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There have been many recent advances in the development of machine learning approaches for active decision making and optimization. These advances have occurred in seemingly disparate communities, each referring to the problem using different terminology: Bayesian optimization, experimental design, bandits, active sensing, automatic algorithm configuration, personalized recommender systems, etc. Recently, significant progress has been made in improving the methodologies used to solve high-dimensional problems and applying these techniques to challenging optimization tasks with limited and noisy feedback. This progress is particularly apparent in areas that seek to automate machine learning algorithms and website analytics. Applying these approaches to increasingly harder problems has also revealed new challenges and opened up many interesting research directions both in developing theory and in practical application.
Following on last year's NIPS workshop, "Bayesian Optimization & Decision Making", the goal of this workshop is to bring together researchers and practitioners from these diverse subject areas to facilitate cross-fertilization by discussing challenges, findings, and sharing data. This year we plan to focus on the intersection of "Theory and Practice". Specifically, we would like to carefully examine the types of problems where Bayesian optimization performs well and ask what theoretical guarantees can be made to explain this performance? Where is the theory lacking? What are the most pressing challenges? In what way can this empirical performance be used to guide the development of new theory?
Author Information
Matthew Hoffman (DeepMind)
Jasper Snoek (Google Brain)
Nando de Freitas (University of Oxford)
Michael A Osborne (U Oxford)
Ryan Adams (Princeton University)
Sebastien Bubeck (MSR)
Philipp Hennig (University of Tübingen and MPI IS Tübingen)
Remi Munos (Google DeepMind)
Andreas Krause (ETH Zurich)
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Scott Linderman · Matthew Johnson · Ryan Adams -
2014 Workshop: NIPS’14 Workshop on Crowdsourcing and Machine Learning »
David Parkes · Denny Zhou · Chien-Ju Ho · Nihar Bhadresh Shah · Adish Singla · Jared Heyman · Edwin Simpson · Andreas Krause · Rafael Frongillo · Jennifer Wortman Vaughan · Panagiotis Papadimitriou · Damien Peters -
2014 Workshop: Discrete Optimization in Machine Learning »
Jeffrey A Bilmes · Andreas Krause · Stefanie Jegelka · S Thomas McCormick · Sebastian Nowozin · Yaron Singer · Dhruv Batra · Volkan Cevher -
2014 Workshop: Bayesian Optimization in Academia and Industry »
Zoubin Ghahramani · Ryan Adams · Matthew Hoffman · Kevin Swersky · Jasper Snoek -
2014 Poster: Incremental Local Gaussian Regression »
Franziska Meier · Philipp Hennig · Stefan Schaal -
2014 Poster: Probabilistic ODE Solvers with Runge-Kutta Means »
Michael Schober · David Duvenaud · Philipp Hennig -
2014 Poster: Active Regression by Stratification »
Sivan Sabato · Remi Munos -
2014 Poster: Best-Arm Identification in Linear Bandits »
Marta Soare · Alessandro Lazaric · Remi Munos -
2014 Poster: Bounded Regret for Finite-Armed Structured Bandits »
Tor Lattimore · Remi Munos -
2014 Poster: Efficient Sampling for Learning Sparse Additive Models in High Dimensions »
Hemant Tyagi · Bernd Gärtner · Andreas Krause -
2014 Poster: Efficient learning by implicit exploration in bandit problems with side observations »
Tomáš Kocák · Gergely Neu · Michal Valko · Remi Munos -
2014 Poster: From MAP to Marginals: Variational Inference in Bayesian Submodular Models »
Josip Djolonga · Andreas Krause -
2014 Poster: Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature »
Tom Gunter · Michael A Osborne · Roman Garnett · Philipp Hennig · Stephen J Roberts -
2014 Oral: Probabilistic ODE Solvers with Runge-Kutta Means »
Michael Schober · David Duvenaud · Philipp Hennig -
2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Efficient Partial Monitoring with Prior Information »
Hastagiri P Vanchinathan · Gábor Bartók · Andreas Krause -
2014 Poster: Optimistic Planning in Markov Decision Processes Using a Generative Model »
Balázs Szörényi · Gunnar Kedenburg · Remi Munos -
2014 Spotlight: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: A framework for studying synaptic plasticity with neural spike train data »
Scott Linderman · Christopher H Stock · Ryan Adams -
2014 Poster: Distributed Parameter Estimation in Probabilistic Graphical Models »
Yariv D Mizrahi · Misha Denil · Nando de Freitas -
2013 Workshop: Machine Learning for Sustainability »
Edwin Bonilla · Thomas Dietterich · Theodoros Damoulas · Andreas Krause · Daniel Sheldon · Iadine Chades · J. Zico Kolter · Bistra Dilkina · Carla Gomes · Hugo P Simao -
2013 Workshop: Learning Faster From Easy Data »
Peter Grünwald · Wouter M Koolen · Sasha Rakhlin · Nati Srebro · Alekh Agarwal · Karthik Sridharan · Tim van Erven · Sebastien Bubeck -
2013 Workshop: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih -
2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan -
2013 Poster: High-Dimensional Gaussian Process Bandits »
Josip Djolonga · Andreas Krause · Volkan Cevher -
2013 Poster: The Randomized Dependence Coefficient »
David Lopez-Paz · Philipp Hennig · Bernhard Schölkopf -
2013 Poster: Prior-free and prior-dependent regret bounds for Thompson Sampling »
Sebastien Bubeck · Che-Yu Liu -
2013 Poster: Thompson Sampling for 1-Dimensional Exponential Family Bandits »
Nathaniel Korda · Emilie Kaufmann · Remi Munos -
2013 Poster: Multi-Task Bayesian Optimization »
Kevin Swersky · Jasper Snoek · Ryan Adams -
2013 Poster: Message Passing Inference with Chemical Reaction Networks »
Nils E Napp · Ryan Adams -
2013 Oral: Message Passing Inference with Chemical Reaction Networks »
Nils E Napp · Ryan Adams -
2013 Poster: A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data »
Jasper Snoek · Richard Zemel · Ryan Adams -
2013 Poster: Distributed Submodular Maximization: Identifying Representative Elements in Massive Data »
Baharan Mirzasoleiman · Amin Karbasi · Rik Sarkar · Andreas Krause -
2013 Poster: Contrastive Learning Using Spectral Methods »
James Y Zou · Daniel Hsu · David Parkes · Ryan Adams -
2013 Poster: Aggregating Optimistic Planning Trees for Solving Markov Decision Processes »
Gunnar Kedenburg · Raphael Fonteneau · Remi Munos -
2012 Workshop: Probabilistic Numerics »
Philipp Hennig · John P Cunningham · Michael A Osborne -
2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
Stefanie Jegelka · Andreas Krause · Jeffrey A Bilmes · Pradeep Ravikumar -
2012 Poster: Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button »
Joan Fruitet · Alexandra Carpentier · Remi Munos · Maureen Clerc -
2012 Poster: Bayesian n-Choose-k Models for Classification and Ranking »
Kevin Swersky · Danny Tarlow · Richard Zemel · Ryan Adams · Brendan J Frey -
2012 Poster: Priors for Diversity in Generative Latent Variable Models »
James Y Zou · Ryan Adams -
2012 Poster: Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions »
Alexandra Carpentier · Remi Munos -
2012 Poster: Risk-Aversion in Multi-armed Bandits »
Amir Sani · Alessandro Lazaric · Remi Munos -
2012 Poster: Active Learning of Model Evidence Using Bayesian Quadrature »
Michael A Osborne · David Duvenaud · Roman Garnett · Carl Edward Rasmussen · Stephen J Roberts · Zoubin Ghahramani -
2012 Session: Oral Session 2 »
Sebastien Bubeck -
2012 Poster: Cardinality Restricted Boltzmann Machines »
Kevin Swersky · Danny Tarlow · Ilya Sutskever · Richard Zemel · Russ Salakhutdinov · Ryan Adams -
2012 Poster: Practical Bayesian Optimization of Machine Learning Algorithms »
Jasper Snoek · Hugo Larochelle · Ryan Adams -
2011 Workshop: Bayesian Nonparametric Methods: Hope or Hype? »
Emily Fox · Ryan Adams -
2011 Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback »
Andreas Krause · Pradeep Ravikumar · Stefanie S Jegelka · Jeffrey A Bilmes -
2011 Workshop: Bayesian optimization, experimental design and bandits: Theory and applications »
Nando de Freitas · Roman Garnett · Frank R Hutter · Michael A Osborne -
2011 Oral: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
2011 Poster: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
2011 Poster: Contextual Gaussian Process Bandit Optimization »
Andreas Krause · Cheng Soon Ong -
2011 Poster: Crowdclustering »
Ryan G Gomes · Peter Welinder · Andreas Krause · Pietro Perona -
2011 Poster: Finite Time Analysis of Stratified Sampling for Monte Carlo »
Alexandra Carpentier · Remi Munos -
2011 Poster: Multi-Bandit Best Arm Identification »
Victor Gabillon · Mohammad Ghavamzadeh · Alessandro Lazaric · Sebastien Bubeck -
2011 Poster: Selecting the State-Representation in Reinforcement Learning »
Odalric-Ambrym Maillard · Remi Munos · Daniil Ryabko -
2011 Poster: Sparse Recovery with Brownian Sensing »
Alexandra Carpentier · Odalric-Ambrym Maillard · Remi Munos -
2011 Session: Spotlight Session 2 »
Remi Munos -
2011 Session: Oral Session 1 »
Remi Munos -
2011 Poster: Optimal Reinforcement Learning for Gaussian Systems »
Philipp Hennig -
2011 Poster: Optimistic Optimization of Deterministic Functions »
Remi Munos -
2011 Poster: Speedy Q-Learning »
Mohammad Gheshlaghi Azar · Remi Munos · Mohammad Ghavamzadeh · Hilbert J Kappen -
2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Workshop: Monte Carlo Methods for Bayesian Inference in Modern Day Applications »
Ryan Adams · Mark A Girolami · Iain Murray -
2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Session: Spotlights Session 10 »
Nando de Freitas -
2010 Oral: Slice sampling covariance hyperparameters of latent Gaussian models »
Iain Murray · Ryan Adams -
2010 Session: Oral Session 12 »
Nando de Freitas -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: Slice sampling covariance hyperparameters of latent Gaussian models »
Iain Murray · Ryan Adams -
2010 Spotlight: Efficient Minimization of Decomposable Submodular Functions »
Peter G Stobbe · Andreas Krause -
2010 Spotlight: LSTD with Random Projections »
Mohammad Ghavamzadeh · Alessandro Lazaric · Odalric-Ambrym Maillard · Remi Munos -
2010 Poster: Discriminative Clustering by Regularized Information Maximization »
Ryan G Gomes · Andreas Krause · Pietro Perona -
2010 Poster: Efficient Minimization of Decomposable Submodular Functions »
Peter G Stobbe · Andreas Krause -
2010 Poster: LSTD with Random Projections »
Mohammad Ghavamzadeh · Alessandro Lazaric · Odalric-Ambrym Maillard · Remi Munos -
2010 Poster: Near-Optimal Bayesian Active Learning with Noisy Observations »
Daniel Golovin · Andreas Krause · Debajyoti Ray -
2010 Poster: Scrambled Objects for Least-Squares Regression »
Odalric-Ambrym Maillard · Remi Munos -
2010 Poster: Error Propagation for Approximate Policy and Value Iteration »
Amir-massoud Farahmand · Remi Munos · Csaba Szepesvari -
2009 Workshop: Adaptive Sensing, Active Learning, and Experimental Design »
Rui M Castro · Nando de Freitas · Ruben Martinez-Cantin -
2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes -
2009 Poster: Sensitivity analysis in HMMs with application to likelihood maximization »
Pierre-Arnaud Coquelin · Romain Deguest · Remi Munos -
2009 Poster: Online Learning of Assignments »
Matthew Streeter · Daniel Golovin · Andreas Krause -
2009 Spotlight: Online Learning of Assignments »
Matthew Streeter · Daniel Golovin · Andreas Krause -
2009 Poster: Compressed Least-Squares Regression »
Odalric-Ambrym Maillard · Remi Munos -
2009 Tutorial: Sequential Monte-Carlo Methods »
Arnaud Doucet · Nando de Freitas -
2008 Poster: An interior-point stochastic approximation method and an L1-regularized delta rule »
Peter Carbonetto · Mark Schmidt · Nando de Freitas -
2008 Poster: Online Optimization in X-Armed Bandits »
Sebastien Bubeck · Remi Munos · Gilles Stoltz · Csaba Szepesvari -
2008 Oral: An interior-point stochastic approximation method and an L1-regularized delta rule »
Peter Carbonetto · Mark Schmidt · Nando de Freitas -
2008 Poster: The Gaussian Process Density Sampler »
Ryan Adams · Iain Murray · David MacKay -
2008 Poster: Algorithms for Infinitely Many-Armed Bandits »
Yizao Wang · Jean-Yves Audibert · Remi Munos -
2008 Demonstration: Worio: A Web-Scale Machine Learning System »
Nando de Freitas · Ali Davar -
2008 Spotlight: The Gaussian Process Density Sampler »
Ryan Adams · Iain Murray · David MacKay -
2008 Spotlight: Algorithms for Infinitely Many-Armed Bandits »
Yizao Wang · Jean-Yves Audibert · Remi Munos -
2008 Poster: Particle Filter-based Policy Gradient in POMDPs »
Pierre-Arnaud Coquelin · Romain Deguest · Remi Munos -
2007 Spotlight: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Spotlight: Consistent Minimization of Clustering Objective Functions »
Ulrike von Luxburg · Sebastien Bubeck · Stefanie S Jegelka · Michael Kaufmann -
2007 Poster: Consistent Minimization of Clustering Objective Functions »
Ulrike von Luxburg · Sebastien Bubeck · Stefanie S Jegelka · Michael Kaufmann -
2007 Poster: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Poster: Fitted Q-iteration in continuous action-space MDPs »
Remi Munos · András Antos · Csaba Szepesvari -
2007 Spotlight: Bayesian Policy Learning with Trans-Dimensional MCMC »
Matthew Hoffman · Arnaud Doucet · Nando de Freitas · Ajay Jasra -
2007 Poster: Bayesian Policy Learning with Trans-Dimensional MCMC »
Matthew Hoffman · Arnaud Doucet · Nando de Freitas · Ajay Jasra -
2007 Poster: Active Preference Learning with Discrete Choice Data »
Eric Brochu · Nando de Freitas · Abhijeet Ghosh -
2006 Poster: Conditional mean field »
Peter Carbonetto · Nando de Freitas