Machine Learning algorithms and optimization techniques have become central to most applications of computing ranging from search, ads, data-mining, data-analytics in large databases, information retrieval and extraction, natural language processing including machine translation, speech, vision, gaming, user adaptation of computing systems, as well as security, privacy, and the broad topic of crowd-sourcing. Our goal is to conduct research in theoretical and practical aspects of Machine Learning and Optimization including:
- Novel machine learning algorithms and paradigms
- Foundational aspects of optimization techniques, including new algorithms and applications to machine learning
- Theoretical analysis of machine learning and optimization algorithms
- Performance analysis and enhancement of machine learning and optimization algorithms
- Applications in search and IR, vision, NLP and other areas
- Data mining and data analytics for very large data sets
Internship and Research Assistantship Opportunities
Internships: We are looking for interns to work on cutting edge research problems leading to publications in top tier conferences and journals. Students should send an email to firstname.lastname@example.org in the format specified on the internship page. Click here to go to our internship page. Note that Indian undergraduate and masters students should apply through their respective campus placement programs rather than e-mailing us directly (we typically do not take Indian non-PhD students unless their institute has a campus placement tie-up with MSR and the students have applied through the program).
Research Assistantships: We are also looking for exceptional candidates who are fresh graduates (bachelors or masters in CS) to work with us for a period of 1+1 years on substantial research problems. The RA program is designed to give a flavor of research to those interested in pursuing a research career. RAs are exposed to cutting edge research and are expected to explore a problem in depth with their mentor and obtain multiple, high quality publications during this period. Previous Research Assistants have gone on to do a PhD from top CS schools or become successful entrepreneurs. Interested candidates with a strong track record in their undergraduate or master’s degree should send an e-mail to Manik Varma at email@example.com along with their CVs and should mention their preferred starting date and the researchers they might be interested in working with.
- Shipra Agrawal and Nikhil R. Devanur, Fast algorithms for online stochastic convex programming, in SODA 2015 (ACM-SIAM Symposium on Discrete Algorithms), SIAM – Society for Industrial and Applied Mathematics, January 2015.
- Purushottam Kar, Harikrishna Narasimhan, and Prateek Jain, Online and Stochastic Gradient Methods for Non-decomposable Loss Functions, in Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS), Neural Information Processing Systems, December 2014.
- Prateek Jain, Ambuj Tewari, and Purushottam Kar, On Iterative Hard Thresholding Methods for High-dimensional M-Estimation, in Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS), Neural Information Processing Systems, December 2014.
- Dhruv Mahajan, S. Sathiya Keerthi, and Sundararajan Sellamanickam, A Distributed Algorithm for Training Nonlinear Kernel Machines, CoRR, September 2014.
- Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, and Rashish Tandon, Learning Sparsely Used Overcomplete Dictionaries, in Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014, July 2014.
- Srinadh Bhojanapalli and Prateek Jain, Universal Matrix Completion, in Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, July 2014.
- Prateek Jain and Sewoong Oh, Learning Mixtures of Discrete Product Distributions using Spectral Decompositions, in Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014, July 2014.
- Shipra Agrawal and Nikhil R. Devanur, Bandits with concave rewards and convex knapsacks, in EC 2014, ACM conference on Economics and Computation, June 2014.
- Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, and Inderjit S. Dhillon, Large-scale Multi-label Learning with Missing Labels, in Proceedings of the 31st International Conference on Machine Learning (ICML), Journal of Machine Learning Research, June 2014.
- Prateek Jain and Abhradeep Guha Thakurta, (Near) Dimension Independent Risk Bounds for Differentially Private Learning, in Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, June 2014.
- Dhruv Mahajan, S. Sathiya Keerthi, and Sundararajan Sellamanickam, A distributed block coordinate descent method for training l1 regularized linear classifiers, CoRR, 2014.
- Sudheendra Vijayanarasimhan, Prateek Jain, and Kristen Grauman, Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning, in IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) , vol. 36, no. 2, pp. 276–288, January 2014.
- Praneeth Netrapalli, Prateek Jain, and Sujay Sanghavi, Phase Retrieval using Alternating Minimization, in 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, Lake Tahoe, Nevada, United States., December 2013.
- Ioannis Mitliagkas, Constantine Caramanis, and Prateek Jain, Memory Limited, Streaming PCA, in 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, Lake Tahoe, Nevada, United States., December 2013.
- K. S. M. Tozammel Hossain, Debprakash Patnaik, Srivatsan Laxman, Prateek Jain, Chris Bailey-Kellogg, and Naren Ramakrishnan, Improved Multiple Sequence Alignments Using Coupled Pattern Mining, in IEEE/ACM Trans. Comput. Biology Bioinform., vol. 10, no. 5, pp. 1098–1112, November 2013.
- Prateek Jain and Abhradeep Thakurta, Differentially Private Learning with Kernels, in Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, July 2013.
- Arun Chaganty, Akash Lal, Aditya V. Nori, and Sriram K. Rajamani, Combining Relational Learning with SMT Solvers using CEGAR, in Computer Aided Verification (CAV), Lecture Notes in Computer Science, July 2013.
- Rohan Ramanath, Monojit Choudhury, Kalika Bali, and Rishiaj Saha Roy, Crowd Prefers the Middle Path: A New IAA Metric for Crowdsourcing Reveals Turker Biases in Query Segmentation, in Proceedings of ACL, Association for Computational Linguistics, July 2013.
- Shipra Agrawal and Navin Goyal, Thompson Sampling for contextual bandits with linear payoffs , in 30th International Conference on Machine Learning (ICML) , June 2013.
- Sivakant Gopi, Praneeth Netrapalli, Prateek Jain, and Aditya V. Nori, One-bit Compressed Sensing: Provable Support and Vector Recovery, in International Conference on Machine Learning (ICML), Journal of Machine Learning Research, June 2013.
- Rahul Sharma, Saurabh Gupta, Bharath Hariharan, Alex Aiken, and Aditya V. Nori, Verification as Learning Geometric Concepts, in Static Analysis Symposium (SAS), Springer Verlag, June 2013.
- Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi, Low-rank matrix completion using alternating minimization, in Symposium on Theory of Computing Conference, STOC'13, Palo Alto, CA, USA, June 1-4, 2013, June 2013.
- C. Jose, P. Goyal, P. Aggrwal, and M. Varma, Local Deep Kernel Learning for Efficient Non-linear SVM Prediction, in Proceedings of the International Conference on Machine Learning, June 2013.
- P.K. Srijith, Shirish Shevade, and S. Sundararajan, Semi-supervised Gaussian Process Ordinal Regression, European Conference on Machine Learning (ECML), June 2013.
- R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma, Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages, in Proceedings of the International World Wide Web Conference, May 2013.