Pages that link to "Item:Q2934003"

The following pages link to Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression (Q2934003):

Displaying 30 items.

• Approximate maximum likelihood estimation for population genetic inference (Q1670294) (← links)
• Stochastic heavy ball (Q1697485) (← links)
• On variance reduction for stochastic smooth convex optimization with multiplicative noise (Q1739038) (← links)
• Asymptotic distribution and convergence rates of stochastic algorithms for entropic optimal transportation between probability measures (Q2039796) (← links)
• Inversion-free subsampling Newton's method for large sample logistic regression (Q2151694) (← links)
• Bridging the gap between constant step size stochastic gradient descent and Markov chains (Q2196224) (← links)
• Finite-sample analysis of \(M\)-estimators using self-concordance (Q2219231) (← links)
• Online estimation of the asymptotic variance for averaged stochastic gradient algorithms (Q2317311) (← links)
• Generalized self-concordant functions: a recipe for Newton-type methods (Q2330645) (← links)
• Convergence of the exponentiated gradient method with Armijo line search (Q2420800) (← links)
• Optimal non-asymptotic analysis of the Ruppert-Polyak averaging stochastic algorithm (Q2680399) (← links)
• (Q4558562) (← links)
• Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression (Q4637017) (← links)
• (Q4969246) (← links)
• (Q4998897) (← links)
• Some Limit Properties of Markov Chains Induced by Recursive Stochastic Algorithms (Q5037552) (← links)
• (Q5053230) (← links)
• (Q5054630) (← links)
• Streaming constrained binary logistic regression with online standardized data (Q5073418) (← links)
• On the rates of convergence of parallelized averaged stochastic gradient algorithms (Q5110810) (← links)
• A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares) (Q5136291) (← links)
• An Efficient Stochastic Newton Algorithm for Parameter Estimation in Logistic Regressions (Q5212017) (← links)
• Convergence Rate of Incremental Gradient and Incremental Newton Methods (Q5237308) (← links)
• Composite Convex Minimization Involving Self-concordant-Like Cost Functions (Q5356980) (← links)
• Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization (Q5737735) (← links)
• <i>L</i>^<i>p</i> and almost sure rates of convergence of averaged stochastic gradient algorithms: locally strongly convex objective (Q5881051) (← links)
• Semi-discrete optimal transport: hardness, regularization and numerical solution (Q6038666) (← links)
• Adaptive step size rules for stochastic optimization in large-scale learning (Q6116586) (← links)
• When will gradient methods converge to max-margin classifier under ReLU models? (Q6541764) (← links)
• An efficient averaged stochastic Gauss-Newton algorithm for estimating parameters of nonlinear regressions models (Q6632594) (← links)