Cikkek nyilvánosan hozzáférhető megbízással - Karthik SridharanTovábbi információ
Valahol hozzáférhető: 25
Learning in games: Robustness of fast convergence
DJ Foster, Z Li, T Lykouris, K Sridharan, E Tardos
Advances in Neural Information Processing Systems 29, 2016
Megbízások: US National Science Foundation
Logistic regression: The importance of being improper
DJ Foster, S Kale, H Luo, M Mohri, K Sridharan
Conference on learning theory, 167-208, 2018
Megbízások: US National Science Foundation, US Department of Defense
Empirical entropy, minimax regret and minimax risk
A Rakhlin, K Sridharan, AB Tsybakov
Megbízások: US National Science Foundation
Uniform convergence of gradients for non-convex learning and optimization
DJ Foster, A Sekhari, K Sridharan
Advances in neural information processing systems 31, 2018
Megbízások: US National Science Foundation, US Department of Defense
Parameter-free online learning via model selection
DJ Foster, S Kale, M Mohri, K Sridharan
Advances in Neural Information Processing Systems 30, 2017
Megbízások: US Department of Defense
Bistro: An efficient relaxation-based method for contextual bandits
A Rakhlin, K Sridharan
International Conference on Machine Learning, 1977-1985, 2016
Megbízások: US National Science Foundation
Guarantees for epsilon-greedy reinforcement learning with function approximation
C Dann, Y Mansour, M Mohri, A Sekhari, K Sridharan
International conference on machine learning, 4666-4689, 2022
Megbízások: US National Science Foundation, European Commission
Second-order information in non-convex stochastic optimization: Power and limitations
Y Arjevani, Y Carmon, JC Duchi, DJ Foster, A Sekhari, K Sridharan
Conference on Learning Theory, 242-299, 2020
Megbízások: US National Science Foundation, US Department of Defense
The complexity of making the gradient small in stochastic convex optimization
DJ Foster, A Sekhari, O Shamir, N Srebro, K Sridharan, B Woodworth
Conference on Learning Theory, 1319-1345, 2019
Megbízások: US National Science Foundation, European Commission
On equivalence of martingale tail bounds and deterministic regret inequalities
A Rakhlin, K Sridharan
Conference on Learning Theory, 1704-1722, 2017
Megbízások: US National Science Foundation
Small-loss bounds for online learning with partial information
T Lykouris, K Sridharan, É Tardos
Conference on Learning Theory, 979-986, 2018
Megbízások: US National Science Foundation
Sgd: The role of implicit regularization, batch-size and multiple-epochs
A Sekhari, K Sridharan, S Kale
Advances In Neural Information Processing Systems 34, 27422-27433, 2021
Megbízások: US National Science Foundation
On the complexity of adversarial decision making
DJ Foster, A Rakhlin, A Sekhari, K Sridharan
Advances in Neural Information Processing Systems 35, 35404-35417, 2022
Megbízások: US National Science Foundation, US Department of Energy, US Department of …
Hypothesis set stability and generalization
DJ Foster, S Greenberg, S Kale, H Luo, M Mohri, K Sridharan
Advances in Neural Information Processing Systems 32, 2019
Megbízások: US National Science Foundation
Private causal inference
MJ Kusner, Y Sun, K Sridharan, KQ Weinberger
Artificial Intelligence and Statistics, 1308-1317, 2016
Megbízások: US National Science Foundation
Online learning: Sufficient statistics and the burkholder method
DJ Foster, A Rakhlin, K Sridharan
Conference On Learning Theory, 3028-3064, 2018
Megbízások: US National Science Foundation, US Department of Defense
Contextual bandits and imitation learning with preference-based active queries
A Sekhari, K Sridharan, W Sun, R Wu
Advances in Neural Information Processing Systems 36, 11261-11295, 2023
Megbízások: US National Science Foundation, US Department of Energy
Online learning with dynamics: A minimax perspective
K Bhatia, K Sridharan
Advances in Neural Information Processing Systems 33, 15020-15030, 2020
Megbízások: US National Science Foundation
Musings on deep learning: Properties of sgd
C Zhang, Q Liao, A Rakhlin, K Sridharan, B Miranda, N Golowich, ...
Center for Brains, Minds and Machines (CBMM), 2017
Megbízások: US National Science Foundation
Agnostic reinforcement learning with low-rank MDPs and rich observations
A Sekhari, C Dann, M Mohri, Y Mansour, K Sridharan
Advances in Neural Information Processing Systems 34, 19033-19045, 2021
Megbízások: US National Science Foundation, Natural Sciences and Engineering Research …
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