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ICML
2010
IEEE
13 years 10 months ago
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is ...
Niranjan Srinivas, Andreas Krause, Sham Kakade, Ma...
ICRA
2010
IEEE
142views Robotics» more  ICRA 2010»
13 years 7 months ago
"Architectural Robotics": An interdisciplinary course rethinking the machines we live in
Abstract— We discuss disciplinary barriers which have traditionally prevented robotics from significantly impacting the built (architectural) environment we inhabit. Specifical...
Apoorva Kapadia, Ian D. Walker, Keith Evan Green, ...
COLT
2008
Springer
13 years 11 months ago
Regret Bounds for Sleeping Experts and Bandits
We study on-line decision problems where the set of actions that are available to the decision algorithm vary over time. With a few notable exceptions, such problems remained larg...
Robert D. Kleinberg, Alexandru Niculescu-Mizil, Yo...
ICANNGA
2009
Springer
212views Algorithms» more  ICANNGA 2009»
14 years 3 months ago
Evolutionary Regression Modeling with Active Learning: An Application to Rainfall Runoff Modeling
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
Ivo Couckuyt, Dirk Gorissen, Hamed Rouhani, Eric L...
ICML
2005
IEEE
14 years 10 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III