conftrace_

least squares regression

49 papers

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Also known as

LSR

Co-occurring keywords

stochastic gradient descent (1091) convergence rate (607) convex optimization (1321) least-squares regression (10) gradient descent (1144) ridge regression (134) early stopping (76) random projection (145) greedy algorithm (242) sparse regression (175)

Papers

Compressed and distributed least-squares regression: convergence rates with applications to federated learning JMLR 2024
Least Squares Regression Can Exhibit Under-Parameterized Double Descent NIPS 2024
Distributed Least Squares in Small Space via Sketching and Bias Reduction NIPS 2024
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract COLT 2024
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent AISTATS 2024
Efficient Top-K Feature Selection Using Coordinate Descent Method AAAI 2023
Hyperparameter Tuning of an Off-Policy Reinforcement Learning Algorithm for H∞ Tracking Control L4DC 2023
Least Squares Estimation using Sketched Data with Heteroskedastic Errors ICML 2022
Accelerated SGD for Non-Strongly-Convex Least Squares COLT 2022
Learning Sparse Fixed-Structure Gaussian Bayesian Networks AISTATS 2022
Gradient Descent in RKHS with Importance Labeling AISTATS 2021
In-Database Regression in Input Sparsity Time ICML 2021
Optimal Randomized First-Order Methods for Least-Squares Problems ICML 2020
Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections JMLR 2020
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms NIPS 2020
Optimal Learning from Verified Training Data NIPS 2020
On Coresets for Regularized Regression ICML 2020
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression COLT 2019
A Continuous-Time View of Early Stopping for Least Squares Regression AISTATS 2019
Asymptotics for Sketching in Least Squares Regression NIPS 2019
Implicit Regularization for Optimal Sparse Recovery NIPS 2019
Sublinear Time Numerical Linear Algebra for Structured Matrices AAAI 2019
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification JMLR 2018
Accelerating Stochastic Gradient Descent for Least Squares Regression COLT 2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces ICML 2018