Latest Articles for Sample Complexity

A study on learning Gaussian mixtures while protecting individual data privacy.

This study highlights the sample complexity of Neural Policy Mirror Descent algorithms in deep learning.

Exploring methods to understand multiple experts' motivations in decision-making.

Examining sample complexity in stochastic programming with heavy-tailed distributions.

This method simplifies log-concave density estimation using independent components.

Examining the complexities and strategies for neural network learning in various data types.

Exploring new methods for better learning outcomes in multiplayer games.

A study on identifying truncation effects in statistical data analysis.

Investigating methods for understanding unknown systems through partial information.

Learn how conditional invariance enhances model performance across varying data types.

A new algorithm improves offline RL efficiency with low-rank MDP structures.

Exploring how neural networks can predict accurately on unseen data.

New methods enhance decision-making for multiple agents in uncertain environments.

This article discusses sampling techniques from mean-field models in complex systems.

Innovative methods for polynomial regression in noisy environments.

New strategies improve decision-making efficiency in various fields.

A look at PAC learning, quantum techniques, and their implications for machine learning.

New algorithms improve decision-making in constrained environments.

A look into how different neural networks learn from images.

A look into how machines learn from past data without real-time interaction.

Learn how direct-search methods help find optimal solutions effectively.

This article discusses efficient learning in multilayer networked dynamical systems.

A new framework for efficient reinforcement learning in complex environments.

A clear guide on decision trees and their real-world applications.

New algorithm improves learning efficiency across multiple tasks.

A new method for efficiently modeling complex Gaussian mixtures.

Explore the impact of norms on neural networks' training and performance.

Exploring the role of gradient descent in stochastic optimization and its implications for sample size.

A study on improving federated learning models for devices with limited resources.

Examining the impact and potential of quantum learning protocols in AI advancements.

New protocols reduce sample complexity for mixed quantum states.

A study revealing factors that influence in-context learning in Transformers.

A look into efficient methods for estimating quantum state properties.

This paper analyzes multi-index models and their role in learning from data.

A method to learn low-dimensional dynamics from noisy high-dimensional observations.

New methods enhance optimization in complex scenarios using latent spaces and noise.

Introducing new algorithms for efficient bilevel optimization in machine learning.

LLP enables model training using average labels from grouped examples.

A new approach for agents to minimize difficult non-smooth functions together.

A new method enhances measurement strategies for quantum systems.