Abhradeep Thakurta
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
31
Venues
15
Active years
2010–2024
Best venue rank
A*
Where they publish
Papers
31 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2024 | AISTATS | Private Learning with Public Features. | Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang |
| 2024 | AISTATS | 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 |
| 2024 | FOCS | Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. | Krishnamurthy Dj Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta |
| 2023 | COLT | Differentially Private and Lazy Online Convex Optimization. | Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta |
| 2023 | COLT | Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets. | Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay |
| 2022 | COLT | Private Matrix Approximation and Geometry of Unitary Orbits. | Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi |
| 2022 | COLT | (Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. | Prateek Varshney, Abhradeep Thakurta, Prateek Jain |
| 2022 | ICML | 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 |
| 2021 | AAAI | Tempered Sigmoid Activations for Deep Learning with Differential Privacy. | Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, lfar Erlingsson |
| 2021 | AISTATS | Evading the Curse of Dimensionality in Unconstrained Private GLMs. | Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta |
| 2021 | COLT | (Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. | Peter Kairouz, Mnica Ribero Diaz, Keith Rush, Abhradeep Thakurta |
| 2021 | ICML | 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 |
| 2021 | ICML | Practical and Private (Deep) Learning Without Sampling or Shuffling. | Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu |
| 2021 | SP | Is Private Learning Possible with Instance Encoding? | Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramr |
| 2021 | SP | Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. | Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini |
| 2019 | WWW | Privacy-preserving Data Mining in Industry. | Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Thakurta |
| 2019 | SODA | Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. | lfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta |
| 2019 | SP | Towards Practical Differentially Private Convex Optimization. | Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, Lun Wang |
| 2018 | FOCS | Privacy Amplification by Iteration. | Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta |
| 2018 | ICML | Differentially Private Matrix Completion Revisited. | Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta |
| 2017 | SP | Is Interaction Necessary for Distributed Private Learning? | Adam D. Smith, Abhradeep Thakurta, Jalaj Upadhyay |
| 2016 | ICALP | Erasure-Resilient Property Testing. | Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma |
| 2015 | UAI | (Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits. | Nikita Mishra, Abhradeep Thakurta |
| 2014 | FOCS | Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. | Raef Bassily, Adam D. Smith, Abhradeep Thakurta |
| 2014 | STOC | Analyze gauss: optimal bounds for privacy-preserving principal component analysis. | Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, Li Zhang |
| 2013 | COLT | Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. | Abhradeep Thakurta, Adam D. Smith |
| 2013 | ICML | Differentially Private Learning with Kernels. | Prateek Jain, Abhradeep Thakurta |
| 2013 | TCC | Testing the Lipschitz Property over Product Distributions with Applications to Data Privacy. | Kashyap Dixit, Madhav Jha, Sofya Raskhodnikova, Abhradeep Thakurta |
| 2012 | SIGMOD | GUPT: privacy preserving data analysis made easy. | Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, Dawn Song, David E. Culler |
| 2011 | ASIACRYPT | Noiseless Database Privacy. | Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman, Abhradeep Thakurta |
| 2010 | KDD | Discovering frequent patterns in sensitive data. | Raghav Bhaskar, Srivatsan Laxman, Adam D. Smith, Abhradeep Thakurta |