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08/23/2023
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
The recent surge of generative AI has been fueled by the generative powe...
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06/20/2023
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks
We study the implicit bias of batch normalization trained by gradient de...
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06/08/2023
Robust Learning with Progressive Data Expansion Against Spurious Correlation
While deep learning models have shown remarkable performance in various ...
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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...
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05/15/2023
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
Recent studies have shown that episodic reinforcement learning (RL) is n...
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05/15/2023
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension
Recently, there has been remarkable progress in reinforcement learning (...
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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...
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05/01/2023
Personalized Federated Learning under Mixture of Distributions
The recent trend towards Personalized Federated Learning (PFL) has garne...
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03/17/2023
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
We study reward-free reinforcement learning (RL) with linear function ap...
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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...
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03/15/2023
Borda Regret Minimization for Generalized Linear Dueling Bandits
Dueling bandits are widely used to model preferential feedback that is p...
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03/15/2023
The Benefits of Mixup for Feature Learning
Mixup, a simple data augmentation method that randomly mixes two data po...
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03/07/2023
Benign Overfitting for Two-layer ReLU Networks
Modern deep learning models with great expressive power can be trained t...
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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...
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12/12/2022
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation....
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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...
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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...
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08/04/2022
Towards Understanding Mixture of Experts in Deep Learning
The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlle...
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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...
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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...
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05/23/2022
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
Recent studies have shown that episodic reinforcement learning (RL) is n...
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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 ...
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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 ...
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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...
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02/14/2022
Benign Overfitting in Two-layer Convolutional Neural Networks
Modern neural networks often have great expressive power and can be trai...
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01/24/2022
Learning Contextual Bandits Through Perturbed Rewards
Thanks to the power of representation learning, neural contextual bandit...
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12/31/2021
Benign Overfitting in Adversarially Robust Linear Classification
"Benign overfitting", where classifiers memorize noisy training data yet...
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12/15/2021
On the Convergence and Robustness of Adversarial Training
Improving the robustness of deep neural networks (DNNs) to adversarial e...
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10/25/2021
Learning Stochastic Shortest Path with Linear Function Approximation
We study the stochastic shortest path (SSP) problem in reinforcement lea...
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10/25/2021
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
Escaping from saddle points and finding local minima is a central proble...
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10/25/2021
Linear Contextual Bandits with Adversarial Corruptions
We study the linear contextual bandit problem in the presence of adversa...
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10/19/2021
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes
Reinforcement learning (RL) algorithms can be used to provide personaliz...
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10/14/2021
Adaptive Differentially Private Empirical Risk Minimization
We propose an adaptive (stochastic) gradient perturbation method for dif...
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10/12/2021
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
We study the model-based reward-free reinforcement learning with linear ...
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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...
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10/08/2021
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
In heterogeneous rank aggregation problems, users often exhibit various ...
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10/07/2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Deep neural networks (DNNs) are known to be vulnerable to adversarial at...
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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...
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08/10/2021
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Stochastic gradient descent (SGD) exhibits strong algorithmic regulariza...
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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...
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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...
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06/22/2021
Pure Exploration in Kernel and Neural Bandits
We study pure exploration in bandits, where the dimension of the feature...
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06/22/2021
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
We study the off-policy evaluation (OPE) problem in reinforcement learni...
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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 ...
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06/22/2021
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation
We study reinforcement learning (RL) with linear function approximation....
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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...
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04/19/2021
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
We analyze the properties of adversarial training for learning adversari...
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03/23/2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
There is an increasing realization that algorithmic inductive biases are...
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02/25/2021
Batched Neural Bandits
In many sequential decision-making problems, the individuals are split i...
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02/17/2021
