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2020 – today
- 2024
[j18]Jason M. Altschuler
, Jinho Bok, Kunal Talwar:
On the Privacy of Noisy Stochastic Gradient Descent for Convex Optimization. SIAM J. Comput. 53(4): 969-1001 (2024)- [c105]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou:
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages. ICML 2024 - [c104]Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar:
Differentially Private Heavy Hitter Detection using Federated Analytics. SaTML 2024: 512-533 - [c103]Guy N. Rothblum, Eran Omri, Junye Chen, Kunal Talwar:
PINE: Efficient Verification of a Euclidean Norm Bound of a Secret-Shared Vector. USENIX Security Symposium 2024 - [i77]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou:
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages. CoRR abs/2404.10201 (2024) - [i76]Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar:
Private Online Learning via Lazy Algorithms. CoRR abs/2406.03620 (2024) - [i75]Hilal Asi, Fabian Boemer, Nicholas Genise, Muhammad Haris Mughees, Tabitha Ogilvie, Rehan Rishi, Guy N. Rothblum, Kunal Talwar, Karl Tarbe, Ruiyu Zhu, Marco Zuliani:
Scalable Private Search with Wally. CoRR abs/2406.06761 (2024) - [i74]Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar:
Instance-Optimal Private Density Estimation in the Wasserstein Distance. CoRR abs/2406.19566 (2024) - 2023
- [c102]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Online Prediction from Experts: Separations and Faster Rates. COLT 2023: 674-699 - [c101]Jason M. Altschuler, Kunal Talwar:
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling. COLT 2023: 2509-2510 - [c100]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. ICML 2023: 1107-1120 - [c99]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar:
Fast Optimal Locally Private Mean Estimation via Random Projections. NeurIPS 2023 - [c98]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy. SODA 2023: 4966-4981 - [e1]Kunal Talwar:
4th Symposium on Foundations of Responsible Computing, FORC 2023, June 7-9, 2023, Stanford University, California, USA. LIPIcs 256, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2023, ISBN 978-3-95977-272-3 [contents] - [i73]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. CoRR abs/2302.14154 (2023) - [i72]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar:
Fast Optimal Locally Private Mean Estimation via Random Projections. CoRR abs/2306.04444 (2023) - [i71]Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar:
Differentially Private Heavy Hitter Detection using Federated Analytics. CoRR abs/2307.11749 (2023) - [i70]Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina, Vitaly Feldman, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver 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:
Samplable Anonymous Aggregation for Private Federated Data Analysis. CoRR abs/2307.15017 (2023) - [i69]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR abs/2307.15835 (2023) - [i68]Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman, Jan Honza Silovsky, Kunal Talwar, Tatiana Likhomanenko:
Federated Learning with Differential Privacy for End-to-End Speech Recognition. CoRR abs/2310.00098 (2023) - [i67]Guy N. Rothblum, Eran Omri, Junye Chen, Kunal Talwar:
PINE: Efficient Norm-Bound Verification for Secret-Shared Vectors. CoRR abs/2311.10237 (2023) - 2022
- [c97]Kunal Talwar:
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation. FORC 2022: 7:1-7:16 - [c96]Hilal Asi, Vitaly Feldman, Kunal Talwar:
Optimal Algorithms for Mean Estimation under Local Differential Privacy. ICML 2022: 1046-1056 - [c95]Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar:
Private frequency estimation via projective geometry. ICML 2022: 6418-6433 - [c94]Lorenzo Orecchia, Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar:
Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML 2022: 17071-17093 - [c93]Jason M. Altschuler, Kunal Talwar:
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss. NeurIPS 2022 - [c92]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS 2022 - [c91]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS 2022 - [c90]Congzheng Song, Filip Granqvist, Kunal Talwar:
FLAIR: Federated Learning Annotated Image Repository. NeurIPS 2022 - [i66]Kunal Talwar:
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation. CoRR abs/2202.10618 (2022) - [i65]Vitaly Feldman, Jelani Nelson, Huy Le Nguyen, Kunal Talwar:
Private Frequency Estimation via Projective Geometry. CoRR abs/2203.00194 (2022) - [i64]Hilal Asi, Vitaly Feldman, Kunal Talwar:
Optimal Algorithms for Mean Estimation under Local Differential Privacy. CoRR abs/2205.02466 (2022) - [i63]Jason M. Altschuler, Kunal Talwar:
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss. CoRR abs/2205.13710 (2022) - [i62]Congzheng Song, Filip Granqvist, Kunal Talwar:
FLAIR: Federated Learning Annotated Image Repository. CoRR abs/2207.08869 (2022) - [i61]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy. CoRR abs/2208.04591 (2022) - [i60]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. CoRR abs/2210.13497 (2022) - [i59]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Online Prediction from Experts: Separations and Faster Rates. CoRR abs/2210.13537 (2022) - [i58]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:
Private Federated Statistics in an Interactive Setting. CoRR abs/2211.10082 (2022) - [i57]Jason M. Altschuler, Kunal Talwar:
Concentration of the Langevin Algorithm's Stationary Distribution. CoRR abs/2212.12629 (2022) - 2021
- [j17]Jason M. Altschuler, Kunal Talwar:
Online Learning over a Finite Action Set with Limited Switching. Math. Oper. Res. 46(1): 179-203 (2021) - [c89]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. FOCS 2021: 954-964 - [c88]Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar:
Private Adaptive Gradient Methods for Convex Optimization. ICML 2021: 383-392 - [c87]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. ICML 2021: 393-403 - [c86]Vitaly Feldman, Kunal Talwar:
Lossless Compression of Efficient Private Local Randomizers. ICML 2021: 3208-3219 - [c85]Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer:
Characterizing Structural Regularities of Labeled Data in Overparameterized Models. ICML 2021: 5034-5044 - [c84]Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar:
When is memorization of irrelevant training data necessary for high-accuracy learning? STOC 2021: 123-132 - [i56]Vitaly Feldman, Kunal Talwar:
Lossless Compression of Efficient Private Local Randomizers. CoRR abs/2102.12099 (2021) - [i55]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in 𝓁1 Geometry. CoRR abs/2103.01516 (2021) - [i54]Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar:
Private Adaptive Gradient Methods for Convex Optimization. CoRR abs/2106.13756 (2021) - 2020
- [c83]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. NeurIPS 2020 - [c82]Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar:
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. NeurIPS 2020 - [c81]Arun Ganesh, Kunal Talwar:
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. NeurIPS 2020 - [c80]Kunal Talwar:
On the Error Resistance of Hinge-Loss Minimization. NeurIPS 2020 - [c79]Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private stochastic convex optimization: optimal rates in linear time. STOC 2020: 439-449 - [i53]Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta:
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation. CoRR abs/2001.03618 (2020) - [i52]Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer:
Exploring the Memorization-Generalization Continuum in Deep Learning. CoRR abs/2002.03206 (2020) - [i51]Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in Linear Time. CoRR abs/2005.04763 (2020) - [i50]Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar:
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. CoRR abs/2006.06914 (2020) - [i49]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. CoRR abs/2010.13639 (2020) - [i48]Arun Ganesh, Kunal Talwar:
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. CoRR abs/2010.14658 (2020) - [i47]Kunal Talwar:
On the Error Resistance of Hinge Loss Minimization. CoRR abs/2012.00989 (2020) - [i46]Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar:
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? CoRR abs/2012.06421 (2020) - [i45]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. CoRR abs/2012.12803 (2020)
2010 – 2019
- 2019
- [j16]Ittai Abraham, Cyril Gavoille, Anupam Gupta, Ofer Neiman, Kunal Talwar:
Cops, Robbers, and Threatening Skeletons: Padded Decomposition for Minor-Free Graphs. SIAM J. Comput. 48(3): 1120-1145 (2019) - [c78]Anupam Gupta, Tomer Koren, Kunal Talwar:
Better Algorithms for Stochastic Bandits with Adversarial Corruptions. COLT 2019: 1562-1578 - [c77]Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. ICML 2019: 1764-1773 - [c76]Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta:
Private Stochastic Convex Optimization with Optimal Rates. NeurIPS 2019: 11279-11288 - [c75]Kunal Talwar:
Computational Separations between Sampling and Optimization. NeurIPS 2019: 14997-15007 - [c74]Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta:
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. SODA 2019: 2468-2479 - [c73]Jingcheng Liu, Kunal Talwar:
Private selection from private candidates. STOC 2019: 298-309 - [i44]Anupam Gupta, Tomer Koren, Kunal Talwar:
Better Algorithms for Stochastic Bandits with Adversarial Corruptions. CoRR abs/1902.08647 (2019) - [i43]Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. CoRR abs/1904.10120 (2019) - [i42]Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta:
Private Stochastic Convex Optimization with Optimal Rates. CoRR abs/1908.09970 (2019) - [i41]Ilya Mironov, Kunal Talwar, Li Zhang:
Rényi Differential Privacy of the Sampled Gaussian Mechanism. CoRR abs/1908.10530 (2019) - [i40]Kunal Talwar:
Computational Separations between Sampling and Optimization. CoRR abs/1911.02074 (2019) - 2018
- [c72]Jason M. Altschuler, Kunal Talwar:
Online learning over a finite action set with limited switching. COLT 2018: 1569-1573 - [c71]Daniel Dadush, Aleksandar Nikolov, Kunal Talwar, Nicole Tomczak-Jaegermann:
Balancing Vectors in Any Norm. FOCS 2018: 1-10 - [c70]Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta:
Privacy Amplification by Iteration. FOCS 2018: 521-532 - [c69]H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang:
Learning Differentially Private Recurrent Language Models. ICLR (Poster) 2018 - [c68]Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson:
Scalable Private Learning with PATE. ICLR 2018 - [c67]Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar:
Online Linear Quadratic Control. ICML 2018: 1028-1037 - [c66]Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry:
Adversarially Robust Generalization Requires More Data. NeurIPS 2018: 5019-5031 - [i39]Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson:
Scalable Private Learning with PATE. CoRR abs/1802.08908 (2018) - [i38]Jason M. Altschuler, Kunal Talwar:
Online learning over a finite action set with limited switching. CoRR abs/1803.01548 (2018) - [i37]Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry:
Adversarially Robust Generalization Requires More Data. CoRR abs/1804.11285 (2018) - [i36]Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar:
Online Linear Quadratic Control. CoRR abs/1806.07104 (2018) - [i35]Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta:
Privacy Amplification by Iteration. CoRR abs/1808.06651 (2018) - [i34]Jingcheng Liu, Kunal Talwar:
Private Selection from Private Candidates. CoRR abs/1811.07971 (2018) - [i33]Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta:
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. CoRR abs/1811.12469 (2018) - 2017
- [c65]Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang:
On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. CSF 2017: 1-6 - [c64]Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar:
Short and Deep: Sketching and Neural Networks. ICLR (Workshop) 2017 - [c63]Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar:
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR 2017 - [c62]Anupam Gupta, R. Ravi, Kunal Talwar, Seeun William Umboh:
LAST but not Least: Online Spanners for Buy-at-Bulk. SODA 2017: 589-599 - [i32]Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang:
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CoRR abs/1708.08022 (2017) - [i31]Petros Maniatis, Ilya Mironov, Kunal Talwar:
Oblivious Stash Shuffle. CoRR abs/1709.07553 (2017) - [i30]H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang:
Learning Differentially Private Language Models Without Losing Accuracy. CoRR abs/1710.06963 (2017) - 2016
- [j15]Aleksandar Nikolov, Kunal Talwar, Li Zhang:
The Geometry of Differential Privacy: The Small Database and Approximate Cases. SIAM J. Comput. 45(2): 575-616 (2016) - [c61]Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang:
Deep Learning with Differential Privacy. CCS 2016: 308-318 - [c60]Parikshit Gopalan, Noam Nisan, Rocco A. Servedio, Kunal Talwar, Avi Wigderson:
Smooth Boolean Functions are Easy: Efficient Algorithms for Low-Sensitivity Functions. ITCS 2016: 59-70 - [r2]Jittat Fakcharoenphol, Satish Rao, Kunal Talwar:
Approximating Metric Spaces by Tree Metrics. Encyclopedia of Algorithms 2016: 113-116 - [i29]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:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR abs/1603.04467 (2016) - [i28]Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar:
Sketching and Neural Networks. CoRR abs/1604.05753 (2016) - [i27]Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang:
Deep Learning with Differential Privacy. CoRR abs/1607.00133 (2016) - [i26]Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar:
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. CoRR abs/1610.05755 (2016) - [i25]Anupam Gupta, R. Ravi, Kunal Talwar, Seeun William Umboh:
LAST but not Least: Online Spanners for Buy-at-Bulk. CoRR abs/1611.00052 (2016) - 2015
- [j14]Cynthia Dwork, Aleksandar Nikolov, Kunal Talwar:
Efficient Algorithms for Privately Releasing Marginals via Convex Relaxations. Discret. Comput. Geom. 53(3): 650-673 (2015) - [j13]Yuval Peres, Kunal Talwar, Udi Wieder:
Graphical balanced allocations and the (1 + β)-choice process. Random Struct. Algorithms 47(4): 760-775 (2015) - [c59]Daniel Delling, Andrew V. Goldberg, Moisés Goldszmidt, John Krumm, Kunal Talwar, Renato F. Werneck:
Navigation made personal: inferring driving preferences from GPS traces. SIGSPATIAL/GIS 2015: 31:1-31:9 - [c58]Kunal Talwar, Abhradeep Thakurta, Li Zhang:
Nearly Optimal Private LASSO. NIPS 2015: 3025-3033 - [c57]Aleksandar Nikolov, Kunal Talwar:
Approximating Hereditary Discrepancy via Small Width Ellipsoids. SODA 2015: 324-336 - [i24]Parikshit Gopalan, Noam Nisan, Rocco A. Servedio, Kunal Talwar, Avi Wigderson:
Smooth Boolean functions are easy: efficient algorithms for low-sensitivity functions. CoRR abs/1508.02420 (2015) - [i23]Parikshit Gopalan, Noam Nisan, Rocco A. Servedio, Kunal Talwar, Avi Wigderson:
Smooth Boolean functions are easy: efficient algorithms for low-sensitivity functions. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [j12]Matthias Englert, Anupam Gupta, Robert Krauthgamer, Harald Räcke, Inbal Talgam-Cohen, Kunal Talwar:
Vertex Sparsifiers: New Results from Old Techniques. SIAM J. Comput. 43(4): 1239-1262 (2014) - [c56]Ittai Abraham, Shiri Chechik, Kunal Talwar:
Fully Dynamic All-Pairs Shortest Paths: Breaking the O(n) Barrier. APPROX-RANDOM 2014: 1-16 - [c55]Cynthia Dwork, Aleksandar Nikolov, Kunal Talwar:
Using Convex Relaxations for Efficiently and Privately Releasing Marginals. SoCG 2014: 261 - [c54]Anupam Gupta, Kunal Talwar, Udi Wieder:
Changing Bases: Multistage Optimization for Matroids and Matchings. ICALP (1) 2014: 563-575 - [c53]Kunal Talwar, Udi Wieder:
Balanced Allocations: A Simple Proof for the Heavily Loaded Case. ICALP (1) 2014: 979-990 - [c52]Robert Krauthgamer, Joseph Naor, Roy Schwartz, Kunal Talwar:
Non-Uniform Graph Partitioning. SODA 2014: 1229-1243 - [c51]