Chicheng Zhang

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

We study multi-task reinforcement learning (RL) in tabular episodic Mark...
07/19/2021 ∙ by Chicheng Zhang, et al. ∙ 0 ∙ share

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Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise

We develop a computationally-efficient PAC active learning algorithm for...
02/10/2021 ∙ by Chicheng Zhang, et al. ∙ 0 ∙ share

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Multitask Bandit Learning through Heterogeneous Feedback Aggregation

In many real-world applications, multiple agents seek to learn how to pe...
10/29/2020 ∙ by Zhi Wang, et al. ∙ 0 ∙ share

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Active Online Domain Adaptation

Online machine learning systems need to adapt to domain shifts. Meanwhil...
06/25/2020 ∙ by Yining Chen, et al. ∙ 9 ∙ share

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Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality

We study stochastic structured bandits for minimizing regret. The fact t...
06/15/2020 ∙ by Kwang-Sung Jun, et al. ∙ 0 ∙ share

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Efficient Contextual Bandits with Continuous Actions

We create a computationally tractable algorithm for contextual bandits w...
06/10/2020 ∙ by Maryam Majzoubi, et al. ∙ 0 ∙ share

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Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance

This paper is concerned with computationally efficient learning of homog...
06/06/2020 ∙ by Jie Shen, et al. ∙ 7 ∙ share

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Efficient active learning of sparse halfspaces with arbitrary bounded noise

In this work we study active learning of homogeneous s-sparse halfspaces...
02/12/2020 ∙ by Chicheng Zhang, et al. ∙ 11 ∙ share

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Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

We design a new algorithm for batch active learning with deep neural net...
06/09/2019 ∙ by Jordan T. Ash, et al. ∙ 3 ∙ share

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Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case

We study the problem of efficient online multiclass linear classificatio...
02/06/2019 ∙ by Alina Beygelzimer, et al. ∙ 0 ∙ share

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Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

We study contextual bandit learning with an abstract policy class and co...
02/05/2019 ∙ by Akshay Krishnamurthy, et al. ∙ 38 ∙ share

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Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

We investigate the feasibility of learning from both fully-labeled super...
01/02/2019 ∙ by Chicheng Zhang, et al. ∙ 6 ∙ share

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Efficient active learning of sparse halfspaces

We study the problem of efficient PAC active learning of homogeneous lin...
05/07/2018 ∙ by Chicheng Zhang, et al. ∙ 0 ∙ share

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Spectral Learning of Binomial HMMs for DNA Methylation Data

We consider learning parameters of Binomial Hidden Markov Models, which ...
02/07/2018 ∙ by Chicheng Zhang, et al. ∙ 0 ∙ share

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Efficient Online Bandit Multiclass Learning with Õ(√(T)) Regret

We present an efficient second-order algorithm with Õ(1/η√(T)) regret fo...
02/25/2017 ∙ by Alina Beygelzimer, et al. ∙ 0 ∙ share

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Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces

It has been a long-standing problem to efficiently learn a halfspace usi...
02/18/2017 ∙ by Songbai Yan, et al. ∙ 0 ∙ share

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Search Improves Label for Active Learning

We investigate active learning with access to two distinct oracles: Labe...
02/23/2016 ∙ by Alina Beygelzimer, et al. ∙ 0 ∙ share

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Active Learning from Weak and Strong Labelers

An active learner is given a hypothesis class, a large set of unlabeled ...
10/09/2015 ∙ by Chicheng Zhang, et al. ∙ 0 ∙ share

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Spectral Learning of Large Structured HMMs for Comparative Epigenomics

We develop a latent variable model and an efficient spectral algorithm m...
06/04/2015 ∙ by Chicheng Zhang, et al. ∙ 0 ∙ share

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Beyond Disagreement-based Agnostic Active Learning

We study agnostic active learning, where the goal is to learn a classifi...
07/10/2014 ∙ by Chicheng Zhang, et al. ∙ 0 ∙ share

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Chicheng Zhang

Featured Co-authors

Tengyu Ma

Daniel Hsu

Akshay Krishnamurthy

Alekh Agarwal

Kamalika Chaudhuri

Jie Shen

John Langford

Zhi Wang

Hal Daumé III

Haipeng Luo

Pranjal Awasthi

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise

Multitask Bandit Learning through Heterogeneous Feedback Aggregation

Active Online Domain Adaptation

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality

Efficient Contextual Bandits with Continuous Actions

Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance

Efficient active learning of sparse halfspaces with arbitrary bounded noise

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Efficient active learning of sparse halfspaces

Spectral Learning of Binomial HMMs for DNA Methylation Data

Efficient Online Bandit Multiclass Learning with Õ(√(T)) Regret

Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces

Search Improves Label for Active Learning

Active Learning from Weak and Strong Labelers

Spectral Learning of Large Structured HMMs for Comparative Epigenomics

Beyond Disagreement-based Agnostic Active Learning