BayesOpt 2017

Workshop schedule

Saturday 9 December, 2017

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, Amazon.com; 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, Amazon.com; 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