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NIPS
2008
13 years 9 months ago
Biasing Approximate Dynamic Programming with a Lower Discount Factor
Most algorithms for solving Markov decision processes rely on a discount factor, which ensures their convergence. It is generally assumed that using an artificially low discount f...
Marek Petrik, Bruno Scherrer
JMLR
2008
159views more  JMLR 2008»
13 years 7 months ago
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to ...
Andreas Krause, Ajit Paul Singh, Carlos Guestrin
SPAA
2004
ACM
14 years 1 months ago
Lower bounds for graph embeddings and combinatorial preconditioners
Given a general graph G, a fundamental problem is to find a spanning tree H that best approximates G by some measure. Often this measure is some combination of the congestion and...
Gary L. Miller, Peter C. Richter
COCO
2009
Springer
131views Algorithms» more  COCO 2009»
14 years 2 months ago
A Multi-Round Communication Lower Bound for Gap Hamming and Some Consequences
—The Gap-Hamming-Distance problem arose in the context of proving space lower bounds for a number of key problems in the data stream model. In this problem, Alice and Bob have to...
Joshua Brody, Amit Chakrabarti
COMPGEOM
2006
ACM
14 years 1 months ago
Lower bounds on locality sensitive hashing
Given a metric space (X, dX), c ≥ 1, r > 0, and p, q ∈ [0, 1], a distribution over mappings H : X → N is called a (r, cr, p, q)-sensitive hash family if any two points in...
Rajeev Motwani, Assaf Naor, Rina Panigrahy