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Learning Theory

5,312 papers

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Papers

Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes NIPS 2022
Understanding Benign Overfitting in Gradient-Based Meta Learning NIPS 2022
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs NIPS 2022
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking NIPS 2022
Algorithms with Prediction Portfolios NIPS 2022
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness NIPS 2022
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes NIPS 2022
A Fourier Approach to Mixture Learning NIPS 2022
Gradient Methods Provably Converge to Non-Robust Networks NIPS 2022
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL NIPS 2022
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets NIPS 2022
Neuron with Steady Response Leads to Better Generalization NIPS 2022
Masked Prediction: A Parameter Identifiability View NIPS 2022
Characterization of Excess Risk for Locally Strongly Convex Population Risk NIPS 2022
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models NIPS 2022
Non-Gaussian Tensor Programs NIPS 2022
An Analysis of Ensemble Sampling NIPS 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit NIPS 2022
Revisiting Neural Scaling Laws in Language and Vision NIPS 2022
On the inability of Gaussian process regression to optimally learn compositional functions NIPS 2022
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks NIPS 2022
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds NIPS 2022
Robust Model Selection and Nearly-Proper Learning for GMMs NIPS 2022
Reconstructing Training Data From Trained Neural Networks NIPS 2022
Towards Understanding the Mixture-of-Experts Layer in Deep Learning NIPS 2022