Schedule

Date

Person in charge

Paper  

19/07/2017

Marius

20+20 presentation on current research

26/07/2017

Ilya

20+20 presentation on current research

02/08/2017

Aaron

20+20 presentation on current research

09/08/2017

Katharina

20+20 presentation on current research

Content

Schedule

Paper Pool

History

        2016

        2015

Paper Pool

Deep GPs

Suggested by

Paper

Frank

Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone

Random Feature Expansions for Deep Gaussian Processes

https://arxiv.org/pdf/1610.04386.pdf

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

12/07/2017

Stefan

20+20 presentation on current research

05/07/2017

Jan

20+20 presentation on current research

14/06/2017

(15.00)

Katharina

Miikkulainen et al

Evolving Deep Neural Networks

https://arxiv.org/pdf/1703.00548.pdf

17/05/2017

Katharina

Preferential Bayesian Optimization

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

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

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.

05/05/2017

Marius

Michael Rudolph,

Christina Hernández Wunsch

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

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 

24/03/2017

Stefan

Olga Wichrowska et al.

Learned Optimizers that Scale and Generalize

https://arxiv.org/pdf/1703.04813

17/03/2017

Ilya

E. Real et al.

Large-Scale Evolution of Image Classifiers

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

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)

10/02/2017

Jan

Rijn, J.N. van

Massively collaborative machine learning, Chapter 6

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

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

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

20/01/2017

Jan

Hyperparameter Optimization Machines

Martin Wistuba, Nicolas Schilling and Lars Schmidt-Thieme

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

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

2016

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.

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

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

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

04/11/2016

Marius

Yuriy Dzerin: Master Project Defense: Parallel Intensification

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

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

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

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

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

16/09/2016

Marius

Matthias Poloczek, Jialei Wang, Peter I. Frazier

Warm Starting Bayesian Optimization

http://arxiv.org/abs/1608.03585

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

02/09/2016

Stefan

Statistical Comparisons of Classifiers over Multiple Data Sets

Janez Demsar

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

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

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

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

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

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

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

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

01/07/2016

Katharina

Active Contextual Entropy Search

Jan Hendrik Metzen, BayesOpt'15

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

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

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

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

x

Marius

Deep Learning for Algorithm Selection

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

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

2015

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

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 

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

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

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 

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

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

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 

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 

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

08/06/2015

Ilya

Online Adaptation of Hyper-parameters in CMA-ES

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 

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

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 

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”

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 

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

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 

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”

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 

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

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)

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 

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

Round Robin counts (starting 14.10.2016)

Matthias

2

Katharina

3

Aaron

3

Stefan

2

Ilya

3

Marius

2

James

1

Jan

3