statistical learning

Statistically Consistent Saliency Estimation ICCV 2021 Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency NeurIPS 2021 Tighter Expected Generalization Error Bounds via Wasserstein Distance NeurIPS 2021 Towards Practical Mean Bounds for Small Samples ICML 2021 You should evaluate your language model on marginal likelihood over tokenisations EMNLP 2021 A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning ICML 2020 A Statistical Learning Approach to Modal Regression JMLR 2020 Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance NeurIPS 2020 Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning ICML 2020 Effectively Unbiased FID and Inception Score and Where to Find Them CVPR 2020 Estimation Rates for Sparse Linear Cyclic Causal Models UAI 2020 Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes JMLR 2020 Feature Noise Induces Loss Discrepancy Across Groups ICML 2020 Generalisation error in learning with random features and the hidden manifold model ICML 2020 Hypothesis Testing Interpretations and Renyi Differential Privacy AISTATS 2020 Improved PAC-Bayesian Bounds for Linear Regression AAAI 2020 Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing ICML 2020 Kernel Alignment Risk Estimator: Risk Prediction from Training Data NeurIPS 2020 Learning discrete distributions: user vs item-level privacy NeurIPS 2020 Minimax Estimation of Conditional Moment Models NeurIPS 2020 PAC-Bayes Learning Bounds for Sample-Dependent Priors NeurIPS 2020 Phase retrieval in high dimensions: Statistical and computational phase transitions NeurIPS 2020 Predicting Declension Class from Form and Meaning ACL 2020 Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions NeurIPS 2020 Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures ICML 2020