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Regression

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Papers

Benefits of monotonicity in safe exploration with Gaussian processes UAI 2023
Practical privacy-preserving Gaussian process regression via secret sharing UAI 2023
Adaptive Conditional Quantile Neural Processes UAI 2023
Simple Transferability Estimation for Regression Tasks UAI 2023
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution UAI 2023
The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models UAI 2023
Semi-supervised learning of partial differential operators and dynamical flows UAI 2023
Learning Nonlinear Causal Effect via Kernel Anchor Regression UAI 2023
Greed is good: correspondence recovery for unlabeled linear regression UAI 2023
SIUNet: Sparsity Invariant U-Net for Edge-Aware Depth Completion WACV 2023
Patch-Level Gaze Distribution Prediction for Gaze Following WACV 2023
CountNet3D: A 3D Computer Vision Approach To Infer Counts of Occluded Objects WACV 2023
Back to MLP: A Simple Baseline for Human Motion Prediction WACV 2023
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model NIPS 2022
MABSplit: Faster Forest Training Using Multi-Armed Bandits NIPS 2022
Semi-supervised Active Linear Regression NIPS 2022
When does return-conditioned supervised learning work for offline reinforcement learning? NIPS 2022
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits NIPS 2022
On A Mallows-type Model For (Ranked) Choices NIPS 2022
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization NIPS 2022
C-Mixup: Improving Generalization in Regression NIPS 2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces NIPS 2022
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression NIPS 2022
Learning with little mixing NIPS 2022
Dynamic Tensor Product Regression NIPS 2022