Scalable Bayesian Optimization Using Deep Neural Networks

19 February 2015

Md. Mostofa Ali Patwary

Papers citing "Scalable Bayesian Optimization Using Deep Neural Networks"

50 / 194 papers shown

Title
A General Recipe for Likelihood-free Bayesian Optimization Jiaming Song Lantao Yu Willie Neiswanger Stefano Ermon 38 23 0 27 Jun 2022
Joint Entropy Search for Maximally-Informed Bayesian Optimization Carl Hvarfner Frank Hutter Luigi Nardi 44 36 0 09 Jun 2022
Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation Qian Chen Zilong Wang Jiawei Chen Haonan Yan Xiaodong Lin FedML 10 17 0 08 Jun 2022
Recent Advances in Bayesian Optimization Xilu Wang Yaochu Jin Sebastian Schmitt Markus Olhofer 40 200 0 07 Jun 2022
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning Yang Li Yu Shen Huaijun Jiang Wentao Zhang Zhi-Xin Yang Ce Zhang Bin Cui 38 15 0 06 Jun 2022
Transfer Learning based Search Space Design for Hyperparameter Tuning Yang Li Yu Shen Huaijun Jiang Tianyi Bai Wentao Zhang Ce Zhang Bin Cui 38 13 0 06 Jun 2022
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates A. Maraval Matthieu Zimmer Antoine Grosnit Rasul Tutunov Jun Wang H. Ammar 35 2 0 27 May 2022
Interpolating Compressed Parameter Subspaces Siddhartha Datta N. Shadbolt 37 5 0 19 May 2022
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution Lixue Cheng Ziyi Yang Chang-Yu Hsieh Ben Liao Shengyu Zhang 30 6 0 19 May 2022
A model aggregation approach for high-dimensional large-scale optimization Haowei Wang Ercong Zhang Szu Hui Ng Giulia Pedrielli 22 1 0 16 May 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 26 48 0 01 May 2022
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs Zehao Dong Muhan Zhang Fuhai Li Yixin Chen CML GNN 33 17 0 19 Mar 2022
Learning Where To Look -- Generative NAS is Surprisingly Efficient Jovita Lukasik Steffen Jung M. Keuper 29 15 0 16 Mar 2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time Mitchell Wortsman Gabriel Ilharco S. Gadre Rebecca Roelofs Raphael Gontijo-Lopes ... Hongseok Namkoong Ali Farhadi Y. Carmon Simon Kornblith Ludwig Schmidt MoMe 56 924 1 10 Mar 2022
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer Greg Yang J. E. Hu Igor Babuschkin Szymon Sidor Xiaodong Liu David Farhi Nick Ryder J. Pachocki Weizhu Chen Jianfeng Gao 28 149 0 07 Mar 2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors Yibo Yang Georgios Kissas P. Perdikaris BDL UQCV 37 40 0 06 Mar 2022
Amortized Proximal Optimization Juhan Bae Paul Vicol Jeff Z. HaoChen Roger C. Grosse ODL 29 14 0 28 Feb 2022
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization Brandon Trabucco Xinyang Geng Aviral Kumar Sergey Levine OffRL 34 95 0 17 Feb 2022
Maximum Likelihood Uncertainty Estimation: Robustness to Outliers Deebul Nair Nico Hochgeschwender Miguel A. Olivares-Mendez OOD 32 7 0 03 Feb 2022
Posterior temperature optimized Bayesian models for inverse problems in medical imaging M. Laves Malte Tolle Alexander Schlaefer Sandy Engelhardt 37 10 0 02 Feb 2022
Automotive Parts Assessment: Applying Real-time Instance-Segmentation Models to Identify Vehicle Parts S. Yusuf Abdulmalik Aldawsari R. Souissi 14 3 0 02 Feb 2022
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework Ziyi Huang Henry Lam A. Meisami Haofeng Zhang 36 4 0 31 Jan 2022
Top-K Ranking Deep Contextual Bandits for Information Selection Systems Jade Freeman Michael Rawson 35 2 0 28 Jan 2022
Thinking inside the box: A tutorial on grey-box Bayesian optimization Raul Astudillo P. Frazier 30 35 0 02 Jan 2022
Online Calibrated and Conformal Prediction Improves Bayesian Optimization Shachi Deshpande Charles Marx Volodymyr Kuleshov 13 7 0 08 Dec 2021
Differentiable Projection for Constrained Deep Learning Dou Huang Haoran Zhang Xuan Song Ryosuke Shibasaki 35 2 0 21 Nov 2021
Merging Models with Fisher-Weighted Averaging Michael Matena Colin Raffel FedML MoMe 50 354 0 18 Nov 2021
Uncertainty Quantification in Neural Differential Equations Olga Graf P. Flores P. Protopapas K. Pichara UQCV AI4CE 37 7 0 08 Nov 2021
Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles Alexander Thebelt Calvin Tsay Robert M. Lee Nathan Sudermann-Merx David Walz T. Tranter Ruth Misener AI4CE 9 30 0 04 Nov 2021
RoMA: Robust Model Adaptation for Offline Model-based Optimization Sihyun Yu Sungsoo Ahn Le Song Jinwoo Shin OffRL 32 31 0 27 Oct 2021
Data-Driven Offline Optimization For Architecting Hardware Accelerators Aviral Kumar Amir Yazdanbakhsh Milad Hashemi Kevin Swersky Sergey Levine 29 36 0 20 Oct 2021
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation Ross M. Clarke E. T. Oldewage José Miguel Hernández-Lobato 31 9 0 20 Oct 2021
Improving Hyperparameter Optimization by Planning Ahead H. Jomaa Jonas K. Falkner Lars Schmidt-Thieme 22 0 0 15 Oct 2021
Marginally calibrated response distributions for end-to-end learning in autonomous driving Clara Hoffmann Nadja Klein 13 2 0 03 Oct 2021
SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation Aythami Morales Julian Fierrez A. Acien Ruben Tolosana Ignacio Serna 26 20 0 02 Sep 2021
Sparse Bayesian Deep Learning for Dynamic System Identification Hongpeng Zhou Chahine Ibrahim W. Zheng Wei Pan BDL 23 25 0 27 Jul 2021
Conservative Objective Models for Effective Offline Model-Based Optimization Brandon Trabucco Aviral Kumar Xinyang Geng Sergey Levine OffRL 44 86 0 14 Jul 2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 85 455 0 13 Jul 2021
Laplace Redux -- Effortless Bayesian Deep Learning Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 60 290 0 28 Jun 2021
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML Sebastian Pineda Arango H. Jomaa Martin Wistuba Josif Grabocka 24 55 0 11 Jun 2021
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data Kourosh Hakhamaneshi Pieter Abbeel Vladimir M. Stojanović Aditya Grover 27 10 0 02 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 35 124 0 14 May 2021
Discovering Diverse Athletic Jumping Strategies Zhiqi Yin Zeshi Yang M. van de Panne KangKang Yin 42 46 0 02 May 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 148 17 0 23 Apr 2021
Fast Design Space Exploration of Nonlinear Systems: Part I S. Narain Emily Mak Dana Chee Brendan Englot K. Pochiraju N. Jha Karthik Narayan 25 5 0 05 Apr 2021
How Powerful are Performance Predictors in Neural Architecture Search? Colin White Arber Zela Binxin Ru Yang Liu Frank Hutter 22 126 0 02 Apr 2021
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty Chao Qu Wenxin Liu Camillo J Taylor UQCV BDL 22 31 0 29 Mar 2021
CATE: Computation-aware Neural Architecture Encoding with Transformers Shen Yan Kaiqiang Song Z. Feng Mi Zhang 22 24 0 14 Feb 2021
Explaining Inference Queries with Bayesian Optimization Brandon Lockhart Jinglin Peng Weiyuan Wu Jiannan Wang Eugene Wu 21 7 0 10 Feb 2021
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models Jeroen van Hoof Joaquin Vanschoren BDL 38 9 0 06 Jan 2021