Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta and Nithin Varma. Erasure-Resilient Property Testing. [Submitted] --- This work was done when I was an employee at Yahoo Labs and Microsoft Research Silicon Valley Campus.
Kunal Talwar, Abhradeep Thakurta and Li Zhang. Nearly optimal private LASSO. [Accepted to NIPS 2015] --- This work was done when I was an employee at at Yahoo Labs.
Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta and Oliver Williams. To Drop or Not to Drop: Generalizability, Stability and Privacy of Dropout. --- This work was done when I was an employee at Stanford University and then at Yahoo Labs.
Ruggerio Cavallo, Abhradeep Thakurta, and Chris Wilkins. Permutation Invariant Learning and Dynamic Mechanism Design. [In AdAuctions Workshop, EC'15]. --- This work was done when I was an employee at Yahoo Labs.
Nikita Mishra and Abhradeep Thakurta. (Nearly) Optimal Differentially Private Stochastic Multi-arm Bandits: From Theory to Practice. [In UAI 2015 and in ICML, 2014 workshop on learning, security and privacy] .
Abhradeep Thakurta. Beyond Worst Case Sensitivity in Private Data Analysis. [Survey article. Encylopedia of Algorithms, 2015].
Raef Bassily, Adam Smith and Abhradeep Thakurta. Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. In FOCS 2014, and in ICML, 2014 workshop on learning, security and privacy (presentation only).
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta and Li Zhang. Analyze Gauss: Optimal Bounds for Privacy-Preserving Principal Component Analysis. STOC-2014, pp. 11-20.
Prateek Jain and Abhradeep Thakurta. (Near) Dimension Independent Risk Bounds for Differentially Private Learning. ICML 2014.
Adam Smith and Abhradeep Thakurta. (Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. NIPS-2013.
Adam Smith and Abhradeep Thakurta. Differentially Private Feature Selection via Stability Arguments, and the Robustness of LASSO. COLT-2013, pp. 30:819-850.
Prateek Jain and Abhradeep Thakurta. Differentially Private Learning with Kernels. ICML-2013, pp. 28(3):118-126.
Kashyap Dixit, Madhav Jha, Sofya Raskhodnikova and Abhradeep Thakurta. Testing Lipschitz Property over Product Distributions with Applications to Statistical Data Privacy. TCC-2013, pp. 418-436.
Prateek Jain and Abhradeep Thakurta. Mirror Descent based Interactive Database Privacy. APPROX/ RANDOM-2012, pp. 579-590.
Daniel Kifer, Adam Smith and Abhradeep Thakurta. Private Convex Empirical Risk Minimization and High-dimensional Regression. COLT-2012, pp. 25.1–25.40.
Prateek Jain, Pravesh Kothari and Abhradeep Thakurta. Differentially Private Online Learning. COLT-2012, pp. 24.1–24.34.
Prashanth Mohan, Abhradeep Thakurta, Elaine Shi and Dawn Song. GUPT: Privacy Preserving Data Analysis Made Easy. SIGMOD 2012, pp. 349-360.
Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman and Abhradeep Thakurta. Noiseless Database Privacy. Asiacrypt, 2011, pp 215-232.
Raghav Bhaskar, Srivatsan Laxman, Adam Smith and Abhradeep Thakurta. Discovering frequent patterns in sensitive data. SIGKDD, 2010, pp. 503-512.
Abhradeep Guha Thakurta, Ashwath Kumar Reddy, and Vijayan Immanuel. GRASP Approach to The Glass Cutting Problem. 5th Mexican International Conference on Artificial Intelligence(MICAI-2006), Research in Computing Science, ISSN 1870-4069.