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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 8 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
13 years 7 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
ICFP
2009
ACM
16 years 5 months ago
Parallel concurrent ML
Concurrent ML (CML) is a high-level message-passing language that supports the construction of first-class synchronous abstractions called events. This mechanism has proven quite ...
John H. Reppy, Claudio V. Russo, Yingqi Xiao
PEPM
2009
ACM
15 years 9 months ago
Translation and optimization for a core calculus with exceptions
A requirement of any source language is to be rich in features and concise to use by the programmers. As a drawback, it is often too complex to analyse, causing research studies t...
Cristina David, Cristian Gherghina, Wei-Ngan Chin
PLDI
2011
ACM
14 years 7 months ago
Language-independent sandboxing of just-in-time compilation and self-modifying code
When dealing with dynamic, untrusted content, such as on the Web, software behavior must be sandboxed, typically through use of a language like JavaScript. However, even for such ...
Jason Ansel, Petr Marchenko, Úlfar Erlingss...