Near Optimal Sample Complexity Bounds

Media Summary: Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos and Alistair Stewart Akshay Ramachandran (University of Amsterdam) ... The 32nd International Conference on Algorithmic Learning Theory (ALT 2021) Title:

Near Optimal Sample Complexity Bounds - Detailed Analysis & Overview

Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos and Alistair Stewart Akshay Ramachandran (University of Amsterdam) ... The 32nd International Conference on Algorithmic Learning Theory (ALT 2021) Title: The 32nd International Conference on Algorithmic Learning Theory (ALT 2021) Title: On the Title: Bellman Eluder Dimension: New Rich Classes of RL Problems, and In the standard setup of parametric M-estimation with convex loss, we consider two population risk minimizers associated with two ...

These allow us to easily derive algorithms for many classes with We give an efficient algorithm for this learning problem that, for any fixed dimension, uses

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Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of ...

Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of ...

Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos and Alistair Stewart

Near Optimal Sample Complexity For Matrix And Tensor Normal Models Via Geodesic Convexity

Near Optimal Sample Complexity For Matrix And Tensor Normal Models Via Geodesic Convexity

Akshay Ramachandran (University of Amsterdam) ...

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

The 32nd International Conference on Algorithmic Learning Theory (ALT 2021) Title:

Learning versus Proofs of Complexity Lower Boundes

Learning versus Proofs of Complexity Lower Boundes

Marco Carmosino (UC San Diego) https://simons.berkeley.edu/talks/learning-versus-proofs-

The Sample Complexity of Revenue Maximization

The Sample Complexity of Revenue Maximization

Tim Roughgarden, Stanford University

On the Sample Complexity of Privately Learning Unbounded Gaussians

On the Sample Complexity of Privately Learning Unbounded Gaussians

The 32nd International Conference on Algorithmic Learning Theory (ALT 2021) Title: On the

Srinivasan Arunachalam: Optimal quantum sample complexity of learning algorithms

Srinivasan Arunachalam: Optimal quantum sample complexity of learning algorithms

We tightly determine the minimal

Chi Jin: Bellman Eluder Dimension: New Rich Classes of RL Problems and Sample-Efficient Algorithms

Chi Jin: Bellman Eluder Dimension: New Rich Classes of RL Problems and Sample-Efficient Algorithms

Title: Bellman Eluder Dimension: New Rich Classes of RL Problems, and

【NeurIPS 2018 Best Paper】Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians vi

【NeurIPS 2018 Best Paper】Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians vi

Nearly

[HDI Lab seminar] Near-Optimal Model Discrimination with Non-Disclosure

[HDI Lab seminar] Near-Optimal Model Discrimination with Non-Disclosure

In the standard setup of parametric M-estimation with convex loss, we consider two population risk minimizers associated with two ...

Michael P. Kim: Near-Optimal Algorithms for Omniprediction

Michael P. Kim: Near-Optimal Algorithms for Omniprediction

Brown CS Theory Seminar on Nov 13, 2024.

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Hypothesis Selection with Privacy Constraints

These allow us to easily derive algorithms for many classes with

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms

We give an efficient algorithm for this learning problem that, for any fixed dimension, uses