(Apologies if you receive this message more than once)
Registration is now open for
COLT 2001: The Fourteenth Annual Conference on Computational Learning Theory
held jointly with
EUROCOLT 2001: The Fifth European Conference on Computational Learning Theory
Trippenhuis, Amsterdam, the Netherlands, July 16 - July 19, 2001
To register, please follow the directions at
www.learningtheory.org/colt2001/
Please note that the EARLY REGISTRATION DEADLINE is indeed VERY early:
May 25th, 2001. We advise people to register early, since,
unfortunately
*If you register after that date, we cannot guarantee accommodation*
Finding accommodation yourself is not easy, since hotels tend to fill
up very quickly during summer in Amsterdam.
Attached is a list of accepted papers for (EURO-) COLT 2001. We hope
to see you all in Amsterdam this summer!
Peter Grunwald
Paul Vitanyi
local co-chairs
----------------------------------------------------
Invited Talk:
Toward a computational theory of data acquisition
by David G. Stork, Chief Scientist, Ricoh California Research Center
Accepted Papers:
Robust Learning -- Rich and Poor
by John Case, Sanjay Jain, Frank Stephan and Rolf Wiehagen
Strong Entropy Concentration, Game Theory and Algorithmic Randomness
by Peter Grünwald
Limitations of Learning Via Embeddings in Euclidean Half-Spaces
by Shai Ben-David, Nadav Eiron and Hans Ulrich Simon
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
by Peter Bartlett and Shahar Mendelson
Tracking a Small Set of Modes by Mixing Past Posteriors
by Olivier Bousquet and Manfred K. Warmuth
On Boosting with Optimal Poly-Bounded Distributions
by Nader Bshouty and Dmitry Gavinsky
Data-Dependent Margin-Based Generalization Bounds for Classification
by Balazs Kegl, Tamas Linder and Gabor Lugosi
On the Synthesis of Strategies Identifying Recursive Functions
by Sandra Zilles
Adaptive Strategies and Regret Minimization in arbitrarily varying
Markov Environments
by Shie Mannor and Nahum Shimkin
Smooth Boosting an Learning with Malicious Noise
by Rocco A. Servedio
Discrete Prediction Games with Arbitrary Feedback and Loss
by Antonio Piccolboni and Christian Schindelhauer
Intrinsic complexity of learning geometrical concepts from positive data
by Sanjay Jain and Efim Kimber
Estimating the optimal Margins of Embeddings in Euclidean Half Spaces
by Jürgen Forster, Niels Schmitt and Hans Ulrich Simon
Potential-based Algorithms in On-line Prediction and Game Theory
By Nicolo Cesa-Bianchi and Gabor Lugosi
On Learning Monotone DNF under Product
by Rocco A. Servedio
On Using Extended Statistical Queries to avoid Membership Queries
by Nadar h. Bshouty and Vitaly Feldman
Efficiently approximating Weighted Sums with Exponentially Many Terms
by Deepak Chawla, Lin Li and Stephen Scott
A Theoretical analysis of Query Selection for collaborative Filtering
by Wee Sun Lee and Philip M. Long
Geometric Bounds for Generalization in Boosting
by Shie Mannor and Ron Meir
Radial Basis Function Neural Networks Have Superlinear VC Dimension
by Michael Schmitt
Learning additive models online with fast evaluating kernels
by Mark Herbster
How Many Queries are Needed to learn One Bit of Information?
by Hans-Ulrich Simon
Learning Relatively Small Classes
by Shahar Mendelson
Learning rates for Q-Learning
by Eyal Even-Darand, Yishay Mansour
A General Dimension for Exact Learning
by Jose L. Balcazar, Jorge Castro and David Guijarro
On Agnostic Learning with {0, *, 1}-valued and Real-valued Hypotheses
by Philip M. Long
Learning Regular Sets with an Incomplete Membership Oracle
by Nader Bshouty and Avi Owshanko
A Generalized Representer Theorem
by Bernhard Schölkopf, Ralf Herbrich and Alex J. Smola
Geometric methods in the analysis of Glivenko-Cantelli classes.
by Shahar Mendelson
A Leave-one-out Validation Bound for Kernel Methods with Applications
in Learning
by Tong Zhang
Pattern recognition and density estimation under the general iid
assumption
by Ilia Nouretdinov, Volodya Vovk, Michael Vyugin and Alex Gammerman
Bounds on sample size for policy evaluation in Markov environments
by Leonid Peshkin and Sayan Mukherjee
Koby Crammer and Yoram Singer, Ultraconservative Online Algorithms for
Multiclass Problems
Paul Goldberg, When can Two Unsupervised Learners Achieve PAC
Separation?
Further Explanation of the Effectiveness of Voting Methods: The Game
Between Margins and Weights
by Vladimir Koltchinskii, Dmitry Panchenko and Fernando Lozano
Learning Monotone DNF From a teacher that almost does not answer
membership Queries
by Nadar Bshouty and Nadav Eiron
A Sequential Approximation Bound for Some Sample-Dependent Convex
Optimization Problems with Applications in Learning
by Tong Zhang
Optimizing Average Reward Using Discounted Rewards
by Sham Kakade
Estimating a Boolean perceptron from its Average Satisfying Assignment:
A bound on the precision required
by Paul Goldberg
Agnostic Boosting
by Shai Ben-David, Philip M. Long and Yishay Mansour
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