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Regression

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Papers

Model Inversion Networks for Model-Based Optimization NIPS 2020
Exact expressions for double descent and implicit regularization via surrogate random design NIPS 2020
Agnostic Learning of a Single Neuron with Gradient Descent NIPS 2020
Unfolding the Alternating Optimization for Blind Super Resolution NIPS 2020
SLIP: Learning to predict in unknown dynamical systems with long-term memory NIPS 2020
NeuMiss networks: differentiable programming for supervised learning with missing values. NIPS 2020
Neural Power Units NIPS 2020
Neuronal Gaussian Process Regression NIPS 2020
Interior Point Solving for LP-based prediction+optimisation NIPS 2020
A simple normative network approximates local non-Hebbian learning in the cortex NIPS 2020
Fair regression with Wasserstein barycenters NIPS 2020
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree NIPS 2020
Learning from Aggregate Observations NIPS 2020
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models NIPS 2020
Myersonian Regression NIPS 2020
Minimax Bounds for Generalized Linear Models NIPS 2020
Optimal Learning from Verified Training Data NIPS 2020
Empirical Likelihood for Contextual Bandits NIPS 2020
Field-wise Learning for Multi-field Categorical Data NIPS 2020
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression NIPS 2020
Truncated Linear Regression in High Dimensions NIPS 2020
Semi-Supervised Neural Architecture Search NIPS 2020
Smooth And Consistent Probabilistic Regression Trees NIPS 2020
An implicit function learning approach for parametric modal regression NIPS 2020
Deep Smoothing of the Implied Volatility Surface NIPS 2020