Probabilistic Line Searches for Stochastic Optimization

10 February 2015

Papers citing "Probabilistic Line Searches for Stochastic Optimization"

19 / 19 papers shown

Title
Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search Nuojin Cheng Alireza Doostan Stephen Becker 39 0 0 30 Apr 2025
Learning-Rate-Free Learning: Dissecting D-Adaptation and Probabilistic Line Search Max McGuinness ODL 20 0 0 06 Aug 2023
Local Bayesian optimization via maximizing probability of descent Quan Nguyen Kaiwen Wu Jacob R. Gardner Roman Garnett 17 23 0 21 Oct 2022
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity A. Davtyan Sepehr Sameni L. Cerkezi Givi Meishvili Adam Bielski Paolo Favaro ODL 53 2 0 07 Jul 2021
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen Dami Choi Lukas Balles David Duvenaud Philipp Hennig ODL 44 6 0 09 Nov 2020
A straightforward line search approach on the expected empirical loss for stochastic deep learning problems Max Mutschler A. Zell 33 0 0 02 Oct 2020
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 11 29 0 18 Jan 2020
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 27 11 0 15 Sep 2019
Stochastic quasi-Newton with line-search regularization A. Wills Thomas B. Schon ODL 16 21 0 03 Sep 2019
LOSSGRAD: automatic learning rate in gradient descent B. Wójcik Lukasz Maziarka Jacek Tabor ODL 34 4 0 20 Feb 2019
The Incremental Proximal Method: A Probabilistic Perspective Ömer Deniz Akyildiz Victor Elvira Joaquín Míguez 9 7 0 12 Jul 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni Stefanos Eleftheriadis J. Hensman BDL 20 85 0 24 Mar 2018
Bayesian Probabilistic Numerical Methods Jon Cockayne Chris J. Oates T. Sullivan Mark Girolami 19 164 0 13 Feb 2017
Coupling Adaptive Batch Sizes with Learning Rates Lukas Balles Javier Romero Philipp Hennig ODL 21 110 0 15 Dec 2016
An empirical analysis of the optimization of deep network loss surfaces Daniel Jiwoong Im Michael Tao K. Branson ODL 27 61 0 13 Dec 2016
Big Batch SGD: Automated Inference using Adaptive Batch Sizes Soham De A. Yadav David Jacobs Tom Goldstein ODL 16 62 0 18 Oct 2016
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation T. Bui Josiah Yan Richard Turner 16 25 0 23 May 2016
Barzilai-Borwein Step Size for Stochastic Gradient Descent Conghui Tan Shiqian Ma Yuhong Dai Yuqiu Qian 40 182 0 13 May 2016
Automatic Differentiation Variational Inference A. Kucukelbir Dustin Tran Rajesh Ranganath Andrew Gelman David M. Blei 27 708 0 02 Mar 2016