Abhradeep Guha Thakurta

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 28

Top Topics

Differentially Private

Convex Optimization

Top Venues

Publications

Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
Privacy Amplification for Matrix Mechanisms. ICLR (2024)
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)
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)
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)
Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky
Differentially Private Image Classification from Features. Trans. Mach. Learn. Res. 2023 (2023)
Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta
Faster Differentially Private Convex Optimization via Second-Order Methods. NeurIPS (2023)
Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. ICML (2023)
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)
Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta
Improved Differentially Private and Lazy Online Convex Optimization. CoRR (2023)
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 (2023)
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 (2023)
Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang
Multi-Task Differential Privacy Under Distribution Skew. ICML (2023)
Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS (2023)
Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
Training Private Models That Know What They Don't Know. NeurIPS (2023)
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)
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)
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 (2022)
Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta
Private Online Prefix Sums via Optimal Matrix Factorizations. CoRR (2022)
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)
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 (2022)
Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Guha Thakurta, Nisheeth K. Vishnoi
Private Matrix Approximation and Geometry of Unitary Orbits. CoRR (2022)
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)
Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta
Differentially Private Model Personalization. NeurIPS (2021)
Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain
Node-Level Differentially Private Graph Neural Networks. CoRR (2021)
Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta
Private Stochastic Convex Optimization with Optimal Rates. NeurIPS (2019)
Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Guha Thakurta
Privacy-preserving Data Mining in Industry. WSDM (2019)
Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar
Model-Agnostic Private Learning. NeurIPS (2018)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta
Practical Locally Private Heavy Hitters. NIPS (2017)
Prateek Jain, Abhradeep Guha Thakurta
(Near) Dimension Independent Risk Bounds for Differentially Private Learning. ICML (2014)
Abhradeep Guha Thakurta, Adam D. Smith
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. NIPS (2013)