b'Quanquan Gu'

research

∙ 05/30/2023

Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models

Large language models (LLMs) have significantly advanced the field of na...

1 Chen Ling, et al. ∙

research

∙ 05/15/2023

Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs

Recent studies have shown that episodic reinforcement learning (RL) is n...

4 Kaixuan Ji, et al. ∙

research

∙ 05/15/2023

Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension

Recently, there has been remarkable progress in reinforcement learning (...

1 Yue Wu, et al. ∙

research

∙ 05/10/2023

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

We study multi-agent reinforcement learning in the setting of episodic M...

3 Yifei Min, et al. ∙

research

∙ 05/01/2023

Personalized Federated Learning under Mixture of Distributions

The recent trend towards Personalized Federated Learning (PFL) has garne...

2 Yue Wu, et al. ∙

research

∙ 03/17/2023

Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs

We study reward-free reinforcement learning (RL) with linear function ap...

4 Junkai Zhang, et al. ∙

research

∙ 03/16/2023

On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits

We study linear contextual bandits in the misspecified setting, where th...

1 Weitong Zhang, et al. ∙

research

∙ 03/15/2023

Borda Regret Minimization for Generalized Linear Dueling Bandits

Dueling bandits are widely used to model preferential feedback that is p...

4 Yue Wu, et al. ∙

research

∙ 03/15/2023

The Benefits of Mixup for Feature Learning

Mixup, a simple data augmentation method that randomly mixes two data po...

0 Difan Zou, et al. ∙

research

∙ 03/07/2023

Benign Overfitting for Two-layer ReLU Networks

Modern deep learning models with great expressive power can be trained t...

2 Yiwen Kou, et al. ∙

research

∙ 03/03/2023

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples

This paper considers the problem of learning a single ReLU neuron with s...

1 Jingfeng Wu, et al. ∙

research

∙ 12/12/2022

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes

We study reinforcement learning (RL) with linear function approximation....

14 Jiafan He, et al. ∙

research

∙ 09/30/2022

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning

With the increasing need for handling large state and action spaces, gen...

13 Zixiang Chen, et al. ∙

research

∙ 08/10/2022

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium

We consider learning Nash equilibria in two-player zero-sum Markov Games...

11 Chris Junchi Li, et al. ∙

research

∙ 08/04/2022

Towards Understanding Mixture of Experts in Deep Learning

The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlle...

27 Zixiang Chen, et al. ∙

research

∙ 08/03/2022

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift

We study linear regression under covariate shift, where the marginal dis...

4 Jingfeng Wu, et al. ∙

research

∙ 07/07/2022

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

We study federated contextual linear bandits, where M agents cooperate w...

6 Jiafan He, et al. ∙

research

∙ 05/23/2022

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs

Recent studies have shown that episodic reinforcement learning (RL) is n...

7 Dongruo Zhou, et al. ∙

research

∙ 03/16/2022

On the Convergence of Certified Robust Training with Interval Bound Propagation

Interval Bound Propagation (IBP) is so far the base of state-of-the-art ...

7 Yihan Wang, et al. ∙

research

∙ 03/07/2022

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime

Stochastic gradient descent (SGD) has achieved great success due to its ...

4 Difan Zou, et al. ∙

research

∙ 02/28/2022

Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds

We consider learning a stochastic bandit model, where the reward functio...

1 Heyang Zhao, et al. ∙

research

∙ 02/14/2022

Benign Overfitting in Two-layer Convolutional Neural Networks

Modern neural networks often have great expressive power and can be trai...

0 Yuan Cao, et al. ∙

research

∙ 01/24/2022

Learning Contextual Bandits Through Perturbed Rewards

Thanks to the power of representation learning, neural contextual bandit...

4 Yiling Jia, et al. ∙

research

∙ 12/31/2021

Benign Overfitting in Adversarially Robust Linear Classification

"Benign overfitting", where classifiers memorize noisy training data yet...

3 Jinghui Chen, et al. ∙

research

∙ 12/15/2021

On the Convergence and Robustness of Adversarial Training

Improving the robustness of deep neural networks (DNNs) to adversarial e...

8 Yisen Wang, et al. ∙

research

∙ 10/25/2021

Learning Stochastic Shortest Path with Linear Function Approximation

We study the stochastic shortest path (SSP) problem in reinforcement lea...

11 Yifei Min, et al. ∙

research

∙ 10/25/2021

Faster Perturbed Stochastic Gradient Methods for Finding Local Minima

Escaping from saddle points and finding local minima is a central proble...

0 Zixiang Chen, et al. ∙

research

∙ 10/25/2021

Linear Contextual Bandits with Adversarial Corruptions

We study the linear contextual bandit problem in the presence of adversa...

5 Heyang Zhao, et al. ∙

research

∙ 10/19/2021

Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes

Reinforcement learning (RL) algorithms can be used to provide personaliz...

9 Chonghua Liao, et al. ∙

research

∙ 10/14/2021

Adaptive Differentially Private Empirical Risk Minimization

We propose an adaptive (stochastic) gradient perturbation method for dif...

12 Xiaoxia Wu, et al. ∙

research

∙ 10/12/2021

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation

We study the model-based reward-free reinforcement learning with linear ...

8 Weitong Zhang, et al. ∙

research

∙ 10/12/2021

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

Stochastic gradient descent (SGD) has been demonstrated to generalize we...

5 Jingfeng Wu, et al. ∙

research

∙ 10/08/2021

Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons

In heterogeneous rank aggregation problems, users often exhibit various ...

6 Yue Wu, et al. ∙

research

∙ 10/07/2021

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

Deep neural networks (DNNs) are known to be vulnerable to adversarial at...

0 Hanxun Huang, et al. ∙

research

∙ 08/25/2021

Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization

Adaptive gradient methods such as Adam have gained increasing popularity...

17 Difan Zou, et al. ∙

research

∙ 08/10/2021

The Benefits of Implicit Regularization from SGD in Least Squares Problems

Stochastic gradient descent (SGD) exhibits strong algorithmic regulariza...

0 Difan Zou, et al. ∙

research

∙ 06/25/2021

Self-training Converts Weak Learners to Strong Learners in Mixture Models

We consider a binary classification problem when the data comes from a m...

2 Spencer Frei, et al. ∙

research

∙ 06/25/2021

Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent

Although the optimization objectives for learning neural networks are hi...

7 Spencer Frei, et al. ∙

research

∙ 06/22/2021

Pure Exploration in Kernel and Neural Bandits

We study pure exploration in bandits, where the dimension of the feature...

20 Yinglun Zhu, et al. ∙

research

∙ 06/22/2021

Variance-Aware Off-Policy Evaluation with Linear Function Approximation

We study the off-policy evaluation (OPE) problem in reinforcement learni...

7 Yifei Min, et al. ∙

research

∙ 06/22/2021

Provably Efficient Representation Learning in Low-rank Markov Decision Processes

The success of deep reinforcement learning (DRL) is due to the power of ...

27 Weitong Zhang, et al. ∙

research

∙ 06/22/2021

Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation

We study reinforcement learning (RL) with linear function approximation....

1 Jiafan He, et al. ∙

research

∙ 04/28/2021

Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures

Modern machine learning systems such as deep neural networks are often h...

4 Yuan Cao, et al. ∙

research

∙ 04/19/2021

Provable Robustness of Adversarial Training for Learning Halfspaces with Noise

We analyze the properties of adversarial training for learning adversari...

8 Difan Zou, et al. ∙

research

∙ 03/23/2021

Benign Overfitting of Constant-Stepsize SGD for Linear Regression

There is an increasing realization that algorithmic inductive biases are...

9 Difan Zou, et al. ∙

research

∙ 02/25/2021

Batched Neural Bandits

In many sequential decision-making problems, the individuals are split i...

6 Quanquan Gu, et al. ∙

research

∙ 02/17/2021

Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation

We study the reinforcement learning for finite-horizon episodic Markov d...

4 Jiafan He, et al. ∙

research

∙ 02/15/2021

Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation

We study reinforcement learning for two-player zero-sum Markov games wit...

4 Zixiang Chen, et al. ∙

research

∙ 02/15/2021

Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation

We study reinforcement learning in an infinite-horizon average-reward se...

7 Yue Wu, et al. ∙

research

∙ 01/06/2021

Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints

We study reinforcement learning (RL) with linear function approximation ...

37 Tianhao Wang, et al. ∙

Quanquan Gu

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