July - December 1997
Organisers: C M Bishop (Aston), D Haussler (UCSC), G E Hinton (Toronto), M Niranjan (Cambridge), L G Valiant (Harvard)
The last few years have seen a substantial growth of research activity in machine learning, focusing in large part on neural network models. For most applications of machine learning the central issue is that of generalization.
This NATO ASI will provide a comprehensive and coherent tutorial programme aimed at research scientists at postdoctoral level and beyond, though it will also be accessible to advanced graduate students having a good mathematical background.
The complete programme is available below
NB Please note change of schedule for Thurs 7th and Weds 13th (updated 7/8/97)
Organising Committee:
Director: C
M Bishop (Aston)
J M Buhmann (Bonn), G E Hinton (Toronto), M I Jordan (MIT)
Lecturers:
| E Baum (NEC) | R Neal (Toronto) |
| C M Bishop (Aston) | DJC MacKay (Cambridge) |
| L Breiman (Berkeley) | B D Ripley (Oxford) |
| J M Buhmann (Bonn) | E Sontag (Rutgers) |
| P Dayan (MIT) | N Tishby (Jerusalem) |
| G E Hinton (Toronto) | L G Valiant (Harvard) |
| T Jaakkola (UCSC) | V Vapnik (AT&T) |
| M I Jordan (MIT) | C K I Williams (Aston) |
| Y Le Cun (AT&T) |
Sunday 3 August
18.00 Welcome reception in the Isaac Newton Institute, and registration
19.00 Dinner in Wolfson Court (for Wolfson Court residents only)
Monday 4 August
08:30 Registration
09:00 Bishop (1) Supervised Learning in Linear Models
10:30 Coffee
11:00 Williams (1) Supervised Learning in Non-linear Models
12:30 Lunch
14:00 Breiman (1) Instability, bias-variance and regularization
15:30 Tea 16:00 LeCun (1) Generalization in high-dimensional tasks
17:30 End of session
Tuesday 5 August
09:00 Bishop (2) Model Complexity and Generalization
10:30 Coffee
11:00 Neal (1) An illustrative research endeavour: The motivation, the idea, an empirical test, and the final conclusions
12:30 Lunch
14:00 MacKay (1) Introduction to Gaussian processes
15:30 Tea 16:00 Dayan (1) Unsupervised Learning: Modelling probability distributions
17:30 End of session
Wednesday 6 August
09:00 Hinton (1) Almost perfect generalization with almost no labelled training data
10:30 Coffee
11:00 Buhmann (1) Unsupervised learning and clustering
12:30 Spotlight presentations of selected posters
13:00 Lunch
Afternoon free for sightseeing, punting etc.
17:00 Wine reception, with poster contributions from participants
Thursday 7 August
09:00 Tishby (1) Statistical physics and phase transitions in learning and generalization
10:30 Coffee
11:00 Baum (1) MultiAgent Economics and Reinforcement Learning
12:30 Lunch
14:00 Breiman (2) Combining estimators
15:30 Tea
16:00 Freund (1) Introduction to Boosting
17:30 End of session
Friday 8 August
09:00 Neal (2) Monte Carlo methods and their application to Bayesian neural network learning
10:30 Coffee
11:00 Williams (2) Generalization in Gaussian processes
12:30 Lunch
14:00 Tishby (2) Towards a statistical theory of representation in learning
15:30 Tea 16:00 Baum (2) The Economics of Metalearning
17:30 End of session
Saturday 9 August
Free day
Sunday 10 August
Coach tour of Woolsthorpe Manor (the birth place of Isaac Newton) and the town of Lincoln
Monday 11 August
09:00 Jordan (1) Introduction to graphical models I
10:30 Coffee
11:00 Jaakkola (1) Introduction to graphical models II
12:30 Lunch
14:00 Hinton (2) Improving generalization by minimizing the description length of the weights
15:30 Tea
16:00 Dayan (2) Correlations and probabilities in population codes
17:30 End of session
Tuesday 12 August
09:00 Jordan (2) Variational methods for graphical models I
10:30 Coffee
11:00 Jaakkola (2) Variational methods for graphical models I
12:30 Lunch
14:00 Buhmann (2) Active learning
15:30 Tea
16:00 MacKay (2) Information theory, error-correcting codes and belief networks
17:30 Pre-dinner drinks reception in the Isaac Newton Institute
Wednesday 13 August
09:00 Vapnik (1) The statistical nature of learning theory
10:30 Coffee
11:00 Valiant (1) Introduction to Computational Learning Theory
12:30 Lunch
14:00 Le Cun (2) Gradient descent dynamics and generalization
15:30 Tea
16:00 Ripley (1) Statistical theories of model fitting
17:30 End of session
Thursday 14 August
09:00 Sontag (1) The VC and related dimensions for static neural networks
10:30 Coffee
11:00 Valiant (2) Recent developments in learning theory
12:30 Lunch
14:00 Ripley (2) Are uniform convergence results practically relevant?
15:30 Tea
16:00 Freund (2) On-line sequence prediction
17.30: Conference dinner in Corpus Christi College
Friday 15 August
09:00 Sontag (2) The VC and related dimensions for dynamic neural networks
10:30 Coffee
11:00 Vapnik (2) Support vector networks
12:30 Lunch
14:00 Panel: panel discussion and wrap-up
15:00 End of workshop