# Learning Theory

112 papers with code • 0 benchmarks • 0 datasets

Learning theory

## Benchmarks

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## Most implemented papers

# A Contextual-Bandit Approach to Personalized News Article Recommendation

In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.

# Learning a Variational Network for Reconstruction of Accelerated MRI Data

Due to its high computational performance, i. e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.

# Generalization in Machine Learning via Analytical Learning Theory

This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions.

# Robust Learning from Untrusted Sources

Modern machine learning methods often require more data for training than a single expert can provide.

# A Brain-inspired Algorithm for Training Highly Sparse Neural Networks

Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, Cosine similarity-based and Random Topology Exploration (CTRE), evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward.

# Foreseeing the Benefits of Incidental Supervision

Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.

# Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models

Computational learning theory states that many classes of boolean formulas are learnable in polynomial time.

# Reverse Engineering the Neural Tangent Kernel

The development of methods to guide the design of neural networks is an important open challenge for deep learning theory.

# Model Zoo: A Growing "Brain" That Learns Continually

We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them.

# Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons

The response time of physical computational elements is finite, and neurons are no exception.