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The Benefits of Implicit Regularization from SGD in Least Squares Problems
Neural Information Processing Systems (NeurIPS), 2021
10 August 2021
Difan Zou
Jingfeng Wu
Vladimir Braverman
Quanquan Gu
Dean Phillips Foster
Sham Kakade
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Papers citing
"The Benefits of Implicit Regularization from SGD in Least Squares Problems"
26 / 26 papers shown
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Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems
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A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time
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Max-Margin Token Selection in Attention Mechanism
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Federated Learning under Covariate Shifts with Generalization Guarantees
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Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
International Conference on Machine Learning (ICML), 2023
Jingfeng Wu
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Zixiang Chen
Vladimir Braverman
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Sham Kakade
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Local SGD in Overparameterized Linear Regression
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Losing momentum in continuous-time stochastic optimisation
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Alessandro Scagliotti
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The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Neural Information Processing Systems (NeurIPS), 2022
Jingfeng Wu
Difan Zou
Vladimir Braverman
Quanquan Gu
Sham Kakade
214
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Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
International Conference on Machine Learning (ICML), 2022
Kais Hariz
Hachem Kadri
Stéphane Ayache
Maher Moakher
Thierry Artières
210
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Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent
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Tianhao Wang
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Sanjeev Arora
368
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A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization
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Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
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265
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Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
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Sham Kakade
373
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Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?
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Guang Cheng
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Learning distinct features helps, provably
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