Abhradeep Thakurta

According to our database, Abhradeep Thakurta authored at least 94 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Differentially Private Stream Processing at Scale.

Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Eidan Cohen, Himani Apte, Jodi Spacek

Proc. VLDB Endow., August, 2024

Near Exact Privacy Amplification for Matrix Mechanisms.

Christopher A. Choquette-Choo, Arun Ganesh, Saminul Haque, Thomas Steinke, Abhradeep Thakurta

CoRR, 2024

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD.

Thomas Steinke, Milad Nasr, Arun Ganesh, Borja Balle, Christopher A. Choquette-Choo, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam D. Smith, Andreas Terzis

CoRR, 2024

Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models.

Hongbin Liu, Lun Wang, Om Thakkar, Abhradeep Thakurta, Arun Narayanan

CoRR, 2024

Training Large ASR Encoders with Differential Privacy.

Geeticka Chauhan, Steve Chien, Om Thakkar, Abhradeep Thakurta, Arun Narayanan

CoRR, 2024

Optimal Rates for DP-SCO with a Single Epoch and Large Batches.

Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Thakurta

CoRR, 2024

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.

Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta

CoRR, 2024

Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements.

Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation.

Gavin Brown, Krishnamurthy Dj Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Guha Thakurta

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Privacy Amplification for Matrix Mechanisms.

Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning.

Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.

Krishnamurthy Dj Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta

Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components.

Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Private Learning with Public Features.

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Towards Large Scale Transfer Learning for Differentially Private Image Classification.

Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky

Trans. Mach. Learn. Res., 2023

Differentially Private Image Classification from Features.

Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky

Trans. Mach. Learn. Res., 2023

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.

Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta

J. Artif. Intell. Res., 2023

Improved Differentially Private and Lazy Online Convex Optimization.

Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta

CoRR, 2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu

CoRR, 2023

Challenges towards the Next Frontier in Privacy.

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

CoRR, 2023

Differentially Private Stream Processing at Scale.

Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek

CoRR, 2023

Training Private Models That Know What They Don't Know.

Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks.

Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Faster Differentially Private Convex Optimization via Second-Order Methods.

Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-Task Differential Privacy Under Distribution Skew.

Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang

Proceedings of the International Conference on Machine Learning, 2023

Why Is Public Pretraining Necessary for Private Model Training?

Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang

Proceedings of the International Conference on Machine Learning, 2023

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.

Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta

Proceedings of the International Conference on Machine Learning, 2023

Measuring Forgetting of Memorized Training Examples.

Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets.

Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Differentially Private and Lazy Online Convex Optimization.

Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Fully Adaptive Composition for Gaussian Differential Privacy.

Adam D. Smith, Abhradeep Thakurta

CoRR, 2022

Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition.

Soumyabrata Pal, Prateek Varshney, Prateek Jain, Abhradeep Guha Thakurta, Gagan Madan, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava

CoRR, 2022

Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search.

Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot, Abhradeep Thakurta

CoRR, 2022

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints.

Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta

CoRR, 2022

(Nearly) Optimal Private Linear Regression via Adaptive Clipping.

Prateek Varshney, Abhradeep Thakurta, Prateek Jain

CoRR, 2022

Large Scale Transfer Learning for Differentially Private Image Classification.

Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky

CoRR, 2022

Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization.

Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay

CoRR, 2022

Private Online Prefix Sums via Optimal Matrix Factorizations.

Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta

CoRR, 2022

Toward Training at ImageNet Scale with Differential Privacy.

Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, Abhradeep Thakurta

CoRR, 2022

When Does Differentially Private Learning Not Suffer in High Dimensions?

Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams.

Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Public Data-Assisted Mirror Descent for Private Model Training.

Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta

Proceedings of the International Conference on Machine Learning, 2022

(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping.

Prateek Varshney, Abhradeep Thakurta, Prateek Jain

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Private Matrix Approximation and Geometry of Unitary Orbits.

Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Node-Level Differentially Private Graph Neural Networks.

Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain

CoRR, 2021

Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning.

Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini

Proceedings of the 42nd IEEE Symposium on Security and Privacy, 2021

Is Private Learning Possible with Instance Encoding?

Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramèr

Proceedings of the 42nd IEEE Symposium on Security and Privacy, 2021

Differentially Private Model Personalization.

Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks.

Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Practical and Private (Deep) Learning Without Sampling or Shuffling.

Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

Proceedings of the 38th International Conference on Machine Learning, 2021

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates.

Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

Proceedings of the 38th International Conference on Machine Learning, 2021

(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces.

Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta

Proceedings of the Conference on Learning Theory, 2021

Evading the Curse of Dimensionality in Unconstrained Private GLMs.

Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Tempered Sigmoid Activations for Deep Learning with Differential Privacy.

Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Practical Locally Private Heavy Hitters.

Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta

J. Mach. Learn. Res., 2020

An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?

Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr

CoRR, 2020

Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning.

Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta

CoRR, 2020

Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems.

Shuang Song, Om Thakkar, Abhradeep Thakurta

CoRR, 2020

Obliviousness Makes Poisoning Adversaries Weaker.

Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta

CoRR, 2020

Guidelines for Implementing and Auditing Differentially Private Systems.

Daniel Kifer, Solomon Messing, Aaron Roth, Abhradeep Thakurta, Danfeng Zhang

CoRR, 2020

Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation.

Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta

CoRR, 2020

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space.

Adam D. Smith, Shuang Song, Abhradeep Thakurta

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Privacy Amplification via Random Check-Ins.

Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Privacy-preserving Data Mining in Industry.

Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Thakurta

Proceedings of the Companion of The 2019 World Wide Web Conference, 2019

Towards Practical Differentially Private Convex Optimization.

Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, Lun Wang

Proceedings of the 2019 IEEE Symposium on Security and Privacy, 2019

Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity.

Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta

Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

Private Stochastic Convex Optimization with Optimal Rates.

Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Erasure-Resilient Property Testing.

Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma

SIAM J. Comput., 2018

Model-Agnostic Private Learning via Stability.

Raef Bassily, Om Thakkar, Abhradeep Thakurta

CoRR, 2018

Model-Agnostic Private Learning.

Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differentially Private Matrix Completion Revisited.

Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta

Proceedings of the 35th International Conference on Machine Learning, 2018

Privacy Amplification by Iteration.

Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta

Proceedings of the 59th IEEE Annual Symposium on Foundations of Computer Science, 2018

2017

Is Interaction Necessary for Distributed Private Learning?

Adam D. Smith, Abhradeep Thakurta, Jalaj Upadhyay

Proceedings of the 2017 IEEE Symposium on Security and Privacy, 2017

2016

Beyond Worst Case Sensitivity in Private Data Analysis.

Abhradeep Thakurta

Encyclopedia of Algorithms, 2016

2015

To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout.

Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta, Oliver Williams

CoRR, 2015

(Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits.

Nikita Mishra, Abhradeep Thakurta

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Nearly Optimal Private LASSO.

Kunal Talwar, Abhradeep Thakurta, Li Zhang

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry.

Kunal Talwar, Abhradeep Thakurta, Li Zhang

CoRR, 2014

Private Empirical Risk Minimization, Revisited.

Raef Bassily, Adam D. Smith, Abhradeep Thakurta

CoRR, 2014

Analyze gauss: optimal bounds for privacy-preserving principal component analysis.

Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, Li Zhang

Proceedings of the Symposium on Theory of Computing, 2014

(Near) Dimension Independent Risk Bounds for Differentially Private Learning.

Prateek Jain, Abhradeep Guha Thakurta

Proceedings of the 31th International Conference on Machine Learning, 2014

Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds.

Raef Bassily, Adam D. Smith, Abhradeep Thakurta

Proceedings of the 55th IEEE Annual Symposium on Foundations of Computer Science, 2014

2013

Testing the Lipschitz Property over Product Distributions with Applications to Data Privacy.

Kashyap Dixit, Madhav Jha, Sofya Raskhodnikova, Abhradeep Thakurta

Proceedings of the Theory of Cryptography - 10th Theory of Cryptography Conference, 2013

(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings.

Abhradeep Guha Thakurta, Adam D. Smith

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Differentially Private Learning with Kernels.

Prateek Jain, Abhradeep Thakurta

Proceedings of the 30th International Conference on Machine Learning, 2013

Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso.

Abhradeep Thakurta, Adam D. Smith

Proceedings of the COLT 2013, 2013

2012

Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression.

Daniel Kifer, Adam D. Smith, Abhradeep Thakurta

Proceedings of the COLT 2012, 2012

Differentially Private Online Learning.

Prateek Jain, Pravesh Kothari, Abhradeep Thakurta

Proceedings of the COLT 2012, 2012

Testing Lipschitz Property over Product Distribution and its Applications to Statistical Data Privacy

Kashyap Dixit, Madhav Jha, Abhradeep Thakurta

CoRR, 2012

GUPT: privacy preserving data analysis made easy.

Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, Dawn Song, David E. Culler

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2012

Mirror Descent Based Database Privacy.

Prateek Jain, Abhradeep Thakurta

Proceedings of the Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 2012

2011

Noiseless Database Privacy.

Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman, Abhradeep Thakurta

IACR Cryptol. ePrint Arch., 2011

2010

Discovering frequent patterns in sensitive data.

Raghav Bhaskar, Srivatsan Laxman, Adam D. Smith, Abhradeep Thakurta

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010