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Offline optimization is a fundamental challenge in science and engineering, where the goal is to optimize black-box functions using only offline datasets.
Nonlinear mixed-discrete structural optimization
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Mixed integer-discrete-continuous optimization by differential evolution - part 2: a practical example, 2000
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Tapabrata Ray, K. Tai, and KIN SEOW · 2001
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Liew, k.: A swarm metaphor for multiobjective design optimization. engineering optimization 34, 141-153
Tapabrata Ray and K. Liew · 2002
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P.A.N. Bosman and D. Thierens · 2003
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Cfd-based design optimization for single element rocket injector
Rajkumar Vaidyanathan, Kevin Tucker, Nilay Papila, and Wei Shyy · 2003
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Formulation of multicriterion design optimization problems for solution with scalar numerical optimization methods
Michael Parsons and Randall Scott · 2004
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Multiobjective structural optimization using a microgenetic algorithm
C A Coello Coello and G T Pulido · 2005
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Scalable Test Problems for Evolutionary Multiobjective Optimization , pp. 105–145
Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler · 2005
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The foldx web server: an online force field
Joost Schymkowitz, Jesper Borg, Francois Stricher, Robby Nys, Frederic Rousseau, and Luis Serrano · 2005
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Innovization: innovating design principles through optimization
Kalyanmoy Deb and Aravind Srinivasan · 2006
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, Aurelio Ranzato, and Fu Jie Huang · 2006
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Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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Radar waveform optimisation as a many-objective application benchmark
Evan J. Hughes · 2007
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Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization
Kalyanmoy Deb and Santosh Tiwari · 2008
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Multiobjective optimization for crash safety design of vehicles using stepwise regression model
Xingtao Liao, Qing Li, Xujing Yang, Weigang Zhang, and Wei Li · 2008
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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Distributionally robust optimization under moment uncertainty with application to data-driven problems
Erick Delage and Yinyu Ye · 2010
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The multiobjective traveling salesman problem: A survey and a new approach
Thibaut Lust and Jacques Teghem · 2010
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ChEMBL: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, and John P Overington · 2011
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Levenshtein distance technique in dictionary lookup methods: An improved approach
Rishin Haldar and Debajyoti Mukhopadhyay · 2011
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Viennarna package 2.0
Ronny Lorenz, Stephan H. Bernhart, Christian Höner zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F. Stadler, and Ivo L. Hofacker · 2011
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Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Jean-Antoine Désidéri · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Deep mutational scanning of an RRM domain of the saccharomyces cerevisiae poly(a)-binding protein
Daniel Melamed, David L Young, Caitlin E Gamble, Christina R Miller, and Stanley Fields · 2013
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Activity-enhancing mutations in an e3 ubiquitin ligase identified by high-throughput mutagenesis
Lea M. Starita, Jonathan N. Pruneda, Russell S. Lo, Douglas M. Fowler, Helen J. Kim, Joseph B. Hiatt, Jay Shendure, Peter S. Brzovic, Stanley Fields, and Rachel E. Klevit · 2013
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Virtual library of simulation experiments: Test functions and datasets, 2013
S. Surjanovic and D. Bingham · 2013
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An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints
Kalyanmoy Deb and Himanshu Jain · 2014
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: Handling constraints and extending to an adaptive approach
Himanshu Jain and Kalyanmoy Deb · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Behavior of multiobjective evolutionary algorithms on many-objective knapsack problems
Hisao Ishibuchi, Naoya Akedo, and Yusuke Nojima · 2015
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Comprehensive Sequence-Flux mapping of a levoglucosan utilization pathway in e. coli
Justin R Klesmith, John-Paul Bacik, Ryszard Michalczyk, and Timothy A Whitehead · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Inceptionism: Going deeper into neural networks, 2015
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 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, Trevor Siggers, Leila Shokri, Raluca Gordân, Nidhi Sahni, Chris Cotsapas, Tong Hao, Song Yi, Manolis Kellis, Mark J Daly, Marc Vidal, David E Hill, and Martha L Bulyk · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 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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An overview of gradient descent optimization algorithms
Sebastian Ruder · 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, Natalya S. Bogatyreva, Peter K. Vlasov, Evgeny S. Egorov, Maria D. Logacheva, Alexey S. Kondrashov, Dmitry M. Chudakov, Ekaterina V. Putintseva, Ilgar Z. Mamedov, Dan S. Tawfik, Konstantin A. Lukyanov, and Fyodor A. Kondrashov · 2016
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Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing · 2016
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The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank P. DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram K. Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack Jr., Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, and Jeffrey J. Gray · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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A framework for exhaustively mapping functional missense variants
Jochen Weile, Song Sun, Atina G Cote, Jennifer Knapp, Marta Verby, Joseph C Mellor, Yingzhou Wu, Carles Pons, Cassandra Wong, Natascha van Lieshout, Fan Yang, Murat Tasan, Guihong Tan, Shan Yang, Douglas M Fowler, Robert Nussbaum, Jesse D Bloom, Marc Vidal, David E Hill, Patrick Aloy, and Frederick P Roth · 2017
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Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded
Emily E Wrenbeck, Laura R Azouz, and Timothy A Whitehead · 2017
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Barret Zoph and Quoc V. Le · 2017
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A tutorial on bayesian optimization
Peter I Frazier · 2018
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Neural architecture optimization
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Conditioning by adaptive sampling for robust design
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