Practical Bayesian Optimization of Machine Learning Algorithms

13 June 2012

Papers citing "Practical Bayesian Optimization of Machine Learning Algorithms"

50 / 2,248 papers shown

Title
Online Hyper-parameter Learning for Auto-Augmentation Strategy Chen Lin Minghao Guo Chuming Li Yuan Xin Wei Wu Dahua Lin Wanli Ouyang Junjie Yan ODL 21 83 0 17 May 2019
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment Chen Huang Shuangfei Zhai Walter A. Talbott Miguel Angel Bautista Shi Sun Carlos Guestrin J. Susskind 29 75 0 15 May 2019
Trajectory-Based Off-Policy Deep Reinforcement Learning Andreas Doerr Michael Volpp Marc Toussaint Sebastian Trimpe Christian Daniel OffRL 29 2 0 14 May 2019
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules Daniel Ho Eric Liang Ion Stoica Pieter Abbeel Xi Chen 35 397 0 14 May 2019
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization Aaron Klein Frank Hutter 15 91 0 13 May 2019
Bayesian Optimized Continual Learning with Attention Mechanism Ju Xu Jin Ma Zhanxing Zhu CLL BDL 16 6 0 10 May 2019
Exploring the Hyperparameter Landscape of Adversarial Robustness Evelyn Duesterwald Anupama Murthi Ganesh Venkataraman M. Sinn Deepak Vijaykeerthy AAML 16 7 0 09 May 2019
Knowing The What But Not The Where in Bayesian Optimization Vu Nguyen Michael A. Osborne 31 37 0 07 May 2019
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks Chunxu Zhang Jiaxu Cui Bo Yang 29 3 0 06 May 2019
Fast and Reliable Architecture Selection for Convolutional Neural Networks Lukas Hahn L. Roese-Koerner Klaus Friedrichs A. Kummert 17 0 0 06 May 2019
Interpretability with Accurate Small Models Abhishek Ghose Balaraman Ravindran 23 1 0 04 May 2019
A Survey on Neural Architecture Search Martin Wistuba Ambrish Rawat Tejaswini Pedapati AI4CE 19 258 0 04 May 2019
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations Marko Jarvenpaa Michael U. Gutmann Aki Vehtari Pekka Marttinen 24 40 0 03 May 2019
An ADMM Based Framework for AutoML Pipeline Configuration Sijia Liu Parikshit Ram Deepak Vijaykeerthy Djallel Bouneffouf Gregory Bramble Horst Samulowitz Dakuo Wang A. Conn Alexander G. Gray 29 75 0 01 May 2019
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations Xiao Zhang Rui Zhao Yu Qiao Xiaogang Wang Hongsheng Li CVBM 32 214 0 01 May 2019
Benchmark and Survey of Automated Machine Learning Frameworks Marc-André Zöller Marco F. Huber 25 86 0 26 Apr 2019
A Novel Orthogonal Direction Mesh Adaptive Direct Search Approach for SVM Hyperparameter Tuning Alexandre Reeberg de Mello Jonathan de Matos M. Stemmer A. Britto Alessandro Lameiras Koerich 17 5 0 26 Apr 2019
Bayesian Search for Robust Optima Nicholas D. Sanders Richard Everson J. Fieldsend Alma A. M. Rahat 25 3 0 25 Apr 2019
Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet) Ali Habibnia E. Maasoumi 21 6 0 25 Apr 2019
Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity Minsu Cho Chinmay Hegde 27 11 0 24 Apr 2019
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series Feng Yin Lishuo Pan Xinwei He Tianshi Chen Sergios Theodoridis Zhi-Quan Zhi-Quan Luo AI4TS 27 25 0 21 Apr 2019
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning Yaoyao Liu Bernt Schiele Qianru Sun BDL 40 128 0 17 Apr 2019
Effective Estimation of Deep Generative Language Models Tom Pelsmaeker Wilker Aziz BDL 29 27 0 17 Apr 2019
On the Performance of Differential Evolution for Hyperparameter Tuning Mischa Schmidt Shahd Safarani J. Gastinger Tobias Jacobs S. Nicolas A. Schulke 17 19 0 15 Apr 2019
Shakeout: A New Approach to Regularized Deep Neural Network Training Guoliang Kang Jun Yu Li Dacheng Tao 28 59 0 13 Apr 2019
Least Squares Auto-Tuning Shane T. Barratt Stephen P. Boyd MoMe 24 23 0 10 Apr 2019
Data adaptation in HANDY economy-ideology model Marcin Sendera 17 3 0 08 Apr 2019
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning Han Guo Ramakanth Pasunuru Joey Tianyi Zhou 25 47 0 08 Apr 2019
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization Michael Volpp Lukas P. Frohlich Kirsten Fischer Andreas Doerr Stefan Falkner Frank Hutter Christian Daniel 26 84 0 04 Apr 2019
Adaptive Bayesian Linear Regression for Automated Machine Learning Weilin Zhou F. Precioso 20 5 0 01 Apr 2019
An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression Jean Feng N. Simon 19 1 0 28 Mar 2019
Meta-Learning surrogate models for sequential decision making Alexandre Galashov Jonathan Richard Schwarz Hyunjik Kim M. Garnelo D. Saxton Pushmeet Kohli S. M. Ali Eslami Yee Whye Teh BDL OffRL 30 26 0 28 Mar 2019
Deep Demosaicing for Edge Implementation R. Ramakrishnan Shangling Jui V. Nia 38 5 0 26 Mar 2019
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search Linnan Wang Yiyang Zhao Yuu Jinnai Yuandong Tian Rodrigo Fonseca BDL 25 95 0 26 Mar 2019
Sampling Acquisition Functions for Batch Bayesian Optimization Alessandro De Palma Celestine Mendler-Dünner Thomas Parnell Andreea Anghel H. Pozidis 39 13 0 22 Mar 2019
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints Nimish Awalgaonkar Ilias Bilionis Xiaoqi Liu P. Karava Athanasios Tzempelikos AI4TS AI4CE 38 2 0 21 Mar 2019
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 24 226 0 19 Mar 2019
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly Kirthevasan Kandasamy Karun Raju Vysyaraju Willie Neiswanger Biswajit Paria Christopher R. Collins J. Schneider Barnabás Póczós Eric Xing 34 174 0 15 Mar 2019
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret Daniele Calandriello Luigi Carratino A. Lazaric Michal Valko Lorenzo Rosasco GP 8 0 0 13 Mar 2019
DeepOBS: A Deep Learning Optimizer Benchmark Suite Frank Schneider Lukas Balles Philipp Hennig ODL 41 71 0 13 Mar 2019
Variational Bayesian Optimal Experimental Design Adam Foster M. Jankowiak Eli Bingham Paul Horsfall Yee Whye Teh Tom Rainforth Noah D. Goodman 44 133 0 13 Mar 2019
Continual Learning in Practice Tom Diethe Tom Borchert Eno Thereska Borja Balle Neil D. Lawrence CLL 14 75 0 12 Mar 2019
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning Liam Li Evan R. Sparks Kevin G. Jamieson Ameet Talwalkar 13 9 0 12 Mar 2019
Efficient Optimization of Echo State Networks for Time Series Datasets Jacob Reinier Maat N. Gianniotis P. Protopapas AI4TS 11 20 0 12 Mar 2019
Financial Applications of Gaussian Processes and Bayesian Optimization Joan Gonzalvez Edmond Lezmi T. Roncalli Jiali Xu 20 54 0 12 Mar 2019
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning Jian Wu Saul Toscano-Palmerin P. Frazier A. Wilson 25 130 0 12 Mar 2019
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions M. Mackay Paul Vicol Jonathan Lorraine David Duvenaud Roger C. Grosse 27 164 0 07 Mar 2019
Real-Time Boiler Control Optimization with Machine Learning Yukun Ding Yiyu Shi AI4CE 38 1 0 07 Mar 2019
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference Kelvin Hsu F. Ramos 30 12 0 03 Mar 2019
High-dimensional Bayesian optimization using low-dimensional feature spaces Riccardo Moriconi M. Deisenroth K. S. S. Kumar 14 11 0 27 Feb 2019