{"id":301196,"date":"2016-11-01T13:02:14","date_gmt":"2016-11-01T20:02:14","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=301196"},"modified":"2025-08-06T11:58:53","modified_gmt":"2025-08-06T18:58:53","slug":"machine-learning-summit-2013","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/machine-learning-summit-2013\/","title":{"rendered":"Machine Learning Summit 2013"},"content":{"rendered":"\n\n

Concorde La Fayette
\n3, Place du G\u00e9n\u00e9ral K\u0153nig 75017
\nParis,\u00a0France<\/p>\n

Past events:<\/strong>
\nDevices and Networking Summit 2015<\/a>
\nSoftware Summit 2011<\/a>Opens in a new tab<\/span><\/p>\n

For the last four decades the digital revolution has been driven by the exponential growth in the number of transistors that can be packed onto a silicon chip. Today we are seeing a new kind of \u201cMoore\u2019s Law\u201d in which the quantity of data in the world is doubling roughly every 18 months. This data deluge has the potential to transform many facets of society, from healthcare to education, and from commerce to the environment. The key to unlock this potential will be the ability to extract useful information from the data, and it is here that machine learning will play a pivotal role.<\/p>\n

Machine learning thrives on data, and we can anticipate substantial growth in the diversity and the scale of impact of machine learning applications over the coming decade. This exciting new opportunity will also raise many challenges, and will require the development of new techniques for handling and learning from large data sets, as well as new tools to support a growing community of machine learning practitioners.<\/p>\n

The Machine Learning Summit 2013 brought together thought leaders and researchers from a broad range of disciplines including computer science, engineering, statistics and mathematics. Together they highlighted some of the key challenges posed by this new era of machine learning, and identified the next generation of approaches, techniques and tools that will be needed to exploit the information revolution for the benefit of society.<\/p>\n

Event Chairs<\/h2>\n\n\n\n
\"Chris
\nChris Bishop<\/td>\n		\"EvelyneEvelyne Viegas<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Confirmed Speakers<\/h2>\n\n\n\n
\"Andrew
\nAndrew Blake<\/td>\n		\"HermannHermann Hauser<\/td>\n\"Judea
\nJudea Pearl<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Opens in a new tab<\/span><\/p>\n

April 22<\/h2>\n\n\n\n\n\n
Time<\/th>\nSession<\/th>\nLocation<\/th>\n<\/tr>\n<\/thead>\n
\n
19:30<\/div>\n<\/td>\n
\n
Welcome Drinks Reception<\/div>\n<\/td>\n
\n
Concorde La Fayette<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

April 23<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nSession<\/th>\nSpeaker<\/th>\nLocation<\/th>\n<\/tr>\n<\/thead>\n
\n
07:35<\/div>\n<\/td>\n
\n
Coach Transfer to\u00a0Microsoft Le Campus<\/div>\n<\/td>\n
<\/td>\n\n
Concorde La Fayette<\/div>\n<\/td>\n<\/tr>\n
\n
08:15<\/div>\n<\/td>\n
\n
Light Breakfast and Registration<\/div>\n<\/td>\n
<\/td>\nArc-en-Ciel<\/td>\n<\/tr>\n
\n
09:00<\/div>\n<\/td>\n
\n
Opening\/Welcome remarks<\/div>\n<\/td>\n
\n
Alain Crozier, President, Microsoft France<\/div>\n<\/td>\n
\n
Grand Bleu<\/div>\n<\/td>\n<\/tr>\n
\n
09:10<\/div>\n<\/td>\n
\n
Introductory Talk<\/div>\n<\/td>\n
\n
Rick Rashid, Microsoft Research<\/div>\n<\/td>\n
\n
Grand Bleu<\/div>\n<\/td>\n<\/tr>\n
\n
09:30<\/div>\n<\/td>\n
\n
Plenary 1 Keynote: Machines that (Learn to) See<\/strong><\/div>\n<\/td>\n
\n
\n

Chair:<\/strong> Chris Bishop, Microsoft Research<\/p>\n

    \n
  • Andrew Blake, Microsoft Research<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
10:30<\/div>\n<\/td>\n
\n
Break<\/div>\n<\/td>\n
<\/td>\n\n
Arc-en-Ciel<\/div>\n<\/td>\n<\/tr>\n
\n
11:00<\/div>\n<\/td>\n
\n
Parallel Sessions<\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\n\n
Session 1: Model-Based Machine Learning in Practice<\/strong><\/div>\n<\/td>\n
\n
\n

Chair:<\/strong> John Bronskill, Microsoft Research<\/p>\n

    \n
  • Thomas Minka, Microsoft Research<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
\n
Rubis<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n\n
Session 2: Large Scale Machine Learning<\/strong><\/div>\n<\/td>\n
\n
\n

Chair:<\/strong> Leon Bottou, Microsoft Research<\/p>\n

    \n
  • Francis Bach, INRIA – Ecole Normale Superieure<\/li>\n
  • Anatoli Juditski, University J. Fourier of Grenoble<\/li>\n
  • Alekh Agarwal, Microsoft Research<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
\n
Grand Bleu<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n\n
Session 3: Game Theory Meets Machine Learning<\/strong><\/div>\n<\/td>\n
\n
\n

Chair:<\/strong> Laurent Massoulie, Microsoft Research-Inria Joint Centre<\/p>\n

    \n
  • Avrim Blum, Carnegie Mellon University<\/li>\n
  • Amos Storkey, University of Edinburgh<\/li>\n
  • Peter Key, Microsoft Research<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
\n
Prairie<\/div>\n<\/td>\n<\/tr>\n
\n
12:30<\/div>\n<\/td>\n
\n
Lunch<\/div>\n<\/td>\n
<\/td>\n\n
Arc-en-Ciel<\/div>\n<\/td>\n<\/tr>\n
\n
13:30<\/div>\n<\/td>\n
\n
Parallel Sessions<\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
13:30<\/div>\n<\/td>\n
\n
Session 4: Learning with Millions of Categories<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Yann LeCun, New York University<\/p>\n
    \n
  • Samy Bengio, Google<\/li>\n
  • Fei-Fei Li, Stanford University<\/li>\n
  • P. Anandan, Microsoft Research<\/li>\n<\/ul>\n<\/td>\n
Prairie<\/td>\n<\/tr>\n
<\/td>\n\n
Session 5: Model-Based Machine Learning Tutorial with Infer.NET<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> John Bronskill, Microsoft Research<\/p>\n
    \n
  • John Guiver, Microsoft Research<\/li>\n<\/ul>\n<\/td>\n
Rubis<\/td>\n<\/tr>\n
<\/td>\n\n
Session 6: Machine Learning and Social Data<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Pushmeet Kohli, Microsoft Research<\/p>\n
    \n
  • Foster Provost, New York University<\/li>\n
  • Sharad Goel, Microsoft Research<\/li>\n
  • Elad Yom-Tov, Microsoft Research<\/li>\n<\/ul>\n<\/td>\n
Grand Bleu<\/td>\n<\/tr>\n
15:00<\/td>\n\n
DemoFest<\/strong><\/div>\n<\/td>\n
\n
    \n
  • Machine Learning for Xbox Live
    \n<\/strong>Noam Koenigstein, Microsoft Research; Ulrich Paquet, Microsoft Research<\/li>\n
  • Automatic Segmentation of High-grade Brain Tumours by Context-sensitive Decision Forests
    \n<\/strong>Darko Zikic, Microsoft Research<\/li>\n
  • Teaching Kinect to Read Your Body and Hands
    \n<\/strong>Jamie Shotton, Microsoft Research<\/li>\n
  • A weakly-supervised approach for building statistical conversational understanding models
    \n<\/strong>Dilek Hakkani-Tur, Microsoft Research; Larry Heck, Microsoft Research; Gokhan Tur, Microsoft Research; Asli Celikyilmaz, Microsoft Research<\/li>\n
  • Model-based Machine Learning Using Infer.Net
    \n<\/strong>John Bronskill, Microsoft Research<\/li>\n
  • Real-Time Business Metadata Extraction
    \n<\/strong>Dimitrios Lymberopoulos, Microsoft Research<\/li>\n
  • Juggling the Jigsaw: Towards Automated Problem Inference from Network Trouble Tickets
    \n<\/strong>Navendu Jain, Microsoft Research<\/li>\n
  • Querying Human Activities – Social Media Analytics Platform
    \n<\/strong>Emre Kiciman, Microsoft Research<\/li>\n
  • Probabilistic Programming with Try F#
    \n<\/strong>Christophe Poulain, Microsoft Research\u00a0<\/strong><\/li>\n<\/ul>\n<\/td>\n
Arc-en-Ciel<\/td>\n<\/tr>\n
17:00<\/td>\n\n
Plenary 2 Keynote: The Mathematics of Causal Inference: with Reflections on Machine Learning<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Tony Hey<\/p>\n
    \n
  • Judea Pearl, University of California<\/li>\n<\/ul>\n<\/td>\n
Grand Bleu<\/td>\n<\/tr>\n
18:00<\/td>\n\n
Close<\/div>\n<\/td>\n
\u00a0<\/strong><\/td>\n<\/td>\n<\/tr>\n
18:15<\/td>\n\n
Coach Transfer to\u00a0La Gare Restaurant<\/div>\n<\/td>\n
\u00a0<\/strong><\/td>\nMicrosoft Le Campus<\/td>\n<\/tr>\n
19:00<\/td>\n\n
Evening Dinner<\/div>\n<\/td>\n
\u00a0<\/strong><\/td>\n<\/td>\n<\/tr>\n
22:00<\/td>\n\n
Coach Transfer to\u00a0Concorde La Fayette<\/div>\n<\/td>\n
\u00a0<\/strong><\/td>\nLa Gare Restaurant<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

April 24<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nSession<\/th>\nSpeaker<\/th>\nLocation<\/th>\n<\/tr>\n<\/thead>\n
\n
07:35<\/div>\n<\/td>\n
\n
Coach Transfer to\u00a0Microsoft Le Campus<\/div>\n<\/td>\n
<\/td>\n\n
Concorde La Fayette<\/div>\n<\/td>\n<\/tr>\n
\n
08:15<\/div>\n<\/td>\n
\n
Light Breakfast<\/div>\n<\/td>\n
<\/td>\nArc-en-Ciel<\/td>\n<\/tr>\n
\n
09:00<\/div>\n<\/td>\n
\n
Plenary 3 Keynote: Data is accumulating at such a rate that there are no longer enough qualified humans to analyse it<\/strong><\/div>\n<\/td>\n
\n
\n

Chair:<\/strong> Peter Lee, Microsoft Research<\/p>\n

    \n
  • Hermann Hauser, Amadeus Capital Partners<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
\n
Grand Bleu<\/div>\n<\/td>\n<\/tr>\n
\n
10:00<\/div>\n<\/td>\n
\n
Break<\/div>\n<\/td>\n
<\/td>\n\n
Arc-en-Ciel<\/div>\n<\/td>\n<\/tr>\n
\n
10:30<\/div>\n<\/td>\n
\n
Parallel Sessions<\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\n\n
Session 7: Machine Learning in Healthcare<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Silvia Chiappa, Microsoft Research<\/p>\n
    \n
  • David Page, University of Wisconsin-Madison<\/li>\n
  • Antonio Criminisi, Microsoft Research<\/li>\n
  • Bert Kappen, University of Radboud<\/li>\n<\/ul>\n<\/td>\n
\n
Grand Bleu<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n\n
Session 8: Machine Learning for Computer Vision<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Sebastian Nowozin, Microsoft Research<\/p>\n
    \n
  • Carsten Rother, Microsoft Research<\/li>\n
  • Tomas Werner, Czech Technical University<\/li>\n
  • Bill Freeman, Massachusetts Institute of Technology<\/li>\n<\/ul>\n<\/td>\n
Prairie<\/td>\n<\/tr>\n
<\/td>\n\n
Session 9: Causality and Machine Learning<\/strong><\/div>\n<\/td>\n
\n
\n

Chair:<\/strong> Zoubin Ghahramani, University of Cambridge<\/p>\n

    \n
  • Isabelle Guyon, ChaLearn. Thomas Richardson, University of Washington<\/li>\n
  • Leon Bottou, Microsoft Research<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
\n
Rubis<\/div>\n<\/td>\n<\/tr>\n
12:00<\/td>\n\n
Lunch<\/div>\n<\/td>\n
<\/td>\n\n
Arc-en-Ciel<\/div>\n<\/td>\n<\/tr>\n
13:00<\/td>\n\n
Parallel Sessions<\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\n\n
Session 10: The Future of Probabilistic Programming<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Thore Graepel, Microsoft Research<\/p>\n
    \n
  • Andy Gordon, Microsoft Research<\/li>\n
  • Vikash Mansinghka, Massachusetts Institute of Technology<\/li>\n
  • Avi Pfeffer, Charles River Analytics<\/li>\n
  • Christopher Re, University of Wisconsin-Madison<\/li>\n<\/ul>\n<\/td>\n
\n
Prairie<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n\n
Session 11: Machine Learning and Natural Language<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Ronan Collobert, Idiap Research Institute<\/p>\n
    \n
  • Steve Renals, University of Edinburgh<\/li>\n
  • Hermann Ney, RWTH Aachen University<\/li>\n
  • Alex Acero, Microsoft Research<\/li>\n<\/ul>\n<\/td>\n
Grand Bleu<\/td>\n<\/tr>\n
<\/td>\n\n
Session 12: Machine Learning and Crowdsourcing<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Yoram Bachrach, Microsoft Research<\/p>\n
    \n
  • Emre Kiciman, Microsoft Research<\/li>\n
  • Eyal Amir, University of Illinois, Urbana-Champaign<\/li>\n
  • David Parkes, Harvard University<\/li>\n<\/ul>\n<\/td>\n
Rubis<\/td>\n<\/tr>\n
14:30<\/td>\n\n
Break<\/div>\n<\/td>\n
\u00a0<\/strong><\/td>\nArc-en-Ciel<\/td>\n<\/tr>\n
<\/td>\n\n
Plenary 4 Keynote: Data Challenges and Opportunities in the Next Decade<\/strong><\/div>\n<\/td>\n
Chair:<\/strong> Jeannette Wing, Microsoft Research<\/p>\n

Panelists: <\/strong><\/p>\n

    \n
  • Eric Horvitz, Microsoft Research<\/li>\n
  • Michel Cosnard, INRIA. Iain Buchan, Manchester University<\/li>\n
  • Lionel Tarassenko, University of Oxford<\/li>\n<\/ul>\n<\/td>\n
Grand Bleu<\/td>\n<\/tr>\n
16:00<\/td>\n\n
Closing Remarks<\/strong><\/div>\n<\/td>\n
\n
    \n
  • Chris Bishop, Microsoft Research<\/li>\n
  • Evelyne Viegas, Microsoft Research<\/li>\n<\/ul>\n<\/td>\n
Grand Bleu<\/td>\n<\/tr>\n
16:10<\/td>\n\n
Close of Day<\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Opens in a new tab<\/span><\/p>\n

Plenary Sessions<\/h2>\n

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