Latest Articles for Sample Complexity

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.

Explore how batch size affects the training of machine learning models.

Learn how to estimate entropy while managing limited memory resources.

Data-driven techniques must protect privacy while identifying the best options.

A look at PAC learning and its role in efficient data-driven decision-making.

Exploring new algorithms for multiclass classification under limited feedback conditions.

Discover recent progress in shadow estimation techniques for analyzing quantum states.

Explore how kernel regression aids decision-making under uncertainty using historical data.

New models enhance video generation by capturing crucial spatial and temporal relationships.

A look at how models predict unseen data.

A look into how quantum properties enhance machine learning techniques.

Discover how machine learning is improving quantum circuit understanding and predictions.

Introducing Fair Best Arm Identification with fairness constraints in decision-making.

A look at how players adapt strategies in competitive scenarios.

New techniques enhance offline learning for Regular Decision Processes in complex environments.

A look into RAI within the multi-armed bandit framework to optimize decision-making.

New methods in multi-agent learning tackle challenges of diverse agents.

Exploring efficient algorithms for decentralized learning while preserving data privacy.

Exploring how quantum concepts can advance machine learning methods.

An overview of optimal sampling methods in least-squares approximation.

Key methods for measuring how close quantum states are to desired outcomes.

Examining how hard samples affect model performance and the reliability of test accuracy.

This article explores smooth boosting and its advantages in model training.

This article discusses neural networks that effectively blend approximation and generalization.

Explore the complexities of game strategies and decision-making techniques.

Learn how shadow tomography gathers data on quantum states efficiently.

This study explores sample complexity in list regression for improved predictions.

Discover effective strategies for informed decision-making in uncertain environments.