Prateek Jain, Om Thakkar and Abhradeep Thakurta. Private Matrix Completion with Worst-case Guarantees. [Under preparation.]

Adam Smith, Abhradeep Thakurta and Jalaj Upadhay. Is Interaction Necessary for Distributed Private Learning? [Accepted. Oakland 2017.] 

Ruggerio Cavallo, Abhradeep Thakurta, and Chris Wilkins. Truthful Dynamic Mechanisms for Multi-Armed Bandits. [In AdAuctions Workshop, EC 2015].

Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta and Nithin Varma. Erasure-Resilient Property Testing. ICALP 2016.   

Kunal Talwar, Abhradeep Thakurta and  Li ZhangNearly optimal private LASSO. NIPS 2015.    

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 ZhangAnalyze 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 ThakurtaDifferentially 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 ThakurtaTesting 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 Reddyand 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.

Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta and Oliver Williams. To Drop or Not to Drop: Generalizability, Stability and Privacy of Dropout. [Manuscript.]