default search action
22nd COLT 2009: Montreal, Quebec, Canada
- COLT 2009 - The 22nd Conference on Learning Theory, Montreal, Quebec, Canada, June 18-21, 2009. 2009
Algorithms I
- Adam Tauman Kalai, Ravi Sastry:
The Isotron Algorithm: High-Dimensional Isotonic Regression. - Nader H. Bshouty, Philip M. Long:
Linear Classifiers are Nearly Optimal When Hidden Variables Have Diverse Effect. - Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh:
Domain Adaptation: Learning Bounds and Algorithms. - Nader H. Bshouty:
Optimal Algorithms for the Coin Weighing Problem with a Spring Scale.
Online Learning I
- Eyal Even-Dar, Robert Kleinberg, Shie Mannor, Yishay Mansour:
Online Learning for Global Cost Functions. - Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims:
The K-armed Dueling Bandits Problem. - Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz:
Online Multi-task Learning with Hard Constraints.
Sparsity and Algorithms
- Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov, Sara A. van de Geer:
Taking Advantage of Sparsity in Multi-Task Learning. - Arnak S. Dalalyan, Alexandre B. Tsybakov:
Sparse Regression Learning by Aggregation and Langevin Monte-Carlo. - Sivan Sabato, Naftali Tishby:
Homogeneous Multi-Instance Learning with Arbitrary Dependence. - Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
A Spectral Algorithm for Learning Hidden Markov Models.
Generalization I
- Andreas Maurer, Massimiliano Pontil:
Empirical Bernstein Bounds and Sample-Variance Penalization. - Mark D. Reid, Robert C. Williamson:
Generalised Pinsker Inequalities. - Sanjay Jain, Frank Stephan:
Consistent Partial Identification.
Algorithms II
- Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale:
Fast and Optimal Prediction on a Labeled Tree. - Mark Herbster, Guy Lever:
Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation. - Maria-Florina Balcan, Mark Braverman:
Finding Low Error Clusterings. - Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Stochastic Convex Optimization.
Dimensionality and Optimization
- Samory Kpotufe:
Escaping the Curse of Dimensionality with a Tree-based Regressor. - Hariharan Narayanan, Partha Niyogi:
On the Sample Complexity of Learning Smooth Cuts on a Manifold. - Hans Ulrich Simon, Nikolas List:
SVM-Optimization and Steepest-Descent Line Search.
Bandits
- Jean-Yves Audibert, Sébastien Bubeck:
Minimax Policies for Adversarial and Stochastic Bandits. - H. Brendan McMahan, Matthew J. Streeter:
Tighter Bounds for Multi-Armed Bandits with Expert Advice. - Nicolò Cesa-Bianchi, Gábor Lugosi:
Combinatorial Bandits. - Jacob D. Abernethy, Alexander Rakhlin:
Beating the Adaptive Bandit with High Probability. - Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality.
Complexity I
- Jeffrey C. Jackson, Karl Wimmer:
New Results for Random Walk Learning. - Vitaly Feldman:
Robustness of Evolvability. - Hayato Kobayashi, Ayumi Shinohara:
Complexity of Teaching by a Restricted Number of Examples. - Luis Rademacher, Navin Goyal:
Learning Convex Bodies is Hard.
Noise
- Adam Tauman Kalai, Varun Kanade, Yishay Mansour:
Reliable Agnostic Learning. - Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz:
Agnostic Online Learning. - Alessandro Lazaric, Rémi Munos:
Hybrid Stochastic-Adversarial On-line Learning.
Active Learning and Stability
- Eric Friedman:
Active Learning for Smooth Problems. - Steve Hanneke:
Adaptive Rates of Convergence in Active Learning. - Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability and Stability in the General Learning Setting. - Ofer Dekel, Ohad Shamir:
Vox Populi: Collecting High-Quality Labels from a Crowd.
Generalization II
- Ingo Steinwart, Don R. Hush, Clint Scovel:
Optimal Rates for Regularized Least Squares Regression. - Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito:
A Note on Learning with Integral Operators. - Yiming Ying, Colin Campbell:
Generalization Bounds for Learning the Kernel Problem.
Open Problems
- Jacob D. Abernethy, Alexander Rakhlin:
An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?. - Maria-Florina Balcan:
Better Guarantees for Sparsest Cut Clustering. - Jacob D. Abernethy, Manfred K. Warmuth:
Minimax Games with Bandits. - Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
The Complexity of Improperly Learning Large Margin Halfspaces.
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-12-15 01:15 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: