Manipulation-resistant Online Learning by Paul Christiano Doctor of Philosophy in Computer Science University of California, Berkeley Umesh Vazirani, Chair Learning algorithms are now routinely applied to data aggregated from millions of untrusted users, including reviews and feedback that are used to define learning systems’ objectives. If some of these users behave manipulatively, traditional...

We investigate a new paradigm in the context of learning in the limit, namely, learning correction grammars for classes of computably enumerable (c.e.) languages. Knowing a language may feature a representation of it in terms of two grammars. The second grammar is used to make corrections to the first grammar. Such a pair of grammars can be seen as a single description of (or grammar for) the l...

Objectives To compare bystander cardiopulmonary resuscitation skills retention between conventional learning and flipped learning for first-year medical students. Methods A post-test only control group design. A total of 108 participants were randomly assigned to either the conventional learning or flipped learning. The primary outcome measures of time to the first chest compression and the n...

Plunkett & Marchman (1993) showed that a neural network trained on an incrementally expanded training set was able to master the past tense and show the U-shaped learning pattern characteristic of children. In Jackson, Constandse & Cottrell (1996) we argued that Plunkett & Marchman’s restriction of the training set was unrealistic and proposed a model of selective attention that enabled our net...

The following pape r presents the results of a study aimed at a chieving a better u nderstanding of the psychological readiness for mobile learning (m-learning) among Saudi students, while also evaluating m-learning readiness as a whole in Saudi Arabia a topic that ha s not y et received adequate attention from researchers. Data was a cquired via a questionnaire administered to 131 Saudi studen...

BACKGROUND Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the negative training set N (non-disease gene set does not...

Im vorliegenden Beitrag wird ein agentenbasiertes Simulationsmodell vorgestellt, mit dem der Day-Ahead-Strommarkt, der Markt für positive Minutenreserve und der Emissionshandel abgebildet wird. Die Betreiber von Kraftwerken werden als lernende Agenten (mit Q-Learning) abgebildet und konkurrieren auf der Anbieterseite der Strommärkte. Das Modell wurde mit Daten des deutschen Strommarkts simulier...

In the learning parities with noise problem —well-studied in learning theory and cryptography— we have access to an oracle that, each time we press a button, returns a random vector a ∈ GF(2) together with a bit b ∈ GF(2) that was computed as a ·u+η, where u ∈ GF(2) is a secret vector, and η ∈ GF(2) is a noise bit that is 1 with some probability p. Say p = 1/3. The goal is to recover u. This ta...