Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space

16 July 2024

Papers citing "Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space"

22 / 22 papers shown

Title
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance) Jan Stanczuk Christian Etmann L. Kreusser Carola-Bibiane Schönlieb GAN 53 48 0 02 Mar 2021
On Linear Optimization over Wasserstein Balls Man-Chung Yue Daniel Kuhn W. Wiesemann 59 52 0 15 Apr 2020
High Dimensional Bayesian Optimization via Supervised Dimension Reduction Miao Zhang Huiqi Li Steven W. Su 42 44 0 21 Jul 2019
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement Szu-Wei Fu Chien-Feng Liao Yu Tsao Shou-De Lin 55 331 0 13 May 2019
Bayesian Optimization using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab GP 74 70 0 07 May 2019
Conditioning by adaptive sampling for robust design David H. Brookes Hahnbeom Park Jennifer Listgarten 59 199 0 29 Jan 2019
A Tutorial on Bayesian Optimization P. Frazier GP 109 1,787 0 08 Jul 2018
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks M. Mustafa Deborah Bard W. Bhimji Z. Lukić Rami Al-Rfou J. Kratochvil GAN 56 124 0 07 Jun 2017
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters Michela Paganini Luke de Oliveira Benjamin Nachman AI4CE 50 222 0 05 May 2017
Improved Training of Wasserstein GANs Ishaan Gulrajani Faruk Ahmed Martín Arjovsky Vincent Dumoulin Aaron Courville GAN 205 9,548 0 31 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 831 5,821 0 05 Dec 2016
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 382 441 0 01 Dec 2016
A Differentiable Physics Engine for Deep Learning in Robotics Jonas Degrave Michiel Hermans J. Dambre Francis Wyffels PINN AI4CE 64 229 0 05 Nov 2016
Automatic chemical design using a data-driven continuous representation of molecules Rafael Gómez-Bombarelli Jennifer N. Wei David Duvenaud José Miguel Hernández-Lobato Benjamín Sánchez-Lengeling Dennis Sheberla J. Aguilera-Iparraguirre Timothy D. Hirzel Ryan P. Adams Alán Aspuru-Guzik 3DV 161 2,932 0 07 Oct 2016
Quantifying Distributional Model Risk via Optimal Transport Jose H. Blanchet Karthyek Murthy 78 425 0 05 Apr 2016
Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations Peyman Mohajerin Esfahani Daniel Kuhn 77 1,661 0 19 May 2015
Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek Oren Rippel Kevin Swersky Ryan Kiros N. Satish N. Sundaram Md. Mostofa Ali Patwary P. Prabhat Ryan P. Adams BDL UQCV 95 1,043 0 19 Feb 2015
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models Michael U. Gutmann J. Corander 152 287 0 14 Jan 2015
Active Learning of Linear Embeddings for Gaussian Processes Roman Garnett Michael A. Osborne Philipp Hennig GP 96 92 0 24 Oct 2013
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 127 1,182 0 02 Nov 2012
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 353 7,942 0 13 Jun 2012
Convergence rates of efficient global optimization algorithms Adam D. Bull 119 641 0 18 Jan 2011