← Optimization & Theory
Machine Learning › Optimization & Theory ›

Sample Complexity

97 directly classified papers

Papers per year

Papers

SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NIPS 2021
Sample Complexity Bounds for RNNs with Application to Combinatorial Graph Problems (Student Abstract) AAAI 2020
The Power of Comparisons for Actively Learning Linear Classifiers NIPS 2020
On the Theory of Transfer Learning: The Importance of Task Diversity NIPS 2020
Optimal Prediction of the Number of Unseen Species with Multiplicity NIPS 2020
Limits on Testing Structural Changes in Ising Models NIPS 2020
Sample complexity and effective dimension for regression on manifolds NIPS 2020
Sample Complexity of Uniform Convergence for Multicalibration NIPS 2020
Efficient Learning of Discrete Graphical Models NIPS 2020
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals NIPS 2020
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NIPS 2020
Online Planning with Lookahead Policies NIPS 2020
Worst-Case Analysis for Randomly Collected Data NIPS 2020
Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems AAAI 2020
A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control AAAI 2020
A General Framework for Symmetric Property Estimation NIPS 2019
Characterizing the Sample Complexity of Pure Private Learners JMLR 2019
Estimating Entropy of Distributions in Constant Space NIPS 2019
Limits of Private Learning with Access to Public Data NIPS 2019
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model NIPS 2019
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons NIPS 2019
Structure Learning with Side Information: Sample Complexity NIPS 2019
Graph-based Discriminators: Sample Complexity and Expressiveness NIPS 2019
Multiclass Learning from Contradictions NIPS 2019
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes NIPS 2018