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COLT
2007
Springer
13 years 10 months ago
Bounded Parameter Markov Decision Processes with Average Reward Criterion
Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, t...
Ambuj Tewari, Peter L. Bartlett
COLT
2007
Springer
13 years 10 months ago
Occam's Hammer
Abstract. We establish a generic theoretical tool to construct probabilistic bounds for algorithms where the output is a subset of objects from an initial pool of candidates (or mo...
Gilles Blanchard, François Fleuret
COLT
2007
Springer
13 years 10 months ago
Sparse Density Estimation with l1 Penalties
Florentina Bunea, Alexandre B. Tsybakov, Marten H....
COLT
2007
Springer
13 years 10 months ago
Mitotic Classes
For the natural notion of splitting classes into two disjoint subclasses via a recursive classifier working on texts, the question is addressed how these splittings can look in th...
Sanjay Jain, Frank Stephan
COLT
2007
Springer
13 years 10 months ago
Learning Large-Alphabet and Analog Circuits with Value Injection Queries
Abstract. We consider the problem of learning an acyclic discrete circuit with n wires, fan-in bounded by k and alphabet size s using value injection queries. For the class of tran...
Dana Angluin, James Aspnes, Jiang Chen, Lev Reyzin
COLT
2007
Springer
13 years 10 months ago
Sketching Information Divergences
When comparing discrete probability distributions, natural measures of similarity are not p distances but rather are informationdivergences such as Kullback-Leibler and Hellinger. ...
Sudipto Guha, Piotr Indyk, Andrew McGregor
COLT
2007
Springer
13 years 10 months ago
On-Line Estimation with the Multivariate Gaussian Distribution
We consider on-line density estimation with the multivariate Gaussian distribution. In each of a sequence of trials, the learner must posit a mean µ and covariance Σ; the learner...
Sanjoy Dasgupta, Daniel Hsu
COLT
2007
Springer
13 years 10 months ago
Property Testing: A Learning Theory Perspective
Property testing deals with tasks where the goal is to distinguish between the case that an object (e.g., function or graph) has a prespecified property (e.g., the function is li...
Dana Ron