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ORCIDKarthik Sridharan
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- affiliation: University at Buffalo, USA
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2020 – today
- 2024
[i52]Karthik Sridharan, Seung Won Wilson Yoo:
Online Learning with Unknown Constraints. CoRR abs/2403.04033 (2024)- [i51]August Y. Chen, Ayush Sekhari, Karthik Sridharan:
Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials. CoRR abs/2407.04264 (2024) - [i50]August Y. Chen, Karthik Sridharan:
From Optimization to Sampling via Lyapunov Potentials. CoRR abs/2410.02979 (2024) - 2023
- [c64]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Contextual Bandits and Imitation Learning with Preference-Based Active Queries. NeurIPS 2023 - [c63]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Selective Sampling and Imitation Learning via Online Regression. NeurIPS 2023 - [i49]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Selective Sampling and Imitation Learning via Online Regression. CoRR abs/2307.04998 (2023) - [i48]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Contextual Bandits and Imitation Learning via Preference-Based Active Queries. CoRR abs/2307.12926 (2023) - 2022
- [j7]Thodoris Lykouris
, Karthik Sridharan, Éva Tardos:
Small-Loss Bounds for Online Learning with Partial Information. Math. Oper. Res. 47(3): 2186-2218 (2022) - [c62]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. ICML 2022: 4666-4689 - [c61]Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. NeurIPS 2022 - [c60]Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS 2022 - [i47]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR abs/2206.09421 (2022) - [i46]Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. CoRR abs/2206.13063 (2022) - [i45]Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR abs/2210.06705 (2022) - 2021
- [c59]Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. NeurIPS 2021: 19033-19045 - [c58]Ayush Sekhari, Karthik Sridharan, Satyen Kale:
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. NeurIPS 2021: 27422-27433 - [i44]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. CoRR abs/2106.11519 (2021) - [i43]Satyen Kale, Ayush Sekhari, Karthik Sridharan:
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. CoRR abs/2107.05074 (2021) - 2020
- [c57]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT 2020: 242-299 - [c56]Kush Bhatia, Karthik Sridharan:
Online learning with dynamics: A minimax perspective. NeurIPS 2020 - [c55]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. NeurIPS 2020 - [i42]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. CoRR abs/2005.03789 (2020) - [i41]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR abs/2006.13476 (2020) - [i40]Kush Bhatia, Karthik Sridharan:
Online learning with dynamics: A minimax perspective. CoRR abs/2012.01705 (2020)
2010 – 2019
- 2019
- [j6]Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Lutong Wang, Taman Narayan, Seungil You, Karthik Sridharan:
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. J. Mach. Learn. Res. 20: 172:1-172:59 (2019) - [c54]Andrew Cotter, Heinrich Jiang, Karthik Sridharan:
Two-Player Games for Efficient Non-Convex Constrained Optimization. ALT 2019: 300-332 - [c53]Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth:
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT 2019: 1319-1345 - [c52]Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan:
Distributed Learning with Sublinear Communication. ICML 2019: 40-50 - [c51]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML 2019: 1397-1405 - [c50]Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. NeurIPS 2019: 6726-6736 - [i39]Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth:
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR abs/1902.04686 (2019) - [i38]Jayadev Acharya, Christopher De Sa, Dylan J. Foster, Karthik Sridharan:
Distributed Learning with Sublinear Communication. CoRR abs/1902.11259 (2019) - [i37]Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. CoRR abs/1904.04755 (2019) - 2018
- [c49]Dylan J. Foster, Karthik Sridharan, Daniel Reichman:
Inference in Sparse Graphs with Pairwise Measurements and Side Information. AISTATS 2018: 1810-1818 - [c48]Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Logistic Regression: The Importance of Being Improper. COLT 2018: 167-208 - [c47]Thodoris Lykouris, Karthik Sridharan, Éva Tardos:
Small-loss bounds for online learning with partial information. COLT 2018: 979-986 - [c46]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Online Learning: Sufficient Statistics and the Burkholder Method. COLT 2018: 3028-3064 - [c45]Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. NeurIPS 2018: 8759-8770 - [e1]Firdaus Janoos, Mehryar Mohri, Karthik Sridharan:
Algorithmic Learning Theory, ALT 2018, 7-9 April 2018, Lanzarote, Canary Islands, Spain. Proceedings of Machine Learning Research 83, PMLR 2018 [contents] - [i36]Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan:
Parameter-free online learning via model selection. CoRR abs/1801.00101 (2018) - [i35]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Online Learning: Sufficient Statistics and the Burkholder Method. CoRR abs/1803.07617 (2018) - [i34]Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Logistic Regression: The Importance of Being Improper. CoRR abs/1803.09349 (2018) - [i33]Andrew Cotter, Heinrich Jiang, Karthik Sridharan:
Two-Player Games for Efficient Non-Convex Constrained Optimization. CoRR abs/1804.06500 (2018) - [i32]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR abs/1807.00028 (2018) - [i31]Andrew Cotter, Heinrich Jiang, Serena Lutong Wang, Taman Narayan, Maya R. Gupta, Seungil You, Karthik Sridharan:
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. CoRR abs/1809.04198 (2018) - [i30]Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. CoRR abs/1810.11059 (2018) - 2017
- [c44]Alexander Rakhlin, Karthik Sridharan:
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. AISTATS 2017: 1403-1411 - [c43]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
ZigZag: A New Approach to Adaptive Online Learning. COLT 2017: 876-924 - [c42]Alexander Rakhlin, Karthik Sridharan:
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities. COLT 2017: 1704-1722 - [c41]Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan:
Parameter-Free Online Learning via Model Selection. NIPS 2017: 6020-6030 - [i29]Dylan J. Foster, Daniel Reichman, Karthik Sridharan:
Inference in Sparse Graphs with Pairwise Measurements and Side Information. CoRR abs/1703.02728 (2017) - [i28]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
ZigZag: A new approach to adaptive online learning. CoRR abs/1704.04010 (2017) - [i27]Thodoris Lykouris, Karthik Sridharan, Éva Tardos:
Small-loss bounds for online learning with partial information. CoRR abs/1711.03639 (2017) - 2016
- [c40]Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger:
Private Causal Inference. AISTATS 2016: 1308-1317 - [c39]Alexander Rakhlin, Karthik Sridharan:
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. ICML 2016: 1977-1985 - [c38]Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan:
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. NIPS 2016: 1642-1650 - [c37]Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos:
Learning in Games: Robustness of Fast Convergence. NIPS 2016: 4727-4735 - [i26]Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan:
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. CoRR abs/1602.02151 (2016) - [i25]Alexander Rakhlin, Karthik Sridharan:
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. CoRR abs/1602.02196 (2016) - [i24]Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos:
Fast Convergence of Common Learning Algorithms in Games. CoRR abs/1606.06244 (2016) - [i23]Alexander Rakhlin, Karthik Sridharan:
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks. CoRR abs/1608.09014 (2016) - 2015
- [j5]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online learning via sequential complexities. J. Mach. Learn. Res. 16: 155-186 (2015) - [c36]Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan:
Online Optimization : Competing with Dynamic Comparators. AISTATS 2015 - [c35]Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan:
Learning with Square Loss: Localization through Offset Rademacher Complexity. COLT 2015: 1260-1285 - [c34]Alexander Rakhlin, Karthik Sridharan:
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. COLT 2015: 1457-1479 - [c33]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Adaptive Online Learning. NIPS 2015: 3375-3383 - [i22]Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan:
Online Optimization : Competing with Dynamic Comparators. CoRR abs/1501.06225 (2015) - [i21]Alexander Rakhlin, Karthik Sridharan:
Online Nonparametric Regression with General Loss Functions. CoRR abs/1501.06598 (2015) - [i20]Alexander Rakhlin, Karthik Sridharan:
Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts. CoRR abs/1501.07340 (2015) - [i19]Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan:
Learning with Square Loss: Localization through Offset Rademacher Complexity. CoRR abs/1502.06134 (2015) - [i18]Alexander Rakhlin, Karthik Sridharan:
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. CoRR abs/1503.01212 (2015) - [i17]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Adaptive Online Learning. CoRR abs/1508.05170 (2015) - [i16]Alexander Rakhlin, Karthik Sridharan:
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities. CoRR abs/1510.03925 (2015) - 2014
- [c32]Alexander Rakhlin, Karthik Sridharan:
Online Non-Parametric Regression. COLT 2014: 1232-1264 - [i15]Alexander Rakhlin, Karthik Sridharan:
Online Nonparametric Regression. CoRR abs/1402.2594 (2014) - 2013
- [c31]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Localization and Adaptation in Online Learning. AISTATS 2013: 516-526 - [c30]Wei Han, Alexander Rakhlin, Karthik Sridharan:
Competing With Strategies. COLT 2013: 966-992 - [c29]Alexander Rakhlin, Karthik Sridharan:
Online Learning with Predictable Sequences. COLT 2013: 993-1019 - [c28]Alexander Rakhlin, Karthik Sridharan:
On Semi-Probabilistic universal prediction. ITW 2013: 1-5 - [c27]Alexander Rakhlin, Karthik Sridharan:
Optimization, Learning, and Games with Predictable Sequences. NIPS 2013: 3066-3074 - [i14]Wei Han, Alexander Rakhlin, Karthik Sridharan:
Competing With Strategies. CoRR abs/1302.2672 (2013) - [i13]Alexander Rakhlin, Karthik Sridharan, Alexandre B. Tsybakov:
Empirical Entropy, Minimax Regret and Minimax Risk. CoRR abs/1308.1147 (2013) - [i12]Alexander Rakhlin, Karthik Sridharan:
Optimization, Learning, and Games with Predictable Sequences. CoRR abs/1311.1869 (2013) - 2012
- [j4]Ofer Dekel, Claudio Gentile, Karthik Sridharan:
Selective sampling and active learning from single and multiple teachers. J. Mach. Learn. Res. 13: 2655-2697 (2012) - [c26]Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan:
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. ICML 2012 - [c25]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. ICML 2012 - [c24]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Relax and Randomize : From Value to Algorithms. NIPS 2012: 2150-2158 - [i11]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Relax and Localize: From Value to Algorithms. CoRR abs/1204.0870 (2012) - [i10]Karthik Sridharan:
Learning From An Optimization Viewpoint. CoRR abs/1204.4145 (2012) - [i9]Alexander Rakhlin, Karthik Sridharan:
Online Learning with Predictable Sequences. CoRR abs/1208.3728 (2012) - 2011
- [j3]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Kernel-Based Halfspaces with the 0-1 Loss. SIAM J. Comput. 40(6): 1623-1646 (2011) - [c23]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Linear and Kernel Predictors with the 0-1 Loss Function. IJCAI 2011: 2740-2745 - [c22]Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan:
Better Mini-Batch Algorithms via Accelerated Gradient Methods. NIPS 2011: 1647-1655 - [c21]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Stochastic, Constrained, and Smoothed Adversaries. NIPS 2011: 1764-1772 - [c20]Nati Srebro, Karthik Sridharan, Ambuj Tewari:
On the Universality of Online Mirror Descent. NIPS 2011: 2645-2653 - [c19]Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Complexity-Based Approach to Calibration with Checking Rules. COLT 2011: 293-314 - [c18]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Beyond Regret. COLT 2011: 559-594 - [i8]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Stochastic and Constrained Adversaries. CoRR abs/1104.5070 (2011) - [i7]Andrew Cotter, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Better Mini-Batch Algorithms via Accelerated Gradient Methods. CoRR abs/1106.4574 (2011) - [i6]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
On the Universality of Online Mirror Descent. CoRR abs/1107.4080 (2011) - 2010
- [j2]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability, Stability and Uniform Convergence. J. Mach. Learn. Res. 11: 2635-2670 (2010) - [c17]Karthik Sridharan, Ambuj Tewari:
Convex Games in Banach Spaces. COLT 2010: 1-13 - [c16]Ofer Dekel, Claudio Gentile, Karthik Sridharan:
Robust Selective Sampling from Single and Multiple Teachers. COLT 2010: 346-358 - [c15]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Kernel-Based Halfspaces with the Zero-One Loss. COLT 2010: 441-450 - [c14]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. NIPS 2010: 1984-1992 - [c13]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
Smoothness, Low Noise and Fast Rates. NIPS 2010: 2199-2207 - [i5]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Kernel-Based Halfspaces with the Zero-One Loss. CoRR abs/1005.3681 (2010) - [i4]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. CoRR abs/1006.1138 (2010) - [i3]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
Smoothness, Low-Noise and Fast Rates. CoRR abs/1009.3896 (2010) - [i2]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Beyond Regret. CoRR abs/1011.3168 (2010)
2000 – 2009
- 2009
- [c12]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
The Complexity of Improperly Learning Large Margin Halfspaces. COLT 2009 - [c11]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Stochastic Convex Optimization. COLT 2009 - [c10]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability and Stability in the General Learning Setting. COLT 2009 - [c9]Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan:
Multi-view clustering via canonical correlation analysis. ICML 2009: 129-136 - [i1]Sham M. Kakade, Ohad Shamir, Karthik Sridharan, Ambuj Tewari:
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. CoRR abs/0911.0054 (2009) - 2008
- [c8]Karthik Sridharan, Sham M. Kakade:
An Information Theoretic Framework for Multi-view Learning. COLT 2008: 403-414 - [c7]Sham M. Kakade, Karthik Sridharan, Ambuj Tewari:
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization. NIPS 2008: 793-800 - [c6]Karthik Sridharan, Shai Shalev-Shwartz, Nathan Srebro:
Fast Rates for Regularized Objectives. NIPS 2008: 1545-1552 - 2006
- [j1]K. G. Srinivasa, Karthik Sridharan, P. Deepa Shenoy, K. R. Venugopal, Lalit M. Patnaik:
A neural network based CBIR system using STI features and relevance feedback. Intell. Data Anal. 10(2): 121-137 (2006) - [c5]Karthik Sridharan, Matthew J. Beal, Venu Govindaraju:
Competitive Mixtures of Simple Neurons. ICPR (2) 2006: 494-497 - [c4]Faisal Farooq, Karthik Sridharan, Venu Govindaraju:
Identifying Handwritten Text in Mixed Documents. ICPR (2) 2006: 1142-1145 - 2005
- [c3]Karthik Sridharan, Venu Govindaraju:
A Sampling Based Approach to Facial Feature Extraction. AutoID 2005: 51-56 - [c2]Karthik Sridharan, Sankalp Nayak, Sharat Chikkerur, Venu Govindaraju:
A Probabilistic Approach to Semantic Face Retrieval System. AVBPA 2005: 977-986 - [c1]K. G. Srinivasa, Karthik Sridharan, P. Deepa Shenoy, K. R. Venugopal, Lalit M. Patnaik:
A Dynamic Migration Model for Self-adaptive Genetic Algorithms. IDEAL 2005: 555-562
Coauthor Index
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