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We develop VSD, a method for conditioning a generative model of discrete, combinatorial designs on a rare desired class by efficiently evaluating a black-box (e.g.
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Matrix algebra useful for statistics
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Gradient-based learning applied to document recognition
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The cross-entropy method for combinatorial and continuous optimization
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Estimation of distribution algorithms: A new tool for evolutionary computation , volume 2
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Active learning for identifying function threshold boundaries
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Online Learning for Linearly Parametrized Control Problems
Yasin Abbasi-Yadkori · 2012
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Bayesian optimal active search and surveying
Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff G. Schneider, and Richard P. Mann · 2012
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Concentration inequalities: A Nonasymptotic Theory of Independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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Active learning for level set estimation
Alkis Gotovos, Nathalie Casati, Gregory Hitz, and Andreas Krause · 2013
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Optimization by variational bounding
Joe Staines and David Barber · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber · 2014
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Discovering valuable items from massive data
Hastagiri P Vanchinathan, Andreas Marfurt, Charles-Antoine Robelin, Donald Kossmann, and Andreas Krause · 2015
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Survey of variation in human transcription factors reveals prevalent dna binding changes
Luis A Barrera, Anastasia Vedenko, Jesse V Kurland, Julia M Rogers, Stephen S Gisselbrecht, Elizabeth J Rossin, Jaie Woodard, Luca Mariani, Kian Hong Kock, Sachi Inukai, et al · 2016
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Truncated variance reduction: A unified approach to bayesian optimization and level-set estimation
Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, and Volkan Cevher · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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Self-normalization techniques for streaming confident regression
Odalric-Ambrym Maillard · 2016
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Local fitness landscape of the green fluorescent protein
Karen S Sarkisyan, Dmitry A Bolotin, Margarita V Meer, Dinara R Usmanova, Alexander S Mishin, George V Sharonov, Dmitry N Ivankov, Nina G Bozhanova, Mikhail S Baranov, Onuralp Soylemez, et al · 2016
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On Kernelized Multi-armed Bandits
Sayak Ray Chowdhury and Aditya Gopalan · 2017
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Efficient nonmyopic active search
Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, and Roman Garnett · 2017
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Plug & play generative networks: Conditional iterative generation of images in latent space
Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
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The reparameterization trick for acquisition functions
James T Wilson, Riccardo Moriconi, Frank Hutter, and Marc Peter Deisenroth · 2017
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Deep neural tangent kernel and laplace kernel have the same RKHS
Lin Chen and Sheng Xu · 2021
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Non-asymptotic approximations of neural networks by Gaussian processes
Ronen Eldan, Dan Mikulincer, and Tselil Schramm · 2021
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On the origin of implicit regularization in stochastic gradient descent
Samuel L. Smith, Benoit Dherin, David G. T. Barrett, and Soham De · 2021
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Bore: Bayesian optimization by density-ratio estimation
Louis C Tiao, Aaron Klein, Matthias W Seeger, Edwin V Bonilla, Cedric Archambeau, and Fabio Ramos · 2021
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On information gain and regret bounds in Gaussian process bandits
Sattar Vakili, Kia Khezeli, and Victor Picheny · 2021
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Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization
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Thomas Bird, Julius Kunze, and David Barber · 2018
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David H Brookes and Jennifer Listgarten · 2018
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Streaming kernel regression with provably adaptive mean, variance, and regularization
Audrey Durand, Odalric-Ambrym Maillard, and Joelle Pineau · 2018
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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, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Efficient high dimensional Bayesian optimization with additivity and quadrature Fourier features
Mojmír Mutný and Andreas Krause · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suria Gunasekar, and Nathan Srebro · 2018
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Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A Osborne, and Eytan Bakshy · 2022
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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
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Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto · 2022
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Batch bayesian optimisation via density-ratio estimation with guarantees
Rafael Oliveira, Louis Tiao, and Fabio T Ramos · 2022
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Formal algorithms for transformers
Mary Phuong and Marcus Hutter · 2022
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Proximal exploration for model-guided protein sequence design
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A general recipe for likelihood-free bayesian optimization
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Accelerating Bayesian optimization for biological sequence design with denoising autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson · 2022
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Design-bench: Benchmarks for data-driven offline model-based optimization
Brandon Trabucco, Xinyang Geng, Aviral Kumar, and Sergey Levine · 2022
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BayesDAG: Gradient-based posterior inference for causal discovery
Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, and Wenbo Gong · 2023
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Bayesian optimization
Roman Garnett · 2023
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Protein design with guided discrete diffusion
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Characterizing the spectrum of the NTK via a power series expansion
Michael Murray, Hui Jin, Benjamin Bowman, and Guido Montufar · 2023
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A rugged yet easily navigable fitness landscape
Andrei Papkou, Lucia Garcia-Pastor, José Antonio Escudero, and Andreas Wagner · 2023
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Kernelized reinforcement learning with order optimal regret bounds
Sattar Vakili and Julia Olkhovskaya · 2023
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Notes for Nonparametric Statistics
E. García-Portugués · 2024
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A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
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poli: a libary of discrete sequence objectives, January 2024
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A combinatorially complete epistatic fitness landscape in an enzyme active site
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A continuous relaxation for discrete Bayesian optimization
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Closed-form test functions for biophysical sequence optimization algorithms
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