Isaac Newton Institute for Mathematical Sciences

Neural Networks and Machine Learning

July - December 1997

Organisers: C M Bishop (Aston), D Haussler (UCSC), G E Hinton (Toronto), M Niranjan (Cambridge), L G Valiant (Harvard)

A NATO Advanced Study Institute

GENERALIZATION IN NEURAL NETWORKS AND MACHINE LEARNING

4 - 15 August 1997

At the Newton Institute, Cambridge, UK

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)

Programme:

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

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