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09/19/2023
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
We consider the problem of learning a model from multiple heterogeneous ...
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12/28/2022
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
We explore the ability of overparameterized shallow ReLU neural networks...
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07/27/2021
Stability Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel
We revisit on-average algorithmic stability of Gradient Descent (GD) for...
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07/27/2021
On the Role of Optimization in Double Descent: A Least Squares Study
Empirically it has been observed that the performance of deep neural net...
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07/12/2021
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping
We explore the ability of overparameterized shallow neural networks to l...
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10/31/2020
On Optimality of Meta-Learning in Fixed-Design Regression with Weighted Biased Regularization
We consider a fixed-design linear regression in the meta-learning model ...
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06/23/2020
PAC-Bayes Analysis Beyond the Usual Bounds
We focus on a stochastic learning model where the learner observes a fin...
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06/18/2020
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
We consider off-policy evaluation in the contextual bandit setting for t...
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02/05/2020
Locally-Adaptive Nonparametric Online Learning
One of the main strengths of online algorithms is their ability to adapt...
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09/04/2019
Efron-Stein PAC-Bayesian Inequalities
We prove semi-empirical concentration inequalities for random variables ...
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02/05/2019
Distribution-Dependent Analysis of Gibbs-ERM Principle
Gibbs-ERM learning is a natural idealized model of learning with stochas...
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09/28/2018
Efficient Linear Bandits through Matrix Sketching
We prove that two popular linear contextual bandit algorithms, OFUL and ...
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11/12/2015
When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks
Since Convolutional Neural Networks (CNNs) have become the leading learn...
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08/06/2014
