Schedule

Date

Person in charge

Paper  

24/05/2017

James

?

Round Robin counts (starting 14.10.2016)

Matthias

2

Katharina

2

Aaron

3

Stefan

2

Ilya

3

Marius

2

James

1

Jan

3

Paper Pool

Parallel Bayesian optimization

Suggested by

Paper

Frank

Thomas Desautels, Andreas Krause and Joel W. Burdick

Parallelizing Exploration-Exploitation Tradeoffs in Gaussian Process Bandit Optimization

In: JMLR 2014

http://jmlr.org/papers/volume15/desautels14a/desautels14a.pdf

Batch Bayesian Optimization via Local Penalization

Javier Gonzalez, Zhenwen Dai, Philipp Hennig, Neil Lawrence

http://bayesopt.github.io/papers/2015/gonzalez-batch.pdf

Katharina

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions

Amar Shah and Zoubin Ghahramani

https://arxiv.org/pdf/1511.07130v1.pdf

Kriging Is Well-Suited to Parallelize Optimization

David Ginsbourger, Rodolphe Le Riche, Laurent Carraro

http://link.springer.com/chapter/10.1007/978-3-642-10701-6_6

Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration

Emile Contal, David Buoni, Alexandre Robicquet, and Nicolas Vayatis

https://arxiv.org/pdf/1304.5350v3.pdf

Beyond the Blackbox

Suggested by

Paper

Frank

Andreas Krause and Cheng Soon Ong

Contextual Gaussian Process Bandit Optimization

In: Advances in Neural Information Processing Systems (NIPS’11)

http://papers.nips.cc/paper/4487-contextual-gaussian-process-bandit-optimization.pdf 

long: http://las.ethz.ch/files/krause11contextual-long.pdf

Acquisition functions etc.

Suggested by

Paper

Output-Space Predictive Entropy Search for Flexible Global Optimization

Matt Hoffman, Zoubin Ghahramani

http://mlg.eng.cam.ac.uk/hoffmanm/papers/hoffman:2015.pdf

Multi-objective optimization

Suggested by

Paper

Predictive Entropy Search for Multi-objective Bayesian Optimization

Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Amar Shah, Ryan Adams

http://bayesopt.github.io/papers/2015/hernandez-lobato.pdf

Gaussian processes

Suggested by

Paper

Aaron

Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression (related to Distributed Gaussian Processes, see below)

Jun Wei Ng, Marc Peter Deisenroth

http://arxiv.org/pdf/1412.3078v1.pdf

Aaron

Manifold Gaussian Processes for Regression

Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth

http://arxiv.org/pdf/1402.5876v3.pdf

Other

Suggested by

Paper

Frank

Yiliang Xu and Arslan Basharat and Jacob Becker and Anthony Hoogs(pdf)(bib)

Complex algorithm optimization through probabilistic search of its configuration tree

In: Proceedings of 11th IEEE International Conference on the Advanced Video and Signal Based Surveillance 2014

Quantifying mismatch in Bayesian optimization

Eric Schulz, Maarten Speekenbrink, J.M. Hernandez-Lobato, Zoubin Ghahramani, Samuel Gershman

https://bayesopt.github.io/papers/2016/Schulz.pdf

Tianqi Chen, Carlos Guestrin

XGBoost: A Scalable Tree Boosting System

http://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf

Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin,

Why Shoud I strust you: Explaining the Predictions of Any Classifier

http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Mondrian Forests: Efficient Online Random Forests/Mondrian Forests for Large-Scale Regression when Uncertainty Matters

https://github.com/balajiln/mondrianforest

History

Date

Person in charge

Paper

10/03/2015

Mustafa

Kent McClymont and Edward C. Keedwell(pdf)(bib)

Markov chain hyper-heuristic (MCHH): an online selective hyper-heuristic for multi-objective continuous problems. GECCO 211

17/03/2015

Mustafa

Mustafa Misir, Katja Verbeeck, Patrick De Causmaecker, Greet Vanden Berghe

A new hyper-heuristic as a general problem solver: an implementation in HyFlex

Journal of Scheduling, 16(3), 291-311, 2013

http://link.springer.com/article/10.1007/s10951-012-0295-8#page-1 

23/03/2015

10:00

Matthias

Caruana, Rich and Niculescu-Mizil, Alexandru and Crew, Geoff and Ksikes, Alex
Ensemble selection from libraries of models

Proceedings of the twenty-first international conference on Machine learning. 2004

http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf

Further reading:

Rich Caruana, Art Munson, Alexandru Niculescu-Mizil

Getting the Most Out of Ensemble Selection

ICDM'06.

(the paper is attached to the original paper in the PDF above)

30/03/2015

10:00

Frank

Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas.

On correlation and budget constraints in model-based bandit

optimization with application to automatic machine learning. AISTATS 2014.

jmlr.org/proceedings/papers/v33/hoffman14.pdf

13/04/2015

10:00

Aaron

Jose Miguel Hernandez-Lobato, Matthew W. Hoffman, Zoubin Ghahramani

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. NIPS 2014

http://papers.nips.cc/paper/5324-predictive-entropy-search-for-efficient-global-optimization-of-black-box-functions.pdf 

20/04/2015

10:00

Martin G. & David B

Martin’s Master Project on “Algorithm Configuration in the cloud”

David’s Bachelor Project on “Instance-Specific Algorithm Schedules”

28/04/2015

10:00

Marius

Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann

Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering. IJCAI 2013

http://4c.ucc.ie/~ymalitsky/Papers/IJCAI-13.pdf 

04/05/2015

10:00

Stefan

An Entropy Search Portfolio for Bayesian Optimization

Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas

http://arxiv.org/abs/1406.4625

01/06/2015
10:00

Stefan

Model-based Genetic Algorithms for Algorithm Configuration

Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, Carlos Ansotegui, and Kevin Tierney

https://drive.google.com/open?id=0B2py-37R9PftOV9URy1XYkhPRzQ&authuser=0 

08/06/2015

Ilya

Online Adaptation of Hyper-parameters in CMA-ES

15/06/2015

10:00

Stefan

Peter Auer, Nicolo Cesa-Bianchi and Paul Fischer

Finite-time analysis of the multiarmed bandit problem, Machine Learning, 47 (2-3), 2002, Springer

http://link.springer.com/article/10.1023/a:1013689704352

03/08/2015

10:00

Mustafa

Neural fitted Q iteration--first experiences with a data efficient neural reinforcement learning method

M. Riedmiller, ECML 2005

http://link.springer.com/chapter/10.1007/11564096_32 

10/08/2015

10:00

Mustafa

Collaborative Expert Portfolio Management
Stern, David H and Samulowitz, Horst and Herbrich, Ralf and Graepel, Thore and Pulina, Luca and Tacchella, Armando. AAAI 2010

http://research.microsoft.com/pubs/122781/matchboxqbf.pdf 

Matchbox: large scale online bayesian recommendations
Stern, David H and Herbrich, Ralf and Graepel, Thore. WWW 2009

http://www2009.eprints.org/12/1/p111.pdf 

17/08/2015

10:00

Katharina

Statistical Regimes and Runtime Prediction

Hurley, Barry and O’Sullivan, Barry. IJCAI’15

http://ijcai.org/papers15/Papers/IJCAI15-051.pdf

21/09/2015

10:00

Stefan

Gambling in a rigged casino: The adversarial multi-armed bandit problem

Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund and Robert E. Schapire

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=492488

29/09/2015

11:00

Matthias

Benchmarking Classification Algorithms on High-Performance Computing Clusters

Bernd Bischl, Julia Schiffner and Claus Weihs

https://www.statistik.tu-dortmund.de/~bischl/mypapers/benchmarking_classification_algorithms_on_high_performance_computing_clusters.pdf

http://rsrg.cms.caltech.edu/netecon/privacy2015/slides/hardt.pdf 

20.10.2015

10:30

Stefan

Matthew Hoffman, Eric Brochu, Nando de Freitas

Portfolio Allocation for Bayesian Optimization

https://dslpitt.org/papers/11/p327-hoffman.pdf

20.11.2015

14:00

(cont. of 10.11.2015)

Stefan

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Niranjan Srinivas and Andreas Krause and Sham M. Kakade and Matthias Seger. ICML 2010

http://las.ethz.ch/files/srinivas10gaussian.pdf

long: http://las.ethz.ch/files/srinivas10gaussian-long.pdf

24.11.2015

10:30

Aaron

Automating Model Search for Large Scale Machine Learning

Sparks, Evan R and Talwalkar, Ameet and Haas, Daniel and Franklin, Michael J and Jordan, Michael I and Kraska, Tim. ACM Symposium on Cloud Computing 2015 (SoCC'15)

https://amplab.cs.berkeley.edu/wp-content/uploads/2015/07/163-sparks.pdf 

01.12.2015

10:30

Matthias

Fast Cross-Validation via Sequential Testing

Tammo Krueger, Danny Panknin, Mikio Braun; 16(Jun):1103−1155, 2015.

http://www.jmlr.org/papers/v16/krueger15a.html

26.01.2016

10:30

Matthias

Learning Hyperparameter Optimization Initializations

Martin Witsuba, Nicolas Schilling, Lars Schmidt-Thieme

http://www.ismll.uni-hildesheim.de/pub/pdfs/wistuba_et_al_DSAA_2015.pdf

23/02/2016

10:30

Marius

Deep Learning for Algorithm Selection

by Andrea Loreggia, Yuri Malitsky, Horst Samulowitz, Vijay Saraswat - AAAI 2016

08.03.2016

10:00

Aaron/

Katharina

GLASSES : Relieving The Myopia Of Bayesian Optimisation. J. González, M. Osborne, and N. Lawrence. In AISTATS’16.

short: https://bayesopt.github.io/papers/2015/gonzalez.pdf 

long: http://arxiv.org/pdf/1510.06299v1.pdf 

Unbounded Bayesian Optimization via Regularization. B. Shahriari, A. Bouchard-Côté, and N. de Freitas, In AISTATS’16. http://arxiv.org/abs/1508.03666

16/06/2016

Matthias

Locally-Biased Bayesian Optimization using Nonstationary Gaussian Processes

Ruben Martinez-Cantin

http://bayesopt.github.io/papers/2015/martinez-cantin.pdf

http://arxiv.org/abs/1506.02080

01/07/2016

Katharina

Active Contextual Entropy Search

Jan Hendrik Metzen, BayesOpt'15

https://bayesopt.github.io/papers/2015/hendrik-metzen.pdf

08/07/2016

Stefan

Minimum Regret Search for Single- and Multi-Task Optimization

Jan Hendrik Metzen

In: Proceedings of The 33rd International Conference on Machine Learning, pp. 192–200, 2016

http://jmlr.org/proceedings/papers/v48/metzen16.html

15/07/2016

Aaron

Bayesian optimization for automated model selection

Gustavo Malkomes, Chip Schaff, Roman Garnett

https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE2fGd4OjFiMDM3YjYyNThiNDMyOWI

22/07/2016

11AM

Ilya

Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits

https://arxiv.org/abs/1603.06560

29/07/2016

Marius

Random Survival Forests

By Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone and Michael S. Lauer

https://arxiv.org/pdf/0811.1645.pdf

12/08/2016

Matthias

Foster Provost, Tom Fawcett and Ron Kohavi

The Case Against Accuracy Estimation for Comparing Induction Algorithms

http://pages.cs.wisc.edu/~shavlik/roc.pdf

19/08/2016

Katharina

Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, George Ke

A Stratified Analysis of Bayesian Optimization Methods

https://arxiv.org/abs/1603.09441

26/08/2016

Aaron

Multi-fidelity Gaussian Process Bandit Optimisation

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos

http://arxiv.org/abs/1603.06288

02/09/2016

Stefan

Statistical Comparisons of Classifiers over Multiple Data Sets

Janez Demsar

http://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf

09/09/2016

Ilya

COCO: Performance Assessment

Nikolaus Hansen, Anne Auger, Dimo Brockhoff, Dejan Tušar and Tea Tušar

http://arxiv.org/pdf/1605.03560.pdf

16/09/2016

Marius

Matthias Poloczek, Jialei Wang, Peter I. Frazier

Warm Starting Bayesian Optimization

http://arxiv.org/abs/1608.03585

30/09/2016

Stefan

Stefan Wager, Trevor Hastie and Bradley Efron

Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife

http://jmlr.org/papers/volume15/wager14a/wager14a.pdf

14/10/2016

Matthias

Scalable Hyperparameter Optimization with Products of Gaussian Process Experts

http://link.springer.com/chapter/10.1007%2F978-3-319-46128-1_3

Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization

Nicolas Schilling, Martin Wistuba and Lars Schmidt-Thieme

http://link.springer.com/chapter/10.1007%2F978-3-319-46128-1_13

Martin Wistuba, Nicolas Schilling and Lars Schmidt-Thieme

21/10/2016

Katharina

The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

Jian Wu, Peter I. Frazier

https://arxiv.org/pdf/1606.04414v1.pdf

28/10/2016

Jan

Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning

Rui Leite and Pavel Brazdil

http://ebooks.iospress.nl/volumearticle/5788

04/11/2016

Marius

Yuriy Dzerin: Master Project Defense: Parallel Intensification

Andre Biedenkapp: Master Thesis Introduction: Per-Instance Algorithm Configuration

18/11/2016

Aaron

Learning Curve Prediction with Bayesian Neural Networks

Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter

https://openreview.net/pdf?id=S11KBYclx

25/11/2016

Ilya

AN EMPIRICAL ANALYSIS OF DEEP NETWORK LOSS SURFACES

Daniel Jiwoong Im, Michael Tao & Kristin Branson

https://openreview.net/pdf?id=rkuDV6iex

02/12/2016

James

Learning to Learn for Global Optimization of Black Box Functions

Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas

https://arxiv.org/abs/1611.03824

16/12/2016

Markus Weimer

Markus Weimer leads the machine learning research group serving Azure ML, Azure Data Lake and Microsoft R Server and internal ML users at Microsoft.

10/01/2017

Marius

IMPORTANT: DATE CHANGED!

The irace package: Iterated racing for automatic algorithm configuration
Manuel López-Ibáñez
 Jérémie Dubois-Lacoste, Leslie Pérez Cáceres , Mauro Birattari , Thomas Stützle

http://www.sciencedirect.com/science/article/pii/S2214716015300270

20/01/2017

Jan

Hyperparameter Optimization Machines

Martin Wistuba, Nicolas Schilling and Lars Schmidt-Thieme

ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7796889

27/01/2017

Matthias

Bayesian Hyperparameter Optimization for Ensemble Learning

Julien-Charles Levesque, Christian Gagne and Robert Sabourin

http://auai.org/uai2016/proceedings/papers/73.pdf

03/02/2017

Ilya

On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang

https://openreview.net/forum?id=H1oyRlYgg

10/02/2017

Jan

Rijn, J.N. van

Massively collaborative machine learning, Chapter 6

https://openaccess.leidenuniv.nl/handle/1887/44814

09/03/2017

Stefan

Zhao Chen and Darvin Yi

The Game Imitation: Deep Supervised Convolutional Networks for

Quick Video Game AI (https://arxiv.org/abs/1702.05663)

Vlad Firoiu et al.

Beating the World's Best at Super Smash Bros. with Deep

Reinforcement Learning (https://arxiv.org/abs/1702.06230)

17/03/2017

Ilya

E. Real et al.

Large-Scale Evolution of Image Classifiers

https://arxiv.org/pdf/1703.01041.pdf

24/03/2017

Stefan

Olga Wichrowska et al.

Learned Optimizers that Scale and Generalize

https://arxiv.org/pdf/1703.04813

31/03/2017

Marius

N. Dang, L. Pérez Cáceres, T. Stützle, and P. De Causmaecker

Configuring irace using surrogate configuration benchmarks

http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-004.pdf 

07/04/2017

Aaron

Mark McLeod, Michael A. Osborne, Stephen J. Roberts

Practical Bayesian Optimization for Variable Cost Objectives

https://arxiv.org/pdf/1703.04335.pdf

05/05/2017

Marius

Michael Rudolph,

Christina Hernández Wunsch

12/05/2017

-

MSc thesis defense of Manuel Ruder (from the computer vision group)

He did, e.g., this: https://arxiv.org/abs/1604.08610, https://www.youtube.com/watch?v=Khuj4ASldmU, and was already on TV with it.

17/05/2017

Katharina

Preferential Bayesian Optimization

Javier Gonzalez, Zhenwen Dai, Andreas Domianou, Neil D. Lawrence

https://arxiv.org/pdf/1704.03651.pdf