COLT 2007 - PDF Preprint Papers

COLT 2007 Annual Workshop on Computational Learning Theory

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COLT
2007
Springer

174views Machine Learning» more COLT 2007»

Regret to the Best vs. Regret to the Average

14 years 5 months ago

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Abstract. We study online regret minimization algorithms in a bicriteria setting, examining not only the standard notion of regret to the best expert, but also the regret to the av...

Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, ...

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Id	Views	Title	Status

1	174	Regret to the Best vs. Regret to the Average	claim
2	172	An Efficient Re-scaled Perceptron Algorithm for Conic Systems	claim
3	159	On-Line Estimation with the Multivariate Gaussian Distribution	claim
4	146	How Good Is a Kernel When Used as a Similarity Measure?	claim
5	144	Improved Rates for the Stochastic Continuum-Armed Bandit Problem	claim
6	143	Bounded Parameter Markov Decision Processes with Average Reward Criterion	claim
7	142	Multi-view Regression Via Canonical Correlation Analysis	claim
8	135	Sketching Information Divergences	claim
9	135	Aggregation by Exponential Weighting and Sharp Oracle Inequalities	claim
10	130	Multitask Learning with Expert Advice	claim
11	129	Stability of k -Means Clustering	claim
12	129	Mind Change Optimal Learning of Bayes Net Structure	claim
13	128	Rademacher Margin Complexity	claim
14	120	Online Learning with Prior Knowledge	claim
15	118	Property Testing: A Learning Theory Perspective	claim
16	118	Learning Large-Alphabet and Analog Circuits with Value Injection Queries	claim
17	118	Transductive Rademacher Complexity and Its Applications	claim
18	117	A Lower Bound for Agnostically Learning Disjunctions	claim
19	117	Resource-Bounded Information Gathering for Correlation Clustering	claim
20	114	Margin Based Active Learning	claim
21	113	Sparse Density Estimation with l1 Penalties	claim
22	113	Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking	claim
23	112	Learning Languages with Rational Kernels	claim
24	112	When Is There a Free Matrix Lunch?	claim
25	108	Learning Nested Halfspaces and Uphill Decision Trees	claim
26	108	Minimax Bounds for Active Learning	claim
27	107	Teaching Dimension and the Complexity of Active Learning	claim
28	104	Observational Learning in Random Networks	claim
29	103	Strategies for Prediction Under Imperfect Monitoring	claim
30	100	Learning Correction Grammars	claim
31	100	Suboptimality of Penalized Empirical Risk Minimization in Classification	claim
32	99	Mitotic Classes	claim
33	99	Robust Reductions from Ranking to Classification	claim
34	98	Learning Permutations with Exponential Weights	claim
35	97	Occam's Hammer	claim
36	97	Generalised Entropy and Asymptotic Complexities of Languages	claim
37	94	U-Shaped, Iterative, and Iterative-with-Counter Learning	claim
38	87	The Loss Rank Principle for Model Selection	claim
39	83	Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector	claim

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