Information

Academic Interests and Expertise

Education:

  • Ph.D. in Computer Science, Ohio State University, USA

Research Interests:

  • Machine Learning
  • Statistical Learning Theory
  • High Dimensional Statistics
  • Artificial Intelligence
  • Data Mining
Publications

Selected Papers:

  • Revisiting kd-tree for nearest neighbor search 
    Parikshit Ram and Kaushik Sinha
    KDD 2019
  • K-means clustering using random matrix sparsification 
    Kaushik Sinha
    ICML 2018
  • Improved nearest neighbor search using auxiliary information and priority functions 
    Omid Keivani and Kaushik Sinha
    ICML 2018
  • Improved Maximum Inner Product Search with Better Theoretical Guarantees using Randomized Partition Trees 
    Omid Keivani, Kaushik Sinha and Parikshit Ram
    Machine Learning. 107 (6), 1069-1094, 2018
  • Sparse Randomized Partition Trees for Nearest Neighbor Search 
    Kaushik Sinha and Omid Keivani
    AISTATS 2017
  • Improved Maximum Inner Product Search with Better Theoretical Guarantees
    Omid Keivani, Kaushik Sinha and Parikshit Ram
    IJCNN 2017
  • Randomized partition trees for nearest neighbor search
    Sanjoy Dasgupt and Kaushik Sinha
    Algorithmica 72 (1), 237-263, 2015
  • Polynomial learning of distribution families
    Mikhail Belki and Kaushik Sinha
    SIAM Journal on Computing 44 (4), 889-911, 2015
  • Randomized Partition Trees for Exact Nearest Neighbor Search 
    Sanjoy Dasgupta and Kaushik Sinha
    COLT 2013
  • Near-optimal Differentially Private Principal Components 
    Kamalika Chaudhuri, Anand D Sarwate and Kaushik Sinha
    NIPS 2012
  • Polynomial Learning of Distribution Families  
    Mikhail Belkin and Kaushik Sinha
    FOCS 2010
  • Toward Learning Gaussian Mixtures with Arbitrary Separation  
    Mikhail Belkin and Kaushik Sinha
    COLT 2010
  • Semi-supervised Learning Using Sparse Eigenfunction Bases 
    Kaushik Sinha and Mikhail Belkin
    NIPS 2009
  • The Value of Labeled and Unlabeled Examples when The Model is Imperfect 
    Kaushik Sinha and Mikhail Belkin
    NIPS 2007
Professional Experience
  • Associate Editor
    • Neurocomputing Journal (2015 to 2017)
  • Technical Program Committee Member
    • ACM Conference on Knowledge Discovery and Data Mining (KDD) 2015, 2016,2017, 2018, 2019
    • International Conference of Machine Learning (ICML) 2010, 2013, 2016
    • International Conference on Association for Advancement of Artificial Intelligence (AAAI) 2017, 2018
    • International Conference on Artificial Intelligence and Statistics (AISTATS) 2018
    • International Conference on Data Mining (ICDM) 2018
  • Reviewer
    • Annual Conference on Neural Information Processing Systems (NeurIPS) 2008 to 2019
    • International Conference on Machine Learning (ICML) 2010, 2012 to 2019
    • International Conference on Artificial Intelligence and Statistics (AISTATS) 2017
    • Annual Conference on learning Theory (COLT) 2008
    • Annual Symposium on Theory of Computing (STOC) 2010
    • IEEE Transactions of Pattern Analysis and Machine Intelligence (PAMI)
    • IEEE Transactions on Neural Networks (TNN)
    • IEEE Transactions on Information Theory
    • Transactions on Knowledge Discovery and Data (TKDD)
    • Journal of Machine Learning Research (JMLR)
    • Pattern Recognition
    • Neurocomputing
    • Bernoulli Journal
    • Journal of Statistical Computation and Simulation
    • Machine Learning Journal
Additional Information

Research Position Available

I have an opening for an MS Thesis/Project student in my research group starting Fall 2021. Ideal candidate should have already taken Machine Learning and/or Deep Learning class. Funding for this position is available through Graduate Teaching Assistantship (GTA). Interested candidates should email me their resume/cv.