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07/20/2023
Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
Despite the empirical success and practical significance of (relational)...
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06/21/2023
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
In stochastic zeroth-order optimization, a problem of practical relevanc...
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12/07/2022
Reconstructing Training Data from Model Gradient, Provably
Understanding when and how much a model gradient leaks information about...
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10/25/2022
COEP: Cascade Optimization for Inverse Problems with Entropy-Preserving Hyperparameter Tuning
We propose COEP, an automated and principled framework to solve inverse ...
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09/28/2022
Efficient Medical Image Assessment via Self-supervised Learning
High-performance deep learning methods typically rely on large annotated...
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05/11/2022
Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks
Influence Maximization (IM), which aims to select a set of users from a ...
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03/29/2022
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
We give novel algorithms for multi-task and lifelong linear bandits with...
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02/24/2022
Sample Efficiency of Data Augmentation Consistency Regularization
Data augmentation is popular in the training of large neural networks; c...
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01/22/2022
Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing
The goal of Knowledge Tracing (KT) is to estimate how well students have...
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10/18/2021
Provable Hierarchy-Based Meta-Reinforcement Learning
Hierarchical reinforcement learning (HRL) has seen widespread interest a...
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07/14/2021
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Deep Reinforcement Learning (RL) powered by neural net approximation of ...
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07/09/2021
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Bandit problems with linear or concave reward have been extensively stud...
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07/06/2021
A Short Note on the Relationship of Information Gain and Eluder Dimension
Eluder dimension and information gain are two widely used methods of com...
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05/05/2021
How Fine-Tuning Allows for Effective Meta-Learning
Representation learning has been widely studied in the context of meta-l...
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02/22/2021
A Theory of Label Propagation for Subpopulation Shift
One of the central problems in machine learning is domain adaptation. Un...
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10/11/2020
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Sampling is a fundamental and arguably very important task with numerous...
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08/03/2020
Predicting What You Already Know Helps: Provable Self-Supervised Learning
Self-supervised representation learning solves auxiliary prediction task...
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03/23/2020
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting
We propose signed splitting steepest descent (S3D), which progressively ...
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03/18/2020
Compressed Sensing with Invertible Generative Models and Dependent Noise
We study image inverse problems with invertible generative priors, speci...
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02/21/2020
Few-Shot Learning via Learning the Representation, Provably
This paper studies few-shot learning via representation learning, where ...
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02/17/2020
CAT: Customized Adversarial Training for Improved Robustness
Adversarial training has become one of the most effective methods for im...
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02/17/2020
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes
In a recent series of papers it has been established that variants of Gr...
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10/17/2019
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Large-scale machine learning training suffers from two prior challenges,...
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10/15/2019
SGD Learns One-Layer Networks in WGANs
Generative adversarial networks (GANs) are a widely used framework for l...
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06/18/2019
Inverting Deep Generative models, One layer at a time
We study the problem of inverting a deep generative model with ReLU acti...
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06/06/2019
Primal-Dual Block Frank-Wolfe
We propose a variant of the Frank-Wolfe algorithm for solving a class of...
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12/01/2018
Discrete Attacks and Submodular Optimization with Applications to Text Classification
Adversarial examples are carefully constructed modifications to an input...
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09/14/2018
Random Warping Series: A Random Features Method for Time-Series Embedding
Time series data analytics has been a problem of substantial interests f...
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03/25/2018
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Vanishing and exploding gradients are two of the main obstacles in train...
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02/22/2018
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
Large batch size training of Neural Networks has been shown to incur acc...
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02/12/2017
Similarity Preserving Representation Learning for Time Series Analysis
A considerable amount of machine learning algorithms take instance-featu...
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09/04/2015