Kunal Talwar - RankMe

227 papers A* 84A 6B 3Journal 107Unranked 23

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	The Importance of Being Smoothly Calibrated. CoRR Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar, Pranay Tankala
2025	A*	`conf`	Adaptive Batch Size for Privately Finding Second-Order Stationary Points. ICLR Daogao Liu, Kunal Talwar
2025	J	`jnl`	Efficient Calibration for Decision Making. CoRR Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar, Pranay Tankala
2025	A*	`conf`	Faster Rates for Private Adversarial Bandits. ICML Hilal Asi, Vinod Raman, Kunal Talwar
2025	J	`jnl`	Faster Rates for Private Adversarial Bandits. CoRR Hilal Asi, Vinod Raman, Kunal Talwar
2025	A*	`conf`	Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis. STOC Xin Lyu, Kunal Talwar
2025	A*	`conf`	Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. ICML Guy Kornowski, Daogao Liu, Kunal Talwar
2025	J	`jnl`	Instance-Optimality for Private KL Distribution Estimation. CoRR Jiayuan Ye, Vitaly Feldman, Kunal Talwar
2025	J	`jnl`	Local Pan-Privacy for Federated Analytics. CoRR Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar
2025	A*	`conf`	Local Pan-privacy for Federated Analytics. ICML Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar
2025	J	`jnl`	On Privately Estimating a Single Parameter. CoRR Hilal Asi, John C. Duchi, Kunal Talwar
2025	J	`jnl`	PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications. CoRR Hilal Asi, Vitaly Feldman, Hannah Keller, Guy N. Rothblum, Kunal Talwar
2025	J	`jnl`	PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications. IACR Cryptol. ePrint Arch. Hilal Asi, Vitaly Feldman, Hannah Keller, Guy N. Rothblum, Kunal Talwar
2025	—	`conf`	Privacy-Computation Trade-Offs in Private Repetition and Metaselection. FORC Kunal Talwar
2025	J	`jnl`	Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling. SIAM J. Math. Data Sci. Jason M. Altschuler, Kunal Talwar
2024	J	`jnl`	Adaptive Batch Size for Privately Finding Second-Order Stationary Points. CoRR Daogao Liu, Kunal Talwar
2024	—	`conf`	Differentially Private Heavy Hitter Detection using Federated Analytics. SaTML Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
2024	J	`jnl`	Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis. CoRR Xin Lyu, Kunal Talwar
2024	J	`jnl`	Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. CoRR Guy Kornowski, Daogao Liu, Kunal Talwar
2024	A*	`conf`	Instance-Optimal Private Density Estimation in the Wasserstein Distance. NeurIPS Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar
2024	J	`jnl`	Instance-Optimal Private Density Estimation in the Wasserstein Distance. CoRR Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar
2024	J	`jnl`	On the Privacy of Noisy Stochastic Gradient Descent for Convex Optimization. SIAM J. Comput. Jason M. Altschuler, Jinho Bok, Kunal Talwar
2024	A*	`conf`	PINE: Efficient Verification of a Euclidean Norm Bound of a Secret-Shared Vector. USENIX Security Symposium Guy N. Rothblum, Eran Omri, Junye Chen, Kunal Talwar
2024	J	`jnl`	Privacy-Computation trade-offs in Private Repetition and Metaselection. CoRR Kunal Talwar
2024	A*	`conf`	Private Online Learning via Lazy Algorithms. NeurIPS Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar
2024	J	`jnl`	Private Online Learning via Lazy Algorithms. CoRR Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar
2024	A*	`conf`	Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages. ICML Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou
2024	J	`jnl`	Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages. CoRR Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou
2024	A*	`conf`	Private and Personalized Frequency Estimation in a Federated Setting. NeurIPS Amrith Setlur, Vitaly Feldman, Kunal Talwar
2024	A*	`conf`	Samplable Anonymous Aggregation for Private Federated Data Analysis. CCS Kunal Talwar, Shan Wang, Audra McMillan, Vitaly Feldman, Pansy Bansal, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Congzheng Song, Linmao Song, Karl Tarbe, Sebastian Vogt, Shundong Zhou, Vojta Jina, Michael Scaria, Luke Winstrom
2024	J	`jnl`	Scalable Private Search with Wally. CoRR Hilal Asi, Fabian Boemer, Nicholas Genise, Muhammad Haris Mughees, Tabitha Ogilvie, Rehan Rishi, Guy N. Rothblum, Kunal Talwar, Karl Tarbe, Ruiyu Zhu, Marco Zuliani
2023	—	`ed.`	4th Symposium on Foundations of Responsible Computing, FORC 2023, Stanford University, California, USA, June 7-9, 2023 FORC Kunal Talwar
2023	J	`jnl`	Differentially Private Heavy Hitter Detection using Federated Analytics. CoRR Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
2023	A*	`conf`	Fast Optimal Locally Private Mean Estimation via Random Projections. NeurIPS Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar
2023	J	`jnl`	Fast Optimal Locally Private Mean Estimation via Random Projections. CoRR Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar
2023	J	`jnl`	Federated Learning with Differential Privacy for End-to-End Speech Recognition. CoRR Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman, Jan Honza Silovsky, Kunal Talwar, Tatiana Likhomanenko
2023	J	`jnl`	Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
2023	A*	`conf`	Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. ICML Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
2023	J	`jnl`	Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. CoRR Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
2023	J	`jnl`	PINE: Efficient Norm-Bound Verification for Secret-Shared Vectors. CoRR Guy N. Rothblum, Eran Omri, Junye Chen, Kunal Talwar
2023	A*	`conf`	Private Online Prediction from Experts: Separations and Faster Rates. COLT Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
2023	A*	`conf`	Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling. COLT Jason M. Altschuler, Kunal Talwar
2023	J	`jnl`	Samplable Anonymous Aggregation for Private Federated Data Analysis. CoRR Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina, Vitaly Feldman, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Michael Scaria, Linmao Song, Congzheng Song, Karl Tarbe, Sebastian Vogt, Luke Winstrom, Shundong Zhou
2023	A*	`conf`	Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy. SODA Vitaly Feldman, Audra McMillan, Kunal Talwar
2022	J	`jnl`	Concentration of the Langevin Algorithm's Stationary Distribution. CoRR Jason M. Altschuler, Kunal Talwar
2022	—	`conf`	Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation. FORC Kunal Talwar
2022	J	`jnl`	Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation. CoRR Kunal Talwar
2022	A*	`conf`	FLAIR: Federated Learning Annotated Image Repository. NeurIPS Congzheng Song, Filip Granqvist, Kunal Talwar
2022	J	`jnl`	FLAIR: Federated Learning Annotated Image Repository. CoRR Congzheng Song, Filip Granqvist, Kunal Talwar
2022	A*	`conf`	Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
2022	A*	`conf`	Optimal Algorithms for Mean Estimation under Local Differential Privacy. ICML Hilal Asi, Vitaly Feldman, Kunal Talwar
2022	J	`jnl`	Optimal Algorithms for Mean Estimation under Local Differential Privacy. CoRR Hilal Asi, Vitaly Feldman, Kunal Talwar
2022	A*	`conf`	Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML Lorenzo Orecchia, Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar
2022	A*	`conf`	Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss. NeurIPS Jason M. Altschuler, Kunal Talwar
2022	J	`jnl`	Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss. CoRR Jason M. Altschuler, Kunal Talwar
2022	J	`jnl`	Private Federated Statistics in an Interactive Setting. CoRR Audra McMillan, Omid Javidbakht, Kunal Talwar, Elliot Briggs, Mike Chatzidakis, Junye Chen, John C. Duchi, Vitaly Feldman, Yusuf Goren, Michael Hesse, Vojta Jina, Anil Katti, Albert Liu, Cheney Lyford, Joey Meyer, Alex Palmer, David Park, Wonhee Park, Gianni Parsa, Paul Pelzl, Rehan Rishi, Congzheng Song, Shan Wang, Shundong Zhou
2022	J	`jnl`	Private Frequency Estimation via Projective Geometry. CoRR Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar
2022	J	`jnl`	Private Online Prediction from Experts: Separations and Faster Rates. CoRR Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
2022	A*	`conf`	Private frequency estimation via projective geometry. ICML Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar
2022	J	`jnl`	Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy. CoRR Vitaly Feldman, Audra McMillan, Kunal Talwar
2022	A*	`conf`	Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
2022	J	`jnl`	Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. CoRR John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
2021	A*	`conf`	Characterizing Structural Regularities of Labeled Data in Overparameterized Models. ICML Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer
2021	A*	`conf`	Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. FOCS Vitaly Feldman, Audra McMillan, Kunal Talwar
2021	A*	`conf`	Lossless Compression of Efficient Private Local Randomizers. ICML Vitaly Feldman, Kunal Talwar
2021	J	`jnl`	Lossless Compression of Efficient Private Local Randomizers. CoRR Vitaly Feldman, Kunal Talwar
2021	J	`jnl`	Online Learning over a Finite Action Set with Limited Switching. Math. Oper. Res. Jason M. Altschuler, Kunal Talwar
2021	A*	`conf`	Private Adaptive Gradient Methods for Convex Optimization. ICML Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar
2021	J	`jnl`	Private Adaptive Gradient Methods for Convex Optimization. CoRR Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar
2021	A*	`conf`	Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. ICML Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
2021	J	`jnl`	Private Stochastic Convex Optimization: Optimal Rates in 𝓁 CoRR Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
2021	A*	`conf`	When is memorization of irrelevant training data necessary for high-accuracy learning? STOC Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar
2020	J	`jnl`	Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation. CoRR Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta
2020	J	`jnl`	Exploring the Memorization-Generalization Continuum in Deep Learning. CoRR Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer
2020	A*	`conf`	Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. NeurIPS Arun Ganesh, Kunal Talwar
2020	J	`jnl`	Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. CoRR Arun Ganesh, Kunal Talwar
2020	J	`jnl`	Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. CoRR Vitaly Feldman, Audra McMillan, Kunal Talwar
2020	J	`jnl`	On the Error Resistance of Hinge Loss Minimization. CoRR Kunal Talwar
2020	A*	`conf`	On the Error Resistance of Hinge-Loss Minimization. NeurIPS Kunal Talwar
2020	J	`jnl`	Private Stochastic Convex Optimization: Optimal Rates in Linear Time. CoRR Vitaly Feldman, Tomer Koren, Kunal Talwar
2020	A*	`conf`	Private stochastic convex optimization: optimal rates in linear time. STOC Vitaly Feldman, Tomer Koren, Kunal Talwar
2020	A*	`conf`	Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. NeurIPS Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar
2020	J	`jnl`	Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. CoRR Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar
2020	A*	`conf`	Stochastic Optimization with Laggard Data Pipelines. NeurIPS Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang
2020	J	`jnl`	Stochastic Optimization with Laggard Data Pipelines. CoRR Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang
2020	J	`jnl`	When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? CoRR Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar
2019	A*	`conf`	Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. SODA Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta
2019	A*	`conf`	Better Algorithms for Stochastic Bandits with Adversarial Corruptions. COLT Anupam Gupta, Tomer Koren, Kunal Talwar
2019	J	`jnl`	Better Algorithms for Stochastic Bandits with Adversarial Corruptions. CoRR Anupam Gupta, Tomer Koren, Kunal Talwar
2019	A*	`conf`	Computational Separations between Sampling and Optimization. NeurIPS Kunal Talwar
2019	J	`jnl`	Computational Separations between Sampling and Optimization. CoRR Kunal Talwar
2019	J	`jnl`	Cops, Robbers, and Threatening Skeletons: Padded Decomposition for Minor-Free Graphs. SIAM J. Comput. Ittai Abraham, Cyril Gavoille, Anupam Gupta, Ofer Neiman, Kunal Talwar
2019	A*	`conf`	Private Stochastic Convex Optimization with Optimal Rates. NeurIPS Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta
2019	J	`jnl`	Private Stochastic Convex Optimization with Optimal Rates. CoRR Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta
2019	A*	`conf`	Private selection from private candidates. STOC Jingcheng Liu, Kunal Talwar
2019	J	`jnl`	Rényi Differential Privacy of the Sampled Gaussian Mechanism. CoRR Ilya Mironov, Kunal Talwar, Li Zhang
2019	A*	`conf`	Semi-Cyclic Stochastic Gradient Descent. ICML Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar
2019	J	`jnl`	Semi-Cyclic Stochastic Gradient Descent. CoRR Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar
2018	A*	`conf`	Adversarially Robust Generalization Requires More Data. NeurIPS Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry
2018	J	`jnl`	Adversarially Robust Generalization Requires More Data. CoRR Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry
2018	J	`jnl`	Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. CoRR Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta
2018	A*	`conf`	Balancing Vectors in Any Norm. FOCS Daniel Dadush, Aleksandar Nikolov, Kunal Talwar, Nicole Tomczak-Jaegermann
2018	—	`conf`	Learning Differentially Private Recurrent Language Models. ICLR (Poster) H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
2018	A*	`conf`	Online Linear Quadratic Control. ICML Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar
2018	J	`jnl`	Online Linear Quadratic Control. CoRR Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar
2018	A*	`conf`	Online learning over a finite action set with limited switching. COLT Jason M. Altschuler, Kunal Talwar
2018	J	`jnl`	Online learning over a finite action set with limited switching. CoRR Jason M. Altschuler, Kunal Talwar
2018	A*	`conf`	Privacy Amplification by Iteration. FOCS Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta
2018	J	`jnl`	Privacy Amplification by Iteration. CoRR Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta
2018	J	`jnl`	Private Selection from Private Candidates. CoRR Jingcheng Liu, Kunal Talwar
2018	—	`conf`	Scalable Private Learning with PATE. ICLR Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson
2018	J	`jnl`	Scalable Private Learning with PATE. CoRR Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson
2017	A*	`conf`	LAST but not Least: Online Spanners for Buy-at-Bulk. SODA Anupam Gupta, R. Ravi, Kunal Talwar, Seeun William Umboh
2017	J	`jnl`	Learning Differentially Private Language Models Without Losing Accuracy. CoRR H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
2017	J	`jnl`	Oblivious Stash Shuffle. CoRR Petros Maniatis, Ilya Mironov, Kunal Talwar
2017	J	`jnl`	On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CoRR Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
2017	A	`conf`	On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. CSF Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
2017	—	`conf`	Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar
2017	—	`conf`	Short and Deep: Sketching and Neural Networks. ICLR (Workshop) Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
2016	—	`ch.`	Approximating Metric Spaces by Tree Metrics. Encyclopedia of Algorithms Jittat Fakcharoenphol, Satish Rao, Kunal Talwar
2016	A*	`conf`	Deep Learning with Differential Privacy. CCS Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
2016	J	`jnl`	Deep Learning with Differential Privacy. CoRR Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
2016	J	`jnl`	LAST but not Least: Online Spanners for Buy-at-Bulk. CoRR Anupam Gupta, R. Ravi, Kunal Talwar, Seeun William Umboh
2016	J	`jnl`	Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. CoRR Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar
2016	J	`jnl`	Sketching and Neural Networks. CoRR Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
2016	A	`conf`	Smooth Boolean Functions are Easy: Efficient Algorithms for Low-Sensitivity Functions. ITCS Parikshit Gopalan, Noam Nisan, Rocco A. Servedio, Kunal Talwar, Avi Wigderson
2016	J	`jnl`	TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng
2016	J	`jnl`	The Geometry of Differential Privacy: The Small Database and Approximate Cases. SIAM J. Comput. Aleksandar Nikolov, Kunal Talwar, Li Zhang
2015	A*	`conf`	Approximating Hereditary Discrepancy via Small Width Ellipsoids. SODA Aleksandar Nikolov, Kunal Talwar
2015	J	`jnl`	Efficient Algorithms for Privately Releasing Marginals via Convex Relaxations. Discret. Comput. Geom. Cynthia Dwork, Aleksandar Nikolov, Kunal Talwar
2015	J	`jnl`	Graphical balanced allocations and the (1 + β)-choice process. Random Struct. Algorithms Yuval Peres, Kunal Talwar, Udi Wieder
2015	—	`conf`	Navigation made personal: inferring driving preferences from GPS traces. SIGSPATIAL/GIS Daniel Delling, Andrew V. Goldberg, Moisés Goldszmidt, John Krumm, Kunal Talwar, Renato F. Werneck
2015	—	`conf`	Nearly Optimal Private LASSO. NIPS Kunal Talwar, Abhradeep Thakurta, Li Zhang
2015	J	`jnl`	Smooth Boolean functions are easy: efficient algorithms for low-sensitivity functions. CoRR Parikshit Gopalan, Noam Nisan, Rocco A. Servedio, Kunal Talwar, Avi Wigderson
2015	J	`jnl`	Smooth Boolean functions are easy: efficient algorithms for low-sensitivity functions. Electron. Colloquium Comput. Complex. Parikshit Gopalan, Noam Nisan, Rocco A. Servedio, Kunal Talwar, Avi Wigderson
2014	A*	`conf`	Analyze gauss: optimal bounds for privacy-preserving principal component analysis. STOC Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, Li Zhang
2014	—	`ch.`	Approximation Algorithms. Tractability Mohit Singh, Kunal Talwar
2014	—	`conf`	Balanced Allocations: A Simple Proof for the Heavily Loaded Case. ICALP (1) Kunal Talwar, Udi Wieder
2014	—	`conf`	Changing Bases: Multistage Optimization for Matroids and Matchings. ICALP (1) Anupam Gupta, Kunal Talwar, Udi Wieder
2014	J	`jnl`	Changing Bases: Multistage Optimization for Matroids and Matchings. CoRR Anupam Gupta, Kunal Talwar, Udi Wieder
2014	J	`jnl`	Consistent Weighted Sampling Made Fast, Small, and Easy. CoRR Bernhard Haeupler, Mark S. Manasse, Kunal Talwar
2014	A*	`conf`	Cops, robbers, and threatening skeletons: padded decomposition for minor-free graphs. STOC Ittai Abraham, Cyril Gavoille, Anupam Gupta, Ofer Neiman, Kunal Talwar
2014	J	`jnl`	Factorization Norms and Hereditary Discrepancy. CoRR Jirí Matousek, Aleksandar Nikolov, Kunal Talwar
2014	—	`conf`	Fully Dynamic All-Pairs Shortest Paths: Breaking the O(n) Barrier. APPROX-RANDOM Ittai Abraham, Shiri Chechik, Kunal Talwar
2014	A*	`conf`	Non-Uniform Graph Partitioning. SODA Robert Krauthgamer, Joseph Naor, Roy Schwartz, Kunal Talwar
2014	J	`jnl`	Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry. CoRR Kunal Talwar, Abhradeep Thakurta, Li Zhang
2014	A*	`conf`	Using Convex Relaxations for Efficiently and Privately Releasing Marginals. SoCG Cynthia Dwork, Aleksandar Nikolov, Kunal Talwar
2014	J	`jnl`	Vertex Sparsifiers: New Results from Old Techniques. SIAM J. Comput. Matthias Englert, Anupam Gupta, Robert Krauthgamer, Harald Räcke, Inbal Talgam-Cohen, Kunal Talwar
2013	J	`jnl`	Approximating Hereditary Discrepancy via Small Width Ellipsoids. CoRR Aleksandar Nikolov, Kunal Talwar
2013	J	`jnl`	Balanced Allocations: A Simple Proof for the Heavily Loaded Case. CoRR Kunal Talwar, Udi Wieder
2013	J	`jnl`	Cops, Robbers, and Threatening Skeletons: Padded Decomposition for Minor-Free Graphs. CoRR Ittai Abraham, Cyril Gavoille, Anupam Gupta, Ofer Neiman, Kunal Talwar
2013	J	`jnl`	Efficient Algorithms for Privately Releasing Marginals via Convex Relaxations. CoRR Cynthia Dwork, Aleksandar Nikolov, Kunal Talwar
2013	A*	`conf`	Minimum Makespan Scheduling with Low Rank Processing Times. SODA Aditya Bhaskara, Ravishankar Krishnaswamy, Kunal Talwar, Udi Wieder
2013	A*	`conf`	On differentially private low rank approximation. SODA Michael Kapralov, Kunal Talwar
2013	J	`jnl`	Random Rates for 0-Extension and Low-Diameter Decompositions. CoRR Anupam Gupta, Kunal Talwar
2013	J	`jnl`	Sparsest Cut on Bounded Treewidth Graphs: Algorithms and Hardness Results CoRR Anupam Gupta, Kunal Talwar, David Witmer
2013	A*	`conf`	Sparsest cut on bounded treewidth graphs: algorithms and hardness results. STOC Anupam Gupta, Kunal Talwar, David Witmer
2013	A*	`conf`	The geometry of differential privacy: the sparse and approximate cases. STOC Aleksandar Nikolov, Kunal Talwar, Li Zhang
2012	J	`jnl`	Efficient distributed approximation algorithms via probabilistic tree embeddings. Distributed Comput. Maleq Khan, Fabian Kuhn, Dahlia Malkhi, Gopal Pandurangan, Kunal Talwar
2012	J	`jnl`	On Privacy-Preserving Histograms CoRR Shuchi Chawla, Cynthia Dwork, Frank McSherry, Kunal Talwar
2012	J	`jnl`	The Geometry of Differential Privacy: the Sparse and Approximate Cases CoRR Aleksandar Nikolov, Kunal Talwar, Li Zhang
2012	A*	`conf`	Unconditional differentially private mechanisms for linear queries. STOC Aditya Bhaskara, Daniel Dadush, Ravishankar Krishnaswamy, Kunal Talwar
2011	J	`jnl`	Making Doubling Metrics Geodesic. Algorithmica Anupam Gupta, Kunal Talwar
2011	J	`jnl`	The Limits of Two-Party Differential Privacy. Electron. Colloquium Comput. Complex. Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, Salil P. Vadhan
2010	J	`jnl`	Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms CoRR Anupam Gupta, Aaron Roth, Grant Schoenebeck, Kunal Talwar
2010	—	`conf`	Constrained Non-monotone Submodular Maximization: Offline and Secretary Algorithms. WINE Anupam Gupta, Aaron Roth, Grant Schoenebeck, Kunal Talwar
2010	A*	`conf`	Differentially Private Combinatorial Optimization. SODA Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar
2010	A	`conf`	Hard Instances for Satisfiability and Quasi-one-way Functions. ICS Andrej Bogdanov, Kunal Talwar, Andrew Wan
2010	—	`conf`	Improving Integrality Gaps via Chvátal-Gomory Rounding. APPROX-RANDOM Mohit Singh, Kunal Talwar
2010	J	`jnl`	Inapproximability of Edge-Disjoint Paths and low congestion routing on undirected graphs. Comb. Matthew Andrews, Julia Chuzhoy, Venkatesan Guruswami, Sanjeev Khanna, Kunal Talwar, Lisa Zhang
2010	J	`jnl`	Lower Bounds on Near Neighbor Search via Metric Expansion CoRR Rina Panigrahy, Kunal Talwar, Udi Wieder
2010	A*	`conf`	Lower Bounds on Near Neighbor Search via Metric Expansion. FOCS Rina Panigrahy, Kunal Talwar, Udi Wieder
2010	A*	`conf`	On the geometry of differential privacy. STOC Moritz Hardt, Kunal Talwar
2010	A*	`conf`	The (1 + beta)-Choice Process and Weighted Balls-into-Bins. SODA Yuval Peres, Kunal Talwar, Udi Wieder
2010	A*	`conf`	The Limits of Two-Party Differential Privacy. FOCS Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, Salil P. Vadhan
2010	J	`jnl`	Ultra-low-dimensional embeddings for doubling metrics. J. ACM T.-H. Hubert Chan, Anupam Gupta, Kunal Talwar
2010	J	`jnl`	Vertex Sparsifiers: New Results from Old Techniques CoRR Matthias Englert, Anupam Gupta, Robert Krauthgamer, Harald Räcke, Inbal Talgam-Cohen, Kunal Talwar
2010	—	`conf`	Vertex Sparsifiers: New Results from Old Techniques. APPROX-RANDOM Matthias Englert, Anupam Gupta, Robert Krauthgamer, Harald Räcke, Inbal Talgam-Cohen, Kunal Talwar
2009	J	`jnl`	A push-relabel approximation algorithm for approximating the minimum-degree MST problem and its generalization to matroids. Theor. Comput. Sci. Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2009	J	`jnl`	Approximating the Bandwidth of Caterpillars. Algorithmica Uriel Feige, Kunal Talwar
2009	J	`jnl`	Differentially Private Approximation Algorithms CoRR Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar
2009	—	`conf`	Differentially Private Combinatorial Optimization. Parameterized complexity and approximation algorithms Kunal Talwar, Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth
2009	J	`jnl`	On the Geometry of Differential Privacy CoRR Moritz Hardt, Kunal Talwar
2009	A*	`conf`	Quincy: fair scheduling for distributed computing clusters. SOSP Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Talwar, Andrew V. Goldberg
2009	A*	`conf`	Secretary problems: weights and discounts. SODA Moshe Babaioff, Michael Dinitz, Anupam Gupta, Nicole Immorlica, Kunal Talwar
2009	A	`conf`	Virtual Ring Routing Trends. DISC Dahlia Malkhi, Siddhartha Sen, Kunal Talwar, Renato Fonseca F. Werneck, Udi Wieder
2009	J	`jnl`	What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs. Algorithmica Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2008	B	`conf`	A Constant Approximation Algorithm for the a prioriTraveling Salesman Problem. IPCO David B. Shmoys, Kunal Talwar
2008	A*	`conf`	A Geometric Approach to Lower Bounds for Approximate Near-Neighbor Search and Partial Match. FOCS Rina Panigrahy, Kunal Talwar, Udi Wieder
2008	—	`ch.`	Approximating Metric Spaces by Tree Metrics. Encyclopedia of Algorithms Jittat Fakcharoenphol, Satish Rao, Kunal Talwar
2008	A*	`conf`	Efficient distributed approximation algorithms via probabilistic tree embeddings. PODC Maleq Khan, Fabian Kuhn, Dahlia Malkhi, Gopal Pandurangan, Kunal Talwar
2008	B	`conf`	How to Complete a Doubling Metric. LATIN Anupam Gupta, Kunal Talwar
2008	A*	`conf`	Ultra-low-dimensional embeddings for doubling metrics. SODA T.-H. Hubert Chan, Anupam Gupta, Kunal Talwar
2007	J	`jnl`	A primal-dual algorithm for computing Fisher equilibrium in the absence of gross substitutability property. Theor. Comput. Sci. Dinesh Garg, Kamal Jain, Kunal Talwar, Vijay V. Vazirani
2007	A*	`conf`	Balanced allocations: the weighted case. STOC Kunal Talwar, Udi Wieder
2007	A*	`conf`	Balloon Popping With Applications to Ascending Auctions. FOCS Nicole Immorlica, Anna R. Karlin, Mohammad Mahdian, Kunal Talwar
2007	A*	`conf`	Hardness of routing with congestion in directed graphs. STOC Julia Chuzhoy, Venkatesan Guruswami, Sanjeev Khanna, Kunal Talwar
2007	J	`jnl`	How to Complete a Doubling Metric CoRR Anupam Gupta, Kunal Talwar
2007	J	`jnl`	Inapproximability of edge-disjoint paths and low congestion routing on undirected graphs. Electron. Colloquium Comput. Complex. Matthew Andrews, Julia Chuzhoy, Venkatesan Guruswami, Sanjeev Khanna, Kunal Talwar, Lisa Zhang
2007	A*	`conf`	Mechanism Design via Differential Privacy. FOCS Frank McSherry, Kunal Talwar
2007	A*	`conf`	Privacy, accuracy, and consistency too: a holistic solution to contingency table release. PODS Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar
2007	A*	`conf`	Reconstructing approximate tree metrics. PODC Ittai Abraham, Mahesh Balakrishnan, Fabian Kuhn, Dahlia Malkhi, Venugopalan Ramasubramanian, Kunal Talwar
2007	A*	`conf`	The price of privacy and the limits of LP decoding. STOC Cynthia Dwork, Frank McSherry, Kunal Talwar
2006	—	`conf`	A Push-Relabel Algorithm for Approximating Degree Bounded MSTs. ICALP (1) Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2006	A*	`conf`	Approximating unique games. SODA Anupam Gupta, Kunal Talwar
2006	J	`jnl`	Hardness of Low Congestion Routing in Directed Graphs. Electron. Colloquium Comput. Complex. Venkatesan Guruswami, Kunal Talwar
2005	—	`conf`	A Primal-Dual Algorithm for Computing Fisher Equilibrium in the Absence of Gross Substitutability Property. WINE Dinesh Garg, Kamal Jain, Kunal Talwar, Vijay V. Vazirani
2005	—	`conf`	A Simple Characterization for Truth-Revealing Single-Item Auctions. WINE Kamal Jain, Aranyak Mehta, Kunal Talwar, Vijay V. Vazirani
2005	—	`conf`	Approximating the Bandwidth of Caterpillars. APPROX-RANDOM Uriel Feige, Kunal Talwar
2005	—	`conf`	Click Fraud Resistant Methods for Learning Click-Through Rates. WINE Nicole Immorlica, Kamal Jain, Mohammad Mahdian, Kunal Talwar
2005	A	`conf`	On Privacy-Preserving Histograms. UAI Shuchi Chawla, Cynthia Dwork, Frank McSherry, Kunal Talwar
2005	A*	`conf`	The Generalized Deadlock Resolution Problem. ICALP Kamal Jain, Mohammad Taghi Hajiaghayi, Kunal Talwar
2005	—	`conf`	What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs. APPROX-RANDOM Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2004	J	`jnl`	A tight bound on approximating arbitrary metrics by tree metrics. J. Comput. Syst. Sci. Jittat Fakcharoenphol, Satish Rao, Kunal Talwar
2004	A*	`conf`	Approximate classification via earthmover metrics. SODA Aaron Archer, Jittat Fakcharoenphol, Chris Harrelson, Robert Krauthgamer, Kunal Talwar, Éva Tardos
2004	J	`jnl`	Approximating metrics by tree metrics. SIGACT News Jittat Fakcharoenphol, Satish Rao, Kunal Talwar
2004	A*	`conf`	Bypassing the embedding: algorithms for low dimensional metrics. STOC Kunal Talwar
2004	A*	`conf`	The complexity of pure Nash equilibria. STOC Alex Fabrikant, Christos H. Papadimitriou, Kunal Talwar
2003	A*	`conf`	A tight bound on approximating arbitrary metrics by tree metrics. STOC Jittat Fakcharoenphol, Satish Rao, Kunal Talwar
2003	J	`jnl`	An Approximate Truthful Mechanism for Combinatorial Auctions with Single Parameter Agents. Internet Math. Aaron Archer, Christos H. Papadimitriou, Kunal Talwar, Éva Tardos
2003	—	`conf`	An Improved Decomposition Theorem for Graphs Excluding a Fixed Minor. RANDOM-APPROX Jittat Fakcharoenphol, Kunal Talwar
2003	A*	`conf`	An approximate truthful mechanism for combinatorial auctions with single parameter agents. SODA Aaron Archer, Christos H. Papadimitriou, Kunal Talwar, Éva Tardos
2003	A*	`conf`	An improved approximation algorithm for the 0-extension problem. SODA Jittat Fakcharoenphol, Chris Harrelson, Satish Rao, Kunal Talwar
2003	A*	`conf`	Paths, Trees, and Minimum Latency Tours. FOCS Kamalika Chaudhuri, Brighten Godfrey, Satish Rao, Kunal Talwar
2003	A	`conf`	The Price of Truth: Frugality in Truthful Mechanisms. STACS Kunal Talwar
2002	B	`conf`	The Single-Sink Buy-at-Bulk LP Has Constant Integrality Gap. IPCO Kunal Talwar
2001	A*	`conf`	Detecting Format String Vulnerabilities with Type Qualifiers. USENIX Security Symposium Umesh Shankar, Kunal Talwar, Jeffrey S. Foster, David A. Wagner