Variational Search Distributions

10 September 2024

Edwin V. Bonilla

Papers citing "Variational Search Distributions"

50 / 51 papers shown

Title
Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking Shikha Surana Nathan Grinsztajn Timothy Atkinson Paul Duckworth Thomas D. Barrett 94 4 0 24 Feb 2025
Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms Samuel Stanton R. Alberstein Nathan C. Frey Andrew Watkins Kyunghyun Cho 79 5 0 28 Jun 2024
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences Miguel González Duque Richard Michael Simon Bartels Yevgen Zainchkovskyy Søren Hauberg Wouter Boomsma 54 5 0 07 Jun 2024
A Continuous Relaxation for Discrete Bayesian Optimization Richard Michael Simon Bartels Miguel González Duque Yevgen Zainchkovskyy J. Frellsen Søren Hauberg Wouter Boomsma 20 4 0 26 Apr 2024
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds Shion Takeno Yu Inatsu Masayuki Karasuyama Ichiro Takeuchi 59 6 0 07 Nov 2023
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery Yashas Annadani Nick Pawlowski Joel Jennings Stefan Bauer Cheng Zhang Wenbo Gong CML BDL 54 19 0 26 Jul 2023
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation Fengxue Zhang Jialin Song James Bowden Alexander Ladd Yisong Yue Thomas Desautels Yuxin Chen 52 7 0 25 Jul 2023
Improving Protein Optimization with Smoothed Fitness Landscapes Andrew Kirjner Jason Yim Raman Samusevich Shahar Bracha Tommi Jaakkola Regina Barzilay Ila Fiete 42 17 0 02 Jul 2023
Kernelized Reinforcement Learning with Order Optimal Regret Bounds Sattar Vakili Julia Olkhovskaya 47 9 0 13 Jun 2023
Protein Design with Guided Discrete Diffusion Nate Gruver Samuel Stanton Nathan C. Frey Tim G. J. Rudner I. Hotzel J. Lafrance-Vanasse A. Rajpal Kyunghyun Cho A. Wilson DiffM 66 113 0 31 May 2023
Characterizing the Spectrum of the NTK via a Power Series Expansion Michael Murray Hui Jin Benjamin Bowman Guido Montúfar 61 11 0 15 Nov 2022
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization Sam Daulton Xingchen Wan David Eriksson Maximilian Balandat Michael A. Osborne E. Bakshy 60 38 0 18 Oct 2022
Batch Bayesian optimisation via density-ratio estimation with guarantees Rafael Oliveira Louis C. Tiao Fabio Ramos 70 7 0 22 Sep 2022
Formal Algorithms for Transformers Mary Phuong Marcus Hutter 45 73 0 19 Jul 2022
A General Recipe for Likelihood-free Bayesian Optimization Jiaming Song Lantao Yu Willie Neiswanger Stefano Ermon 55 24 0 27 Jun 2022
Diffusion-LM Improves Controllable Text Generation Xiang Lisa Li John Thickstun Ishaan Gulrajani Percy Liang Tatsunori B. Hashimoto AI4CE 216 818 0 27 May 2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders Samuel Stanton Wesley J. Maddox Nate Gruver Phillip M. Maffettone E. Delaney Peyton Greenside A. Wilson BDL 53 96 0 23 Mar 2022
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization Brandon Trabucco Xinyang Geng Aviral Kumar Sergey Levine OffRL 69 100 0 17 Feb 2022
A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models Hanqing Zhang Haolin Song Shaoyu Li Ming Zhou Dawei Song 71 220 0 14 Jan 2022
Conservative Objective Models for Effective Offline Model-Based Optimization Brandon Trabucco Aviral Kumar Xinyang Geng Sergey Levine OffRL 65 92 0 14 Jul 2021
BORE: Bayesian Optimization by Density-Ratio Estimation Louis C. Tiao Aaron Klein Matthias Seeger Edwin V. Bonilla Cédric Archambeau F. Ramos 58 28 0 17 Feb 2021
Non-asymptotic approximations of neural networks by Gaussian processes Ronen Eldan Dan Mikulincer T. Schramm 99 23 0 17 Feb 2021
On the Origin of Implicit Regularization in Stochastic Gradient Descent Samuel L. Smith Benoit Dherin David Barrett Soham De MLT 32 204 0 28 Jan 2021
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design Sam Sinai Richard Wang Alexander Whatley Stewart Slocum Elina Locane Eric D. Kelsic 57 79 0 05 Oct 2020
On the linearity of large non-linear models: when and why the tangent kernel is constant Chaoyue Liu Libin Zhu M. Belkin 104 141 0 02 Oct 2020
Implicit Gradient Regularization David Barrett Benoit Dherin 67 151 0 23 Sep 2020
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS Lin Chen Sheng Xu 115 94 0 22 Sep 2020
On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili Kia Khezeli Victor Picheny GP 52 133 0 15 Sep 2020
Infinite attention: NNGP and NTK for deep attention networks Jiri Hron Yasaman Bahri Jascha Narain Sohl-Dickstein Roman Novak 24 114 0 18 Jun 2020
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining Austin Tripp Erik A. Daxberger José Miguel Hernández-Lobato MedIm 56 139 0 16 Jun 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 298 10,591 0 17 Feb 2020
Plug and Play Language Models: A Simple Approach to Controlled Text Generation Sumanth Dathathri Andrea Madotto Janice Lan Jane Hung Eric Frank Piero Molino J. Yosinski Rosanne Liu KELM 121 968 0 04 Dec 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 67 408 0 25 Jun 2019
A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization David H. Brookes A. Busia Clara Fannjiang Kevin Patrick Murphy Jennifer Listgarten 47 22 0 24 May 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 176 1,097 0 18 Feb 2019
Conditioning by adaptive sampling for robust design David H. Brookes Hahnbeom Park Jennifer Listgarten 52 199 0 29 Jan 2019
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon Yoshua Bengio Andrea Lodi Antoine Prouvost 153 1,377 0 15 Nov 2018
Design by adaptive sampling David H. Brookes Jennifer Listgarten TPM 71 66 0 08 Oct 2018
Stochastic Variational Optimization Thomas Bird Julius Kunze David Barber DRL 34 14 0 13 Sep 2018
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces Mattes Mollenhauer Ingmar Schuster Stefan Klus Christof Schütte 38 19 0 24 Jul 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 232 3,191 0 20 Jun 2018
The reparameterization trick for acquisition functions James T. Wilson Riccardo Moriconi Frank Hutter M. Deisenroth 54 79 0 01 Dec 2017
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 127 914 0 27 Oct 2017
Streaming kernel regression with provably adaptive mean, variance, and regularization A. Durand Odalric-Ambrym Maillard Joelle Pineau 37 43 0 02 Aug 2017
On Kernelized Multi-armed Bandits Sayak Ray Chowdhury Aditya Gopalan 109 460 0 03 Apr 2017
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic Jonathan Scarlett Andreas Krause Volkan Cevher 76 91 0 24 Oct 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 147 2,921 0 07 Oct 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 63 1,091 0 16 Aug 2016
Discovering Valuable Items from Massive Data Hastagiri P. Vanchinathan Andreas Marfurt C. Robelin Donald Kossmann Andreas Krause 50 36 0 02 Jun 2015
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 101 1,165 0 31 Dec 2013