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Abhradeep Thakurta
Person information
- affiliation: Microsoft Research Silicon Valley
- affiliation: Pennsylvania State University, Computer Science and Engineering Department
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2020 – today
- 2024
- [c58]Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava:
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components. AISTATS 2024: 1702-1710 - [c57]Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Learning with Public Features. AISTATS 2024: 4150-4158 - [c56]Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta:
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. ICLR 2024 - [c55]Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta:
Privacy Amplification for Matrix Mechanisms. ICLR 2024 - [c54]Gavin Brown, Krishnamurthy Dj Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Guha Thakurta:
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. ICML 2024 - [c53]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta:
Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements. ICML 2024 - [i56]Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Thakurta:
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. CoRR abs/2402.13531 (2024) - [i55]Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta:
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR abs/2404.16706 (2024) - [i54]Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Thakurta:
Optimal Rates for DP-SCO with a Single Epoch and Large Batches. CoRR abs/2406.02716 (2024) - 2023
- [j5]Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta:
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. J. Artif. Intell. Res. 77: 1113-1201 (2023) - [j4]Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky
:
Differentially Private Image Classification from Features. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky
:
Towards Large Scale Transfer Learning for Differentially Private Image Classification. Trans. Mach. Learn. Res. 2023 (2023) - [c52]Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay:
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets. COLT 2023: 1730-1773 - [c51]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta:
Differentially Private and Lazy Online Convex Optimization. COLT 2023: 4599-4632 - [c50]Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. ICLR 2023 - [c49]Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta:
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. ICML 2023: 5924-5963 - [c48]Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? ICML 2023: 10611-10627 - [c47]Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. ICML 2023: 17784-17807 - [c46]Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS 2023 - [c45]Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. NeurIPS 2023 - [c44]Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS 2023 - [c43]Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot:
Training Private Models That Know What They Don't Know. NeurIPS 2023 - [i53]Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. CoRR abs/2302.07975 (2023) - [i52]Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? CoRR abs/2302.09483 (2023) - [i51]Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR abs/2302.09699 (2023) - [i50]Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta:
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR abs/2303.00654 (2023) - [i49]Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek:
Differentially Private Stream Processing at Scale. CoRR abs/2303.18086 (2023) - [i48]Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath
, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang:
Challenges towards the Next Frontier in Privacy. CoRR abs/2304.06929 (2023) - [i47]Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. CoRR abs/2305.13209 (2023) - [i46]Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot:
Training Private Models That Know What They Don't Know. CoRR abs/2305.18393 (2023) - [i45]Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR abs/2306.08153 (2023) - [i44]Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta:
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. CoRR abs/2310.06771 (2023) - [i43]Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Learning with Public Features. CoRR abs/2310.15454 (2023) - [i42]Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta:
Privacy Amplification for Matrix Mechanisms. CoRR abs/2310.15526 (2023) - [i41]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta:
Improved Differentially Private and Lazy Online Convex Optimization. CoRR abs/2312.11534 (2023) - 2022
- [c42]Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. COLT 2022: 1126-1166 - [c41]Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi:
Private Matrix Approximation and Geometry of Unitary Orbits. COLT 2022: 3547-3588 - [c40]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. ICML 2022: 517-535 - [c39]Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta:
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. NeurIPS 2022 - [c38]Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta:
When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS 2022 - [i40]Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, Abhradeep Thakurta:
Toward Training at ImageNet Scale with Differential Privacy. CoRR abs/2201.12328 (2022) - [i39]Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta:
Private Online Prefix Sums via Optimal Matrix Factorizations. CoRR abs/2202.08312 (2022) - [i38]Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay:
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization. CoRR abs/2204.01585 (2022) - [i37]Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky
:
Large Scale Transfer Learning for Differentially Private Image Classification. CoRR abs/2205.02973 (2022) - [i36]Matthew Jagielski, Om Thakkar, Florian Tramèr
, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. CoRR abs/2207.00099 (2022) - [i35]Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta:
When Does Differentially Private Learning Not Suffer in High Dimensions? CoRR abs/2207.00160 (2022) - [i34]Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Guha Thakurta, Nisheeth K. Vishnoi:
Private Matrix Approximation and Geometry of Unitary Orbits. CoRR abs/2207.02794 (2022) - [i33]Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression via Adaptive Clipping. CoRR abs/2207.04686 (2022) - [i32]Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta:
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR abs/2210.01864 (2022) - [i31]Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot, Abhradeep Thakurta:
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search. CoRR abs/2210.02156 (2022) - [i30]Soumyabrata Pal, Prateek Varshney, Prateek Jain, Abhradeep Guha Thakurta, Gagan Madan, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava:
Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition. CoRR abs/2210.03505 (2022) - [i29]Adam D. Smith, Abhradeep Thakurta:
Fully Adaptive Composition for Gaussian Differential Privacy. CoRR abs/2210.17520 (2022) - [i28]Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta:
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. CoRR abs/2211.06530 (2022) - [i27]Harsh Mehta, Walid Krichene, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky
:
Differentially Private Image Classification from Features. CoRR abs/2211.13403 (2022) - 2021
- [c37]Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson:
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. AAAI 2021: 9312-9321 - [c36]Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta:
Evading the Curse of Dimensionality in Unconstrained Private GLMs. AISTATS 2021: 2638-2646 - [c35]Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta:
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. COLT 2021: 2717-2746 - [c34]Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. ICML 2021: 1877-1887 - [c33]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML 2021: 5213-5225 - [c32]Samuel Deng, Sanjam Garg
, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta:
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks. NeurIPS 2021: 10862-10875 - [c31]Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta:
Differentially Private Model Personalization. NeurIPS 2021: 29723-29735 - [c30]Nicholas Carlini, Samuel Deng, Sanjam Garg
, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramèr
:
Is Private Learning Possible with Instance Encoding? SP 2021: 410-427 - [c29]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. SP 2021: 866-882 - [i26]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR abs/2101.04535 (2021) - [i25]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR abs/2103.00039 (2021) - [i24]Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. CoRR abs/2107.09802 (2021) - [i23]Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain:
Node-Level Differentially Private Graph Neural Networks. CoRR abs/2111.15521 (2021) - [i22]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. CoRR abs/2112.00193 (2021) - 2020
- [j2]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta:
Practical Locally Private Heavy Hitters. J. Mach. Learn. Res. 21: 16:1-16:42 (2020) - [c28]Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. NeurIPS 2020 - [c27]Adam D. Smith, Shuang Song, Abhradeep Thakurta:
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. NeurIPS 2020 - [i21]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) - [i20]Daniel Kifer, Solomon Messing, Aaron Roth, Abhradeep Thakurta, Danfeng Zhang:
Guidelines for Implementing and Auditing Differentially Private Systems. CoRR abs/2002.04049 (2020) - [i19]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta:
Obliviousness Makes Poisoning Adversaries Weaker. CoRR abs/2003.12020 (2020) - [i18]Shuang Song, Om Thakkar, Abhradeep Thakurta:
Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. CoRR abs/2006.06783 (2020) - [i17]Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. CoRR abs/2007.06605 (2020) - [i16]Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson:
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. CoRR abs/2007.14191 (2020) - [i15]Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta:
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. CoRR abs/2008.06570 (2020) - [i14]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr:
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR abs/2011.05315 (2020)
2010 – 2019
- 2019
- [c26]Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta:
Private Stochastic Convex Optimization with Optimal Rates. NeurIPS 2019: 11279-11288 - [c25]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 - [c24]Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, Lun Wang:
Towards Practical Differentially Private Convex Optimization. IEEE Symposium on Security and Privacy 2019: 299-316 - [c23]Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Guha Thakurta:
Privacy-preserving Data Mining in Industry. WSDM 2019: 840-841 - [c22]Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Thakurta:
Privacy-preserving Data Mining in Industry. WWW (Companion Volume) 2019: 1308-1310 - [i13]Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta:
Private Stochastic Convex Optimization with Optimal Rates. CoRR abs/1908.09970 (2019) - 2018
- [j1]Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma
:
Erasure-Resilient Property Testing. SIAM J. Comput. 47(2): 295-329 (2018) - [c21]Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta:
Privacy Amplification by Iteration. FOCS 2018: 521-532 - [c20]Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion Revisited. ICML 2018: 2220-2229 - [c19]Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar:
Model-Agnostic Private Learning. NeurIPS 2018: 7102-7112 - [i12]Raef Bassily, Om Thakkar, Abhradeep Thakurta:
Model-Agnostic Private Learning via Stability. CoRR abs/1803.05101 (2018) - [i11]Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta:
Privacy Amplification by Iteration. CoRR abs/1808.06651 (2018) - [i10]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
- [c18]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta:
Practical Locally Private Heavy Hitters. NIPS 2017: 2288-2296 - [c17]Adam D. Smith, Abhradeep Thakurta, Jalaj Upadhyay:
Is Interaction Necessary for Distributed Private Learning? IEEE Symposium on Security and Privacy 2017: 58-77 - [i9]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta:
Practical Locally Private Heavy Hitters. CoRR abs/1707.04982 (2017) - [i8]Prateek Jain, Om Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion, Revisited. CoRR abs/1712.09765 (2017) - 2016
- [c16]Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma
:
Erasure-Resilient Property Testing. ICALP 2016: 91:1-91:15 - [r1]Abhradeep Thakurta:
Beyond Worst Case Sensitivity in Private Data Analysis. Encyclopedia of Algorithms 2016: 192-199 - [i7]Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma:
Erasure-Resilient Property Testing. CoRR abs/1607.05786 (2016) - 2015
- [c15]Kunal Talwar, Abhradeep Thakurta, Li Zhang:
Nearly Optimal Private LASSO. NIPS 2015: 3025-3033 - [c14]Nikita Mishra, Abhradeep Thakurta:
(Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits. UAI 2015: 592-601 - [i6]Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta, Oliver Williams:
To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout. CoRR abs/1503.02031 (2015) - 2014
- [c13]Raef Bassily, Adam D. Smith, Abhradeep Thakurta:
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. FOCS 2014: 464-473 - [c12]Prateek Jain, Abhradeep Guha Thakurta:
(Near) Dimension Independent Risk Bounds for Differentially Private Learning. ICML 2014: 476-484 - [c11]Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, Li Zhang:
Analyze gauss: optimal bounds for privacy-preserving principal component analysis. STOC 2014: 11-20 - [i5]Raef Bassily, Adam D. Smith, Abhradeep Thakurta:
Private Empirical Risk Minimization, Revisited. CoRR abs/1405.7085 (2014) - [i4]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
- [c10]Abhradeep Thakurta, Adam D. Smith:
Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. COLT 2013: 819-850 - [c9]Prateek Jain, Abhradeep Thakurta:
Differentially Private Learning with Kernels. ICML (3) 2013: 118-126 - [c8]Abhradeep Guha Thakurta, Adam D. Smith:
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. NIPS 2013: 2733-2741 - [c7]Kashyap Dixit, Madhav Jha, Sofya Raskhodnikova, Abhradeep Thakurta:
Testing the Lipschitz Property over Product Distributions with Applications to Data Privacy. TCC 2013: 418-436 - 2012
- [c6]Prateek Jain, Abhradeep Thakurta:
Mirror Descent Based Database Privacy. APPROX-RANDOM 2012: 579-590 - [c5]Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, Dawn Song, David E. Culler:
GUPT: privacy preserving data analysis made easy. SIGMOD Conference 2012: 349-360 - [c4]Prateek Jain, Pravesh Kothari, Abhradeep Thakurta:
Differentially Private Online Learning. COLT 2012: 24.1-24.34 - [c3]Daniel Kifer, Adam D. Smith, Abhradeep Thakurta:
Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. COLT 2012: 25.1-25.40 - [i3]Kashyap Dixit, Madhav Jha, Abhradeep Thakurta:
Testing Lipschitz Property over Product Distribution and its Applications to Statistical Data Privacy. CoRR abs/1209.4056 (2012) - 2011
- [c2]Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman, Abhradeep Thakurta:
Noiseless Database Privacy. ASIACRYPT 2011: 215-232 - [i2]Prateek Jain, Pravesh Kothari, Abhradeep Thakurta:
Differentially Private Online Learning. CoRR abs/1109.0105 (2011) - [i1]Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman, Abhradeep Thakurta:
Noiseless Database Privacy. IACR Cryptol. ePrint Arch. 2011: 487 (2011) - 2010
- [c1]Raghav Bhaskar, Srivatsan Laxman, Adam D. Smith, Abhradeep Thakurta:
Discovering frequent patterns in sensitive data. KDD 2010: 503-512
Coauthor Index
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