no code implementations • 25 Mar 2024 • Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei

We revisit the problem of offline reinforcement learning with value function realizability but without Bellman completeness.

no code implementations • 18 Jan 2024 • Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie

The theories of offline and online reinforcement learning, despite having evolved in parallel, have begun to show signs of the possibility for a unification, with algorithms and analysis techniques for one setting often having natural counterparts in the other.

1 code implementation • 14 Nov 2023 • Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun

In this work, we propose a new hybrid RL algorithm that combines an on-policy actor-critic method with offline data.

no code implementations • 24 Jul 2023 • Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu

We consider the problem of contextual bandits and imitation learning, where the learner lacks direct knowledge of the executed action's reward.

no code implementations • 27 Jun 2023 • Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang

Subsequently, given any subset of examples that wish to be unlearnt, the goal is to learn, without the knowledge of the original training dataset, a good predictor that is identical to the predictor that would have been produced when learning from scratch on the surviving examples.

1 code implementation • NeurIPS 2023 • Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari

We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced.

1 code implementation • 13 Oct 2022 • Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction.

no code implementations • 13 Oct 2022 • Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan

When these potentials further satisfy certain self-bounding properties, we show that they can be used to provide a convergence guarantee for Gradient Descent (GD) and SGD (even when the paths of GF and GD/SGD are quite far apart).

no code implementations • 27 Jun 2022 • Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan

A central problem in online learning and decision making -- from bandits to reinforcement learning -- is to understand what modeling assumptions lead to sample-efficient learning guarantees.

no code implementations • 24 Jun 2022 • Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun

We show our algorithm's computational and statistical complexities scale polynomially with respect to the horizon and the intrinsic dimension of the feature on the observation space.

no code implementations • 24 Jun 2022 • Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun

We study Reinforcement Learning for partially observable dynamical systems using function approximation.

no code implementations • 19 Jun 2022 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

This paper presents a theoretical analysis of such policies and provides the first regret and sample-complexity bounds for reinforcement learning with myopic exploration.

no code implementations • NeurIPS 2021 • Satyen Kale, Ayush Sekhari, Karthik Sridharan

We show that there is an SCO problem such that GD with any step size and number of iterations can only learn at a suboptimal rate: at least $\widetilde{\Omega}(1/n^{5/12})$.

no code implementations • NeurIPS 2021 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

In this work, we consider the more realistic setting of agnostic RL with rich observation spaces and a fixed class of policies $\Pi$ that may not contain any near-optimal policy.

no code implementations • NeurIPS 2021 • Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang

We investigate the problem of active learning in the streaming setting in non-parametric regimes, where the labels are stochastically generated from a class of functions on which we make no assumptions whatsoever.

no code implementations • NeurIPS 2021 • Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh

We study the problem of unlearning datapoints from a learnt model.

no code implementations • 24 Jun 2020 • Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan

We design an algorithm which finds an $\epsilon$-approximate stationary point (with $\|\nabla F(x)\|\le \epsilon$) using $O(\epsilon^{-3})$ stochastic gradient and Hessian-vector products, matching guarantees that were previously available only under a stronger assumption of access to multiple queries with the same random seed.

no code implementations • NeurIPS 2020 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

We study episodic reinforcement learning in Markov decision processes when the agent receives additional feedback per step in the form of several transition observations.

no code implementations • 13 Feb 2019 • Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth

Notably, we show that in the global oracle/statistical learning model, only logarithmic dependence on smoothness is required to find a near-stationary point, whereas polynomial dependence on smoothness is necessary in the local stochastic oracle model.

no code implementations • NeurIPS 2018 • Dylan J. Foster, Ayush Sekhari, Karthik Sridharan

We investigate 1) the rate at which refined properties of the empirical risk---in particular, gradients---converge to their population counterparts in standard non-convex learning tasks, and 2) the consequences of this convergence for optimization.

no code implementations • 13 Jul 2017 • Marc Pickett, Ayush Sekhari, James Davidson

Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning.

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