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Regression

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Unconditional Coresets for Regularized Loss Minimization AISTATS 2020
Accelerating Gradient Boosting Machines AISTATS 2020
Online Binary Space Partitioning Forests AISTATS 2020
Sparse Hilbert-Schmidt Independence Criterion Regression AISTATS 2020
Scalable Feature Selection for (Multitask) Gradient Boosted Trees AISTATS 2020
Low-rank regularization and solution uniqueness in over-parameterized matrix sensing AISTATS 2020
Neural Topic Model with Attention for Supervised Learning AISTATS 2020
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses AISTATS 2020
Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models AISTATS 2020
A Multiclass Classification Approach to Label Ranking AISTATS 2020
EM Converges for a Mixture of Many Linear Regressions AISTATS 2020
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information AISTATS 2020
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach AISTATS 2020
Sparse Orthogonal Variational Inference for Gaussian Processes AISTATS 2020
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis AISTATS 2020
Censored Quantile Regression Forest AISTATS 2020
Conditional Linear Regression AISTATS 2020
Deep Structured Mixtures of Gaussian Processes AISTATS 2020
Dynamic content based ranking AISTATS 2020
Hyperbolic Manifold Regression AISTATS 2020
Safe-Bayesian Generalized Linear Regression AISTATS 2020
Support recovery and sup-norm convergence rates for sparse pivotal estimation AISTATS 2020
Learning Entangled Single-Sample Distributions via Iterative Trimming AISTATS 2020
Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters AISTATS 2020
Statistical and Computational Rates in Graph Logistic Regression AISTATS 2020