(EURO-) COLT 2001 Presentations

 

Accepted Papers

John Case,  Sanjay Jain, Frank Stephan and Rolf Wiehagen,
   Robust Learning -- Rich and Poor

Peter Grünwald, Strong Entropy Concentration, Game Theory and Algorithmic
    Randomness

Shai Ben-David, Nadav Eiron and Hans Ulrich Simon , Limitations of Learning
    Via Embeddings in Euclidean Half-Spaces

Peter Bartlett and Shahar Mendelson, Rademacher and Gaussian Complexities:
    Risk Bounds and Structural Results

Olivier Bousquet and Manfred K. Warmuth, Tracking a Small Set of Modes by
    Mixing Past Posteriors

Nader Bshouty and Dmitry Gavinsky, On Boosting with Optimal Poly-Bounded
    Distributions

Balazs Kegl, Tamas Linder and Gabor Lugosi, Data-Dependent Margin-Based
    Generalization Bounds for Classification

Sandra Zilles, On the Synthesis of Strategies Identifying Recursive Functions

Shie Mannor and Nahum Shimkin, Adaptive Strategies and Regret Minimization
    in arbitrarily varying Markov Environments

Rocco A. Servedio, Smooth Boosting an Learning with Malicious Noise

Antonio Piccolboni and Christian Schindelhauer, Discrete Prediction Games
    with Arbitrary Feedback and Loss

Sanjay Jain and Efim Kinber, Intrinsic complexity of learning geometrical
   concepts from positive data

Jürgen Forster and Niels Schmitt and Hans Ulrich Simon, Estimating the
   optimal Margins of Embeddings in Euclidean Half Spaces

Nicolo Cesa-Bianchi and Gabor Lugosi, Potential-based Algorithms in On-line
   Prediction and Game Theory

Rocco A. Servedio, On Learning Monotone DNF under Product

Nadar h. Bshouty and Vitaly Feldman, On Using Extended Statistical Queries
    to avoid Membership Queries

Deepak Chawla, Lin Li and Stephen Scott, Efficiently approximating Weighted
    Sums with Exponentially Many Terms

Wee Sun Lee and Philip M. Long, A Theoretical analysis of Query Selection
    for collaborative Filtering

Shie Mannor and Ron Meir, Geometric Bounds for Generalization in Boosting

Michael Schmitt, Radial Basis Function Neural Networks Have Superlinear VC
    Dimension

Mark Herbster, Learning additive models online with fast evaluating kernels

Hans-Ulrich Simon, How Many Queries are Needed to learn One Bit of
    Information?

Shahar Mendelson, Learning Relatively Small Classes

Eyal Even-Darand Yishay Mansour, Learning rates for Q-Learning

Jose L. Balcazar, Jorge Castro and David Guijarro, A General Dimension for
    Exact Learning

Philip M. Long, On Agnostic Learning with {0, *, 1}-valued and Real-valued
    Hypotheses

Nader Bshouty and Avi Owshanko, Learning Regular Sets with an Incomplete
    Membership Oracle

Bernhard Schölkopf, Ralf Herbrich and Alex J. Smola, A Generalized
    Representer Theorem

Shahar Mendelson, Geometric methods in the analysis of Glivenko-Cantelli classes

Tong Zhang, A Leave-one-out Validation Bound for Kernel Methods with
    Applications in Learning

Ilia Nouretdinov, Volodya Vovk, Michael Vyugin and Alex Gammerman, Pattern
    recognition and density estimation under the general iid assumption

Leonid Peshkin and Sayan Mukherjee, Bounds on sample size for policy
    evaluation in Markov environments

Koby Crammer and Yoram Singer, Ultraconservative Online Algorithms for
    Multiclass Problems

Paul Goldberg, When can Two Unsupervised Learners Achieve PAC Separation?

Vladimir Koltchinskii, Dmitry Panchenko and Fernando Lozano, Further
    Explanation of the Effectiveness of Voting Methods: The Game Between Margins
    and Weights

Nadar Bshouty and Nadav Eiron, Learning Monotone DNF From a teacher that
    almost does not answer membership Queries

Tong Zhang, A Sequential Approximation Bound for Some Sample-Dependent
    Convex Optimization Problems with Applications in Learning

Sham Kakade, Optimizing Average Reward Using Discounted Rewards

Paul Goldberg, Estimating a Boolean perceptron from its Average Satisfying
    Assignment: A bound on the precision required

Shai Ben-David, Philip M. Long and Yishay Mansour, Agnostic Boosting
 
 
 
 

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