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2020 – today
- 2023
[c98]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy. SODA 2023: 4966-4981- [i67]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. CoRR abs/2302.14154 (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]Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, Li Zhang:
Analyze gauss: optimal bounds for privacy-preserving principal component analysis. STOC 2014: 11-20 - [c50]Ittai Abraham, Cyril Gavoille, Anupam Gupta, Ofer Neiman, Kunal Talwar:
Cops, robbers, and threatening skeletons: padded decomposition for minor-free graphs. STOC 2014: 79-88 - [p1]Mohit Singh, Kunal Talwar:
Approximation Algorithms. Tractability 2014: 231-259 - [i22]Anupam Gupta, Kunal Talwar, Udi Wieder:
Changing Bases: Multistage Optimization for Matroids and Matchings. CoRR abs/1404.3768 (2014) - [i21]Jirí Matousek, Aleksandar Nikolov, Kunal Talwar:
Factorization Norms and Hereditary Discrepancy. CoRR abs/1408.1376 (2014) - [i20]Bernhard Haeupler, Mark S. Manasse, Kunal Talwar:
Consistent Weighted Sampling Made Fast, Small, and Easy. CoRR abs/1410.4266 (2014) - [i19]Kunal Talwar, Abhradeep Thakurta, Li Zhang:
Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry. CoRR abs/1411.5417 (2014) - 2013
- [c49]Aditya Bhaskara, Ravishankar Krishnaswamy, Kunal Talwar, Udi Wieder:
Minimum Makespan Scheduling with Low Rank Processing Times. SODA 2013: 937-947 - [c48]Michael Kapralov, Kunal Talwar:
On differentially private low rank approximation. SODA 2013: 1395-1414 - [c47]Anupam Gupta, Kunal Talwar, David Witmer:
Sparsest cut on bounded treewidth graphs: algorithms and hardness results. STOC 2013: 281-290 - [c46]Aleksandar Nikolov, Kunal Talwar, Li Zhang:
The geometry of differential privacy: the sparse and approximate cases. STOC 2013: 351-360 - [i18]Anupam Gupta, Kunal Talwar, David Witmer:
Sparsest Cut on Bounded Treewidth Graphs: Algorithms and Hardness Results. CoRR abs/1305.1347 (2013) - [i17]Anupam Gupta, Kunal Talwar:
Random Rates for 0-Extension and Low-Diameter Decompositions. CoRR abs/1307.5582 (2013) - [i16]Cynthia Dwork, Aleksandar Nikolov, Kunal Talwar:
Efficient Algorithms for Privately Releasing Marginals via Convex Relaxations. CoRR abs/1308.1385 (2013) - [i15]Kunal Talwar, Udi Wieder:
Balanced Allocations: A Simple Proof for the Heavily Loaded Case. CoRR abs/1310.5367 (2013) - [i14]Ittai Abraham, Cyril Gavoille, Anupam Gupta, Ofer Neiman, Kunal Talwar:
Cops, Robbers, and Threatening Skeletons: Padded Decomposition for Minor-Free Graphs. CoRR abs/1311.3048 (2013) - [i13]Aleksandar Nikolov, Kunal Talwar:
Approximating Hereditary Discrepancy via Small Width Ellipsoids. CoRR abs/1311.6204 (2013) - 2012
- [j11]Maleq Khan, Fabian Kuhn, Dahlia Malkhi, Gopal Pandurangan, Kunal Talwar:
Efficient distributed approximation algorithms via probabilistic tree embeddings. Distributed Comput. 25(3): 189-205 (2012) - [c45]Aditya Bhaskara, Daniel Dadush, Ravishankar Krishnaswamy, Kunal Talwar:
Unconditional differentially private mechanisms for linear queries. STOC 2012: 1269-1284 - [i12]Shuchi Chawla, Cynthia Dwork, Frank McSherry, Kunal Talwar:
On Privacy-Preserving Histograms. CoRR abs/1207.1371 (2012) - [i11]Aleksandar Nikolov, Kunal Talwar, Li Zhang:
The Geometry of Differential Privacy: the Sparse and Approximate Cases. CoRR abs/1212.0297 (2012) - 2011
- [j10]Anupam Gupta, Kunal Talwar:
Making Doubling Metrics Geodesic. Algorithmica 59(1): 66-80 (2011) - [i10]Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, Salil P. Vadhan:
The Limits of Two-Party Differential Privacy. Electron. Colloquium Comput. Complex. TR11 (2011) - 2010
- [j9]Matthew Andrews, Julia Chuzhoy, Venkatesan Guruswami, Sanjeev Khanna, Kunal Talwar, Lisa Zhang:
Inapproximability of Edge-Disjoint Paths and low congestion routing on undirected graphs. Comb. 30(5): 485-520 (2010) - [j8]T.-H. Hubert Chan, Anupam Gupta, Kunal Talwar:
Ultra-low-dimensional embeddings for doubling metrics. J. ACM 57(4): 21:1-21:26 (2010) - [c44]Matthias Englert, Anupam Gupta, Robert Krauthgamer, Harald Räcke, Inbal Talgam-Cohen, Kunal Talwar:
Vertex Sparsifiers: New Results from Old Techniques. APPROX-RANDOM 2010: 152-165 - [c43]Mohit Singh, Kunal Talwar:
Improving Integrality Gaps via Chvátal-Gomory Rounding. APPROX-RANDOM 2010: 366-379 - [c42]Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, Salil P. Vadhan:
The Limits of Two-Party Differential Privacy. FOCS 2010: 81-90 - [c41]