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Learning Theory

5,312 papers

Papers per year

Papers

Online Linear Optimization with Many Hints NIPS 2020
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification NIPS 2020
The Pitfalls of Simplicity Bias in Neural Networks NIPS 2020
Neural Complexity Measures NIPS 2020
Provably adaptive reinforcement learning in metric spaces NIPS 2020
Generalization Bound of Gradient Descent for Non-Convex Metric Learning NIPS 2020
Limits on Testing Structural Changes in Ising Models NIPS 2020
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms NIPS 2020
Information theoretic limits of learning a sparse rule NIPS 2020
What shapes feature representations? Exploring datasets, architectures, and training NIPS 2020
A Bayesian Perspective on Training Speed and Model Selection NIPS 2020
On the Modularity of Hypernetworks NIPS 2020
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth NIPS 2020
Generalized Leverage Score Sampling for Neural Networks NIPS 2020
An Optimal Elimination Algorithm for Learning a Best Arm NIPS 2020
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks NIPS 2020
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition NIPS 2020
Stage-wise Conservative Linear Bandits NIPS 2020
In search of robust measures of generalization NIPS 2020
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits NIPS 2020
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning NIPS 2020
Neural Architecture Generator Optimization NIPS 2020
Temporal Variability in Implicit Online Learning NIPS 2020
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes NIPS 2020
Multi-Stage Influence Function NIPS 2020