Machine Learning and Optimization
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
People
Shipra Agrawal
P. Anandan
Publications
- Avinash Achar, Srivatsan Laxman, Raajay Viswanathan, and P. S. Sastry, Discovering Injective Episodes with General Partial Orders, in Data Mining and Knowledge Discovery (DAMI), vol. 25, no. 1, pp. 67--108, Springer, 2012
- Avinash Achar, Srivatsan Laxman, and P.S. Sastry, A Unified View of the Apriori-based algorithms for Frequent Episode Discovery, in Knowledge and Information Systems (KAIS), vol. 31, no. 2, pp. 223--250, Springer Verlag, 2012
- Rahul Sharma, Aditya V. Nori, and Alex Aiken, Interpolants as Classifiers, no. MSR-TR-2012-13, January 2012
- Raajay Viswanathan, Prateek Jain, Srivatsan Laxman, and Arvind Arasu, A Learning Framework for Self-Tuning Histograms, no. MSR-TR-2011-140, December 2011
- Sandeep Karanth, Srivatsan Laxman, Prasad Naldurg, Ramarathnam Venkatesan, J. Lambert, and Jinwook Shin, ZDVUE: Prioritization of JavaScript Attacks To Discover New Vulnerabilities, in Proceedings of the Fourth ACM Workshop on Artificial Intelligence and Security (AISEC 2011), ACM, October 2011
- Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta, Differentially Private Online Learning, no. MSR-TR-2011-141, September 2011
- Dipak L. Chaudhari, Om P. Damani, and Srivatsan Laxman, Lexical Co-occurrence, Statistical Significance, and Word Association, in Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), Edinburgh, UK, ACL/SIGPARSE, July 2011
- Prateek Jain, Brian Kulis, Jason V. Davis, and Inderjit S. Dhillon, Metric and Kernel Learning using a Linear Transformation, in Journal of Machine Learning (JMLR), 2011
- Purushottam Kar and Prateek Jain, Similarity-based Learning via Data driven Embeddings, in 25th Annual Conference on Neural Information Processing Systems (NIPS), 2011
- Nikita Mishra, Rishiraj Saha Roy, Niloy Ganguly, Srivatsan Laxman, and Monojit Choudhury, Unsupervised query segmentation using only query logs, in Proceedings of the Twentieth International World Wide Web Conference (WWW 2011), Companion Volume, Hyderabad, Mar 28-Apr 1, ACM, 2011
