I am interested in all theoretical
aspects of Machine Learning.




Publications

    A. Preprints

  1. On Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts
    Alexander Rakhlin, Karthik Sridharan
    [pdf]

  2. Online Nonparametric Regression with General Loss Functions
    Alexander Rakhlin, Karthik Sridharan
    [Arxiv]

  3. On Convex Optimization, Fat Shattering and Learning
    Nathan Srebro, Karthik Sridharan
    [pdf]

    4. B. Conferences

  4. From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
    Chris De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan
    Neural Information Processing Systems (NeurIPS), 2022

  5. On the Complexity of Adversarial Decision Making (full oral)
    Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
    Neural Information Processing Systems (NeurIPS), 2022

  6. Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
    Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
    International Conference on Machine Learning (ICML 2022) . Short version at RLDM 2022 - Reinforcement Learning and Decision Making conference.

  7. SGD: The role of Implicit Regularization, Batch-size and Multiple Epochs
    Satyen Kale, Ayush Sekhari, Karthik Sridharan
    Neural Information Processing Systems (NeurIPS), 2021

  8. Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
    Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
    Neural Information Processing Systems (NeurIPS), 2021

  9. Online learning with dynamics: A minimax perspective
    Kush Bhatia, Karthik Sridharan
    Neural Information Processing Systems (NeurIPS), 2020

  10. Reinforcement Learning with Feedback Graphs
    Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
    NeurIPS 2020. Short version at ICML 2020 Theoretical Foundations of RL workshop.

  11. Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations Yossi Arjevani, Yair Carmon, John C Duchi, Dylan J Foster, Ayush Sekhari, Karthik Sridharan
    COLT 2020.

  12. Hypothesis Set Stability and Generalization
    Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
    NeurIPS 2019
    [arxiv]

  13. The Complexity of Making the Gradient Small in Stochastic Convex Optimization (best student paper award)
    Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth
    Conference on Learning Theory (COLT 2019)
    [arxiv]

  14. Distributed Learning with Sublinear Communication (full oral presentation)
    Jayadev Acharya, Christopher De Sa, Dylan J. Foster, Karthik Sridharan
    International Conference on Machine Learning (ICML 2019)
    [arxiv]

  15. Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
    Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You
    International Conference on Machine Learning (ICML 2019)
    [arxiv]

  16. Two-Player Games for Efficient Non-Convex Constrained Optimization (best paper award)
    Andrew Cotter, Heinrich Jiang, Karthik Sridharan
    Algorithmic Learning Theory (ALT 2019)

  17. Uniform Convergence of Gradients for Non-Convex Learning and Optimization
    Dylan Foster, Ayush Sekhari, Karthik Sridharan
    NeurIPS 2018

  18. Online Learning: Sufficient Statistics and the Burkholder Method
    Dylan Foster, Alexander Rakhlin, Karthik Sridharan
    COLT 2018

  19. Logistic Regression: The Importance of Being Improper (best student paper award)
    Dylan Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
    COLT 2018

  20. Small-loss bounds for online learning with partial information
    Thodoris Lykouris, Karthik Sridharan, Eva Tardos
    COLT 2018

  21. Inference in Sparse Graphs with Pairwise Measurements and Side Information
    Dylan Foster, Daniel Reichman, Karthik Sridharan
    AISTATS 2018, [Arxiv]  
  22. Parameter-Free Online Learning via Model Selection
    Dylan Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
    NIPS 2017  
  23. ZigZag: A new approach to adaptive online learning
    Dylan Foster, Alexander Rakhlin, Karthik Sridharan
    Conference on Learning Theory (COLT) 2017 [ArXiv]  
  24. On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
    Alexander Rakhlin, Karthik Sridharan
    Conference on Learning Theory (COLT) 2017 [ArXiv]  
  25. Efficient Multiclass Prediction on Graphs via Surrogate Losses
    Alexander Rakhlin, Karthik Sridharan
    AISTATS 2017 [PDF]  
  26. Learning in Games: Robustness of Fast Convergence
    Dylan Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos
    NIPS 2016  
  27. Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters
    Zeyuan Allen-Zhu*, Yang Yuan*, Karthik Sridharan
    NIPS 2016 (* - main contributors)  
  28. BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
    Alexander Rakhlin, Karthik Sridharan
    ICML 2016, [ArXiv]  
  29. Differentially Private Causal Inference
    Matt Kusner, Yu Sun, Karthik Sridharan , Kilian Weinberger
    AISTATS 2016  
  30. Adaptive Online Learning
    Dylan Foster, Alexander Rakhlin, Karthik Sridharan
    NIPS 2015, [arxiv]  
  31. Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
    Alexander Rakhlin, Karthik Sridharan
    COLT 2015, [arxiv]  
  32. Learning with Square Loss: Localization through Offset Rademacher Complexity
    Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
    COLT 2015, [arxiv]  
  33. Online Optimization : Competing with Dynamic Comparators
    Ali Jadbabaie, Alexander Rakhlin, Shahin Shshrampour, Karthik Sridharan
    AISTATS 2015, [pdf]  
  34. Online Nonparametric Regression
    Alexander Rakhlin, Karthik Sridharan
    COLT 2014 [pdf]  
  35. On Semi-Probabilistic Universal Prediction
    Alexander Rakhlin, Karthik Sridharan
    Proceedings of IEEE Information Theory Workshop, 2013. Invited paper [pdf]  
  36. Optimization, Learning, and Games with Predictable Sequences
    Alexander Rakhlin, Karthik Sridharan
    NIPS 2013 [pdf]  
  37. Competing With Strategies
    Wei Han, Alexander Rakhlin, Karthik Sridharan
    COLT 2013 [pdf]  
  38. Online Learning with Predictable Sequences
    Alexander Rakhlin, Karthik Sridharan
    COLT 2013 [pdf] , [Arxiv version]  
  39. Localization and Adaptation in Online Learning (full oral presentation)
    Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
    AISTATS 2013

     
  40. Relax and Randomize: From Value to Algorithms (full oral presentation)
    Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
    NIPS 2012 [pdf]

     
  41. Making Stochastic Gradient Descent Optimal for Strongly Convex Problems
    Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
    ICML 2012 [Arxiv Version]  
  42. Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss
    Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan
    ICML 2012 [pdf]  
  43. On the Universality of Online Mirror Descent
    Nathan Srebro, Karthik Sridharan, Ambuj Tewari
    NIPS 2011 [Arxiv Version]  
  44. Better Mini-Batch Algorithms via Accelerated Gradient Methods
    Andrew Cotter, Ohad Shamir , Nathan Srebro, Karthik Sridharan
    NIPS 2011 [Arxiv Version]  
  45. Online Learning: Stochastic and Constrained Adversaries
    Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
    NIPS 2011 [pdf]   [Arxiv Version]  
  46. Online Learning: Beyond Regret (best paper award)
    Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
    COLT 2011, [pdf]   [Arxiv Version]  
  47. Complexity-Based Approach to Calibration with Checking Rules
    Dean Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
    COLT 2011, [pdf]    
  48. Online Learning: Random Averages, Combinatorial Parameters and Learnability (full oral presentation)
    Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
    NIPS 2010 [pdf]   [Arxiv Version]  
  49. Smoothness, Low-Noise and Fast Rates
    Nathan Srebro, Karthik Sridharan, Ambuj Tewari
    NIPS 2010 [pdf]   [Arxiv version]  
  50. Robust Selective Sampling from Single and Multiple Teachers
    Ofer Dekel, Claudio Gentile, Karthik Sridharan
    COLT 2010 [pdf]  
  51. Convex Games in Banach Spaces
    Karthik Sridharan, Ambuj Tewari
    COLT 2010 [pdf]  

  52. Learning Kernel-Based Halfspaces with the Zero-One Loss (best paper award)
    Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
    COLT 2010 [pdf], A shorter version presented at the best paper track IJCAI 2011 [pdf]

     

  53. Learning exponential families in high-dimensions: Strong convexity and sparsity
    Sham Kakade, Ohad Shamir, Karthik Sridharan, Ambuj Tewari
    AISTATS 2010 [Arxiv version]

     

  54. The Complexity of Improperly Learning Large Margin Halfspaces
    Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
    Open Problems, COLT 2009 [pdf]

     

  55. Learnability and Stability in the General Learning Setting
    Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
    COLT 2009 [pdf]

     

  56. Stochastic Convex Optimization
    Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
    COLT 2009 [pdf]

     

  57. Multi-View Clustering via Canonical Correlation Analysis
    Kamalika Chaudhuri, Sham Kakade, Karen Livescu, Karthik Sridharan
    ICML 2009 [pdf]

     

  58. On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds and Regularization
    Sham Kakade, Karthik Sridharan, Ambuj Tewari
    NIPS 2008 [pdf]

     

  59. Fast Rates for Regularized Objectives
    Shai Shalev-Shwartz, Nathan Srebro, Karthik Sridharan
    NIPS 2008 [pdf]

     

  60. Information Theoretic Framework for Multi-view Learning
    Karthik Sridharan, Sham M. Kakade
    21st Annual Conference on Learning Theory (COLT 2008) [pdf]

     

  61. Competitive Mixtures of Simple Neurons
    Karthik Sridharan, Matthew J Beal, Venu Govindaraju
    ICPR'06 [pdf]  

  62. Identifying handwritten text in mixed documents
    Faisal Farooq, Karthik Sridharan, Venu Govindaraju
    ICPR'06

     

  63. Classification of Machine Print and Handwritten Arabic Documents
    Karthik Sridharan, Faisal Farooq, Venu Govindaraju
    (SDIUT 2005, pp. 89-94.)

     

  64. A Sampling Based Approach to Facial Feature Extraction IEEE link
    Karthik Sridharan, Venu Govindaraju
    (IEEE AUTOID 2005. Best Paper Award - Second Prize, pp.51-56)

     

  65. A Probabilistic Approach to Semantic Face Retrieval springer link
    Karthik Sridharan, Sankalp Nayak, Sharat Chikkerur, Venu Govindaraju
    (AVBPA 2005, pp.977-986.)

     

  66. A Dynamic Migration Model for Self-adaptive Genetic Algorithms springer link
    K.G. Srinivasa, Karthik Sridharan, P. Deepa Shenoy, Venugopal K.R., L.M. Patnaik
    (Proceedings of 6th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 05),
    Springer Verlag, LNCS, July 6th – 9th 2005, Brisbane, Australia, pp. 555-562.)

     

  67. An Effective Content-Based Image Retrieval System Using STI Features and Relevance Feedback
    K.G. Srinivasa, Karthik Sridharan, P. Deepa Shenoy, Venugopal K.R., L.M. Patnaik
    (KBCS-2004, Fifth International Conference On Knowledge Based Computer Systems,
    Hyderabad, India, December 19-22, 2004, pp. 290 - 301.)

     

  68. EASOM: An Efficient Soft Computing Method for Predicting the Share Values ACTA press link
    K.G. Srinivasa, Karthik Sridharan, P. Deepa Shenoy, Venugopal K.R., L.M. Patnaik
    (Proceedings of IASTED International Conference on Artificial Intelligence and Applications (AIA 2004), ISSN: 1027-2666,
    Austria, Innsburg, Feb 16 - 18, 2004, pp. 264-269.)

    69. C. Journals

     

  69. Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
    Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Maya Gupta, Seungil You, Karthik Sridharan
    Journal of Machine Learning Research, (to appear) 2019  

  70. Empirical Entropy, Minimax Regret and Minimax Risk
    Alexander Rakhlin, Karthik Sridharan, Alexandre Tsybakov
    Bernoulli Journal, 2014 (to appear) [pdf]  

  71. Online Learning via Sequential Complexities
    Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
    Journal of Machine Learning Research, 2014

     

  72. Sequential Complexities and Uniform Martingale Laws of Large Numbers
    Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
    Probability Theory and Related Fields, 2014, to appear [pdf]  

  73. Selective Sampling and Active Learning from Single and Multiple Teachers
    Ofer Dekel, Claudio Gentile, Karthik Sridharan
    Journal of Machine Learning Research, 2012 [pdf]

     

  74. Learning Kernel Based Halfspaces with the 0-1 Loss
    Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
    SIAM Journal on Computing, 40(6):1623-1646, 2011 [pdf]

     

  75. Learnability, Stability and Uniform Convergence
    Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
    Journal of Machine Learning Research, 11(Oct):2635-2670, 2010 [pdf]

     

  76. A Neural Network based CBIR System using STI Features and Relevance Feedback
    K.G. Srinivasa, Karthik Sridharan, P. Deepa Shenoy, Venugopal K.R., L.M. Patnaik
    International Journal on Intelligent Data Analysis, Volume 10, Number 2, 2006, IOS Press.

D. Thesis

     
  1. Doctoral Thesis : Learning from an Optimization Viewpoint
    Karthik Sridharan Advisor : Nati Srebro
    Thesis Commitee : David McAllester, Arkadi Nemirovski, Alexander Razborov, Nathan Srebro
    Toyota Technological Institute at Chicago
    [pdf]
     
  2. Master's Thesis : Semantic Face Retrieval
    Karthik Sridharan
    Advisor : Venu Govindaraju
    Computer Science, SUNY Buffalo, 2006
    [pdf]

E. Notes

     

  1. A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks
    Alexander Rakhlin, Karthik Sridharan [Arxiv]

  2. Note on Refined Dudley Integral Covering Number Bound
    Nathan Srebro, Karthik Sridharan
    [pdf]

     

  3. A Gentle Introduction to Concentration Inequalities
    Karthik Sridharan
    (Theorems and proofs of a few concentration inequalities) - [pdf] [PS]

  4. Fast Convergence Rates for Excess Regularized Risk with Application to SVM
    Karthik Sridharan
    [pdf]

F. Books and Book Chapters

  1. Statistical Learning Theory and Sequential Prediction
    Alexander Rakhlin, Karthik Sridharan
    Lecture Notes (Early book draft) [pdf]

  2. On Martingale Extensions of Vapnik-Chervonenkis Theory with Applications to Online Learning
    Alexander Rakhlin, Karthik Sridharan
    Chapter 15, pp 197-215,: "Measures of Complexity", Festschrift for Alexey Chervonenkis. the book , Chapter [pdf]


G. Selected Current Projects

  1. Plug & Play Machine Learning

  2. Online Learning on Graphs and other interconnected instances

  3. Building de-polarizing recommender systems