijver hierarchy Electron. Colloquium Comput. Complex. Mikhail Alecknovich, Sanjeev Arora, Iannis Tourlakis
2003	A*	`conf`	Approximation Schemes for Degree-Restricted MST and Red-Blue Separation Problem. ICALP Sanjeev Arora, Kevin L. Chang
2003	J	`jnl`	Approximation Schemes for Minimum Latency Problems. SIAM J. Comput. Sanjeev Arora, George Karakostas
2003	J	`jnl`	Approximation schemes for NP-hard geometric optimization problems: a survey. Math. Program. Sanjeev Arora
2003	—	`ed.`	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 RANDOM-APPROX Sanjeev Arora, Klaus Jansen, José D. P. Rolim, Amit Sahai
2003	J	`jnl`	Fitting algebraic curves to noisy data. J. Comput. Syst. Sci. Sanjeev Arora, Subhash Khot
2003	J	`jnl`	How NP got a new definition: a survey of probabilistically checkable proofs CoRR Sanjeev Arora
2003	J	`jnl`	Improved Low-Degree Testing and its Applications. Comb. Sanjeev Arora, Madhu Sudan
2003	B	`conf`	Proving Integrality Gaps without Knowing the Linear Program. FCT Sanjeev Arora
2002	—	`conf`	A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. NIPS Eric Allender, Sanjeev Arora, Michael J. Kearns, Cristopher Moore, Alexander Russell
2002	J	`jnl`	A new rounding procedure for the assignment problem with applications to dense graph arrangement problems. Math. Program. Sanjeev Arora, Alan M. Frieze, Haim Kaplan
2002	A*	`conf`	A randomized online algorithm for bandwidth utilization. SODA Sanjeev Arora, Bo Brinkman
2002	A*	`conf`	Fitting algebraic curves to noisy data. STOC Sanjeev Arora, Subhash Khot
2002	A*	`conf`	Proving Integrality Gaps without Knowing the Linear Program. FOCS Sanjeev Arora, Béla Bollobás, László Lovász
2001	—	`conf`	Approximation Schemes for Geometric NP-Hard Problems: A Survey. FSTTCS Sanjeev Arora
2001	A*	`conf`	Learning mixtures of arbitrary gaussians. STOC Sanjeev Arora, Ravi Kannan
2000	A*	`conf`	A 2+epsilon approximation algorithm for the SODA Sanjeev Arora, George Karakostas
2000	—	`conf`	Approximation algorithms that take advice. APPROX Sanjeev Arora
1999	A*	`conf`	Approximation Schemes for Minimum Latency Problems. STOC Sanjeev Arora, George Karakostas
1999	A*	`conf`	Page Replacement for General Caching Problems. SODA Susanne Albers, Sanjeev Arora, Sanjeev Khanna
1999	J	`jnl`	Polynomial Time Approximation Schemes for Dense Instances of NP-Hard Problems. J. Comput. Syst. Sci. Sanjeev Arora, David R. Karger, Marek Karpinski
1998	A*	`conf`	A Polynomial-Time Approximation Scheme for Weighted Planar Graph TSP. SODA Sanjeev Arora, Michelangelo Grigni, David R. Karger, Philip N. Klein, Andrzej Woloszyn
1998	A*	`conf`	Approximation Schemes for Euclidean STOC Sanjeev Arora, Prabhakar Raghavan, Satish Rao
1998	J	`jnl`	Polynomial Time Approximation Schemes for Euclidean Traveling Salesman and other Geometric Problems. J. ACM Sanjeev Arora
1998	J	`jnl`	Probabilistic Checking of Proofs: A New Characterization of NP. J. ACM Sanjeev Arora, Shmuel Safra
1998	J	`jnl`	Proof Verification and the Hardness of Approximation Problems. J. ACM Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, Mario Szegedy
1998	J	`jnl`	Proof verification and the hardness of approximation problems. Electron. Colloquium Comput. Complex. Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, Mario Szegedy
1998	A*	`conf`	The Approximability of NP-hard Problems. STOC Sanjeev Arora
1997	A*	`conf`	Improved Low-Degree Testing and its Applications. STOC Sanjeev Arora, Madhu Sudan
1997	J	`jnl`	Improved low-degree testing and its applications Electron. Colloquium Comput. Complex. Sanjeev Arora, Madhu Sudan
1997	—	`conf`	Nearly Linear Time Approximation Schemes for Euclidean TSP and Other Geometric Problems. RANDOM Sanjeev Arora
1997	A*	`conf`	Nearly Linear Time Approximation Schemes for Euclidean TSP and other Geometric Problems. FOCS Sanjeev Arora
1997	—	`conf`	Nearly linear time approximation schemes for Euclidean TSP and other geometric problems. Network Design: Connectivity and Facilities Location Sanjeev Arora
1997	J	`jnl`	On Winning Strategies in Ehrenfeucht-Fraïssé Games. Theor. Comput. Sci. Sanjeev Arora, Ronald Fagin
1997	J	`jnl`	The Hardness of Approximate Optima in Lattices, Codes, and Systems of Linear Equations. J. Comput. Syst. Sci. Sanjeev Arora, László Babai, Jacques Stern, Z. Sweedyk
1996	A*	`conf`	A New Rounding Procedure for the Assignment Problem with Applications to Dense Graph Arrangement Problems. FOCS Sanjeev Arora, Alan M. Frieze, Haim Kaplan
1996	J	`jnl`	On-Line Algorithms for Path Selection in a Nonblocking Network. SIAM J. Comput. Sanjeev Arora, Frank Thomson Leighton, Bruce M. Maggs
1996	A*	`conf`	Polynomial Time Approximation Schemes for Euclidean TSP and Other Geometric Problems. FOCS Sanjeev Arora
1995	A*	`conf`	Polynomial time approximation schemes for dense instances of STOC Sanjeev Arora, David R. Karger, Marek Karpinski
1995	A*	`conf`	Reductions, Codes, PCPs, and Inapproximability. FOCS Sanjeev Arora
1994	A*	`conf`	Simulating quadratic dynamical systems is PSPACE-complete (preliminary version). STOC Sanjeev Arora, Yuval Rabani, Umesh V. Vazirani
1993	A*	`conf`	The Hardness of Approximate Optimia in Lattices, Codes, and Systems of Linear Equations FOCS Sanjeev Arora, László Babai, Jacques Stern, Z. Sweedyk
1992	A*	`conf`	Probabilistic Checking of Proofs; A New Characterization of NP FOCS Sanjeev Arora, Shmuel Safra
1992	A*	`conf`	Proof Verification and Hardness of Approximation Problems FOCS Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu Sudan, Mario Szegedy
1990	A*	`conf`	On-line Algorithms for Path Selection in a Nonblocking Network (Extended Abstract) STOC Sanjeev Arora, Frank Thomson Leighton, Bruce M. Maggs