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Learning Theory

5,312 papers

Papers per year

Papers

Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model NIPS 2020
Identifying Learning Rules From Neural Network Observables NIPS 2020
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective NIPS 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation NIPS 2020
Toward the Fundamental Limits of Imitation Learning NIPS 2020
Logarithmic Pruning is All You Need NIPS 2020
Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes NIPS 2020
Triple descent and the two kinds of overfitting: where & why do they appear? NIPS 2020
Inferring learning rules from animal decision-making NIPS 2020
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition NIPS 2020
On ranking via sorting by estimated expected utility NIPS 2020
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks NIPS 2020
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms NIPS 2020
PAC-Bayes Learning Bounds for Sample-Dependent Priors NIPS 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks NIPS 2020
Exact expressions for double descent and implicit regularization via surrogate random design NIPS 2020
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling NIPS 2020
An analytic theory of shallow networks dynamics for hinge loss classification NIPS 2020
Agnostic Learning of a Single Neuron with Gradient Descent NIPS 2020
Learning under Model Misspecification: Applications to Variational and Ensemble methods NIPS 2020
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel NIPS 2020
Estimating decision tree learnability with polylogarithmic sample complexity NIPS 2020
Boundary thickness and robustness in learning models NIPS 2020
The Power of Comparisons for Actively Learning Linear Classifiers NIPS 2020
From Boltzmann Machines to Neural Networks and Back Again NIPS 2020