BayesOpt 2017

NIPS Workshop on Bayesian Optimization
December 9, 2017
Long Beach, USA

Accepted Papers
Past Workshops

Special Issue

Workshop schedule

Saturday 9 December, 2017

Timetable Session
9:00 AM Introduction and opening remarks
9:10 AM Invited talk: Andreas Krause
Towards Safe Bayesian Optimization
9:40 AM Invited talk: Yutian Chen
Learning to learn without gradient descent by gradient descent
10:10 AM Poster spotlights 1
10:30 AM Coffee Break
11:00 AM Invited talk: Stefanie Jegelka
Scaling Bayesian Optimization in High Dimensions
11:30 AM Poster spotlights 2
11:45 AM Poster session 1
12:00 PM Lunch Break
2:00 PM Invited talk: Romy Lorenz
Neuroadaptive Bayesian Optimization - Implications for Cognitive Sciences
2:30 PM Poster session 2
3:00 PM Coffee Break
3:30 PM Poster session 3
4:00 PM Invited talk: Peter Frazier
Knowledge Gradient Methods for Bayesian Optimization
4:30 PM Invited talk: David Ginsbourger
Quantifying and reducing uncertainties on sets under Gaussian Process priors
5:00 PM Panel discussion (moderated by Philipp Hennig)
Yutian Chen, Romy Lorenz, Peter Frazier and David Ginsbourger
Ask questions
6:00 PM End

Poster spotlights 1

  1. Batched Large-scale Bayesian Optimization in High-dimensional Spaces
    Zi Wang, MIT; Clement Gehring, MIT; Pushmeet Kohli, DeepMind; Stefanie Jegelka, MIT
  2. Information-Based Multi-Fidelity Bayesian Optimization
    Yehong Zhang, National University of Singapore; Trong Nghia Hoang, MIT; Kian Hsiang Low, National University of Singapore ; Mohan Kankanhalli, National University of Singapore,
  3. Context-Dependent Bayesian Optimization in Real-Time Optimal Control: A Case Study in Airborne Wind Energy Systems
    Ali Baheri, UNC Charlotte
  4. Constrained Bayesian Optimization for Automatic Chemical Design
    Ryan-Rhys Griffiths, University of Cambridge; Jose-Miguel Hernandez-Lobato, University of Cambridge
  5. Learning to Transfer Initializations for Bayesian Hyperparameter Optimization
    Jungtaek Kim, POSTECH; Saehoon Kim, POSTECH; Seungjin Choi, POSTECH
  6. Bayesian Optimization of Unimodal Functions
    Michael Andersen, Aalto University; Eero Siivola, Aalto University; Aki Vehtari, Aalto University
  7. Bayesian Adaptive Direct Search: Hybrid Bayesian Optimization for Model Fitting
    Luigi Acerbi, New York University; Wei Ji Ma, New York University
  8. Efficient nonmyopic batch active search
    Shali Jiang, Washington University in St. Louis; Gustavo Malkomes, Washington University in St. Louis; Matthew Abbott, Washington University in St. Louis; Benjamin Moseley, Washington University in St. Louis; Roman Garnett, Washington University in St. Louis
  9. Predictive Variance Reduction Search
    Vu Nguyen, Deakin University; Sunil Gupta, Deakin University, Australia; Santu Rana, Deakin University, Australia; Cheng Li, Deakin University; Svetha Venkatesh, Deakin University
  10. Distance Exploration for Scalable Batch Bayesian Optimization
    Vu Nguyen, Deakin University; Sunil Gupta, Deakin University, Australia; Santu Rana, Deakin University, Australia; Cheng Li, Deakin University; Svetha Venkatesh, Deakin University
  11. Bayesian Optimization Under Uncertainty
    Justin Beland, University of Toronto; Prasanth Nair, University of Toronto
  12. Bayesian Optimization with Monotonicity Information
    Cheng Li, Deakin University; Santu Rana, Deakin University, Australia; Sunil Gupta, Deakin University, Australia; Vu Nguyen, Deakin University; Svetha Venkatesh, Deakin University
  13. Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
    Eero Siivola, Aalto University; aki Vehtari, Aalto University; Javier Gonzalez,; Jarno Vanhatalo, University of Helsinki
  14. Fast Information-theoretic Bayesian Optimisation
    Binxin Ru, University of Oxford; Michael A. Osborne, University of Oxford; Mark Mcleod, University of Oxford
  15. Finding minimum energy paths using Gaussian process regression
    Olli-Pekka Koistinen, Aalto University; aki Vehtari, Aalto University

Poster spotlights 2

  1. Continuous-Fidelity Bayesian Optimization with Knowledge Gradient
    Jian Wu, Cornell University; Peter Frazier, Cornell University
  2. Probabilistic Optimization with Latent Search for Automatic Model Selection
    Xiaoyu Lu, University of Oxford; Javier Gonzalez,; Zhenwen Dai, Amazon; Neil Lawrence, Amazon
  3. RoBO: A Flexible and Robust Bayesian Optimization Framework in Python
    Aaron Klein, Universität Freiburg; Stefan Falkner, University of Freiburg; Numair Mansur, University of Freiburg; Frank Hutter, University of Freiburg
  4. GPflowOpt: A Bayesian Optimization Library using TensorFlow
    Nicolas Knudde, Ghent University imec; Joachim van der Herten, Ghent University imec; Tom Dhaene, Ghent University imec; Ivo Couckuyt, Ghent University iMinds
  5. Memory Bandits: a Bayesian Approach for the Switching Bandit problem
    Reda ALAMI, Orange Labs; Odalric Maillard, Inria; Raphael Feraud, Orange Labs
  6. Learning Locomotion Primitives from Contextual Bayesian Optimization
    Brian Yang, UC Berkeley; Grant Wang, UC Berkeley; Roberto Calandra, UC Berkeley; Daniel Contreras, UC Berkeley; Sergey Levine, UC Berkeley; Kristofer Pister, UC Berkeley
  7. The Kalai-Smorodinski solution for many-objective Bayesian optimization
    Mickael Binois, University of Chicago; Victor Picheny, INRA; Abderrahmane Habbal, Universite de Nice
  8. An ADMM Framework for Constrained Bayesian Optimization
    Setareh Ariafar, Northeastern University; Jaume Coll-Font , Northeastern University; Dana Brooks, Northeastern University; Jennifer Dy, Northeastern
  9. Filtering Outliers in Bayesian Optimization
    Ruben Martinez-Cantin, SigOpt; Kevin Tee, SigOpt; Mike Mccourt, SigOpt; Katharina Eggensperger, University of Freiburg
  10. The reparameterization trick for acquisition functions
    James Wilson, Imperial College of London; Riccardo Moriconi, Imperial College of London; Frank Hutter, University of Freiburg; Marc Peter Deisenroth, Imperial College of London
  11. A parametric approach to Bayesian optimization with pairwise comparisons
    Marco Cox, Eindhoven University of Technology; Bert de Vries, Eindhoven University of Technology
  12. Combining Hyperband and Bayesian Optimization
    Stefan Falkner, University of Freiburg; Aaron Klein, Universität Freiburg; Frank Hutter, University of Freiburg
  13. Bayesian Optimization for a Better Dessert
    Greg Kochanski, Google, Inc.; Daniel Golovin, Google, Inc.; John Karro, Google, Inc.; Ben Solnik, Google, Inc.; Subhodeep Moitra, Google, Inc.; D Sculley, Google
  14. Multi-Attribute Bayesian Optimization under Utility Uncertainty
    Raul Astudillo Marban, Cornell University; Peter Frazier, Cornell University
  15. Shape optimization in laminar flow
    Stephan Eismann, Stanford University; Stefan Bartzsch, TU Munich; Stefano Ermon, Stanford University