Kaushik Sinha

Education:

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

Research Interests:

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

Selected Papers:

• Revisiting kd-tree for nearest neighbor search [pdf]
  Parikshit Ram and Kaushik Sinha
  KDD 2019
• K-means clustering using random matrix sparsification [pdf]
  Kaushik Sinha
  ICML 2018
• Improved nearest neighbor search using auxiliary information and priority functions [pdf]
  Omid Keivani and Kaushik Sinha
  ICML 2018
• Improved Maximum Inner Product Search with Better Theoretical Guarantees using Randomized Partition Trees [journal_version]
  Omid Keivani, Kaushik Sinha and Parikshit Ram
  Machine Learning. 107 (6), 1069-1094, 2018
• Sparse Randomized Partition Trees for Nearest Neighbor Search [pdf]
  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 [pdf]
  Sanjoy Dasgupta and Kaushik Sinha
  COLT 2013
• Near-optimal Differentially Private Principal Components [pdf]
  Kamalika Chaudhuri, Anand D Sarwate and Kaushik Sinha
  NIPS 2012
• Polynomial Learning of Distribution Families [pdf] 
  Mikhail Belkin and Kaushik Sinha
  FOCS 2010
• Toward Learning Gaussian Mixtures with Arbitrary Separation [pdf] [Full Version]
  Mikhail Belkin and Kaushik Sinha
  COLT 2010
• Semi-supervised Learning Using Sparse Eigenfunction Bases [pdf]
  Kaushik Sinha and Mikhail Belkin
  NIPS 2009
• The Value of Labeled and Unlabeled Examples when The Model is Imperfect [pdf]
  Kaushik Sinha and Mikhail Belkin
  NIPS 2007

• 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

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 calndidates should email me their resume/cv.