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06/15/2023
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
In federated frequency estimation (FFE), multiple clients work together ...
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05/19/2023
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
Recent research has observed that in machine learning optimization, grad...
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03/17/2023
Fixed Design Analysis of Regularization-Based Continual Learning
We consider a continual learning (CL) problem with two linear regression...
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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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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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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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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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08/11/2021
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
For the problem of task-agnostic reinforcement learning (RL), an agent f...
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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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04/17/2021
Lifelong Learning with Sketched Structural Regularization
Preventing catastrophic forgetting while continually learning new tasks ...
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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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11/25/2020
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
In this paper we consider multi-objective reinforcement learning where t...
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11/04/2020
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate
Understanding the algorithmic regularization effect of stochastic gradie...
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08/15/2020
Obtaining Adjustable Regularization for Free via Iterate Averaging
Regularization for optimization is a crucial technique to avoid overfitt...
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06/18/2019
The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation
The randomness in Stochastic Gradient Descent (SGD) is considered to pla...
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08/18/2018
Tangent-Normal Adversarial Regularization for Semi-supervised Learning
The ever-increasing size of modern datasets combined with the difficulty...
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03/01/2018