Statistical Learning

Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data ICML 2018 Orthogonal Machine Learning: Power and Limitations ICML 2018 Outlier Detection and Robust Estimation in Nonparametric Regression AISTATS 2018 Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate NeurIPS 2018 PAC-Bayes bounds for stable algorithms with instance-dependent priors NeurIPS 2018 PAC-learning in the presence of adversaries NeurIPS 2018 Parallelizing Spectrally Regularized Kernel Algorithms JMLR 2018 Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks NeurIPS 2018 Plug-in Estimators for Conditional Expectations and Probabilities AISTATS 2018 Post Selection Inference with Kernels AISTATS 2018 Practical Methods for Graph Two-Sample Testing NeurIPS 2018 Predicting Semantic Relations using Global Graph Properties EMNLP 2018 Privacy-preserving Prediction COLT 2018 Quantifying Learning Guarantees for Convex but Inconsistent Surrogates NeurIPS 2018 QuantTree: Histograms for Change Detection in Multivariate Data Streams ICML 2018 Ranking Distributions based on Noisy Sorting ICML 2018 Rates of Convergence of Spectral Methods for Graphon Estimation ICML 2018 Removing Hidden Confounding by Experimental Grounding NeurIPS 2018 Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression COLT 2018 Revealing Common Statistical Behaviors in Heterogeneous Populations ICML 2018 Robust Hypothesis Testing Using Wasserstein Uncertainty Sets NeurIPS 2018 Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) ICML 2018 Scalable Hash-Based Estimation of Divergence Measures AISTATS 2018 Scaling the Poisson GLM to massive neural datasets through polynomial approximations NeurIPS 2018 Sharp Bounds for Generalized Uniformity Testing NeurIPS 2018