arXiv subjects

Eran Yahav

Publications and source records attributed to Eran Yahav.

21 records · Page 2Linked to original sources

arXiv2017

Learning Disjunctions of Predicates

Let $F$ be a set of boolean functions. We present an algorithm for learning $F_\vee := \{\vee_{f\in S} f \mid S \subseteq F\}$ from membership queries. Our algorithm asks at most $|F| \cdot OPT(F_\vee)$ membership queries where $OPT(F_\vee)$ is the minimum worst case number of membership queries for learning $F_\vee$. When $F$ is a set of halfspaces over a constant dimension space or a set of variable inequalities, our algorithm runs in polynomial time. The problem we address has practical importance in the field of program synthesis, where the goal is to synthesize a program that meets some requirements. Program synthesis has become popular especially in settings aiming to help end users. In such settings, the requirements are not provided upfront and the synthesizer can only learn them by posing membership queries to the end user. Our work enables such synthesizers to learn the exact requirements while bounding the number of membership queries.

cs.LG↗

arXiv2016

Optimal Learning of Specifications from Examples

A fundamental challenge in synthesis from examples is designing a learning algorithm that poses the minimal number of questions to an end user while guaranteeing that the target hypothesis is discovered. Such guarantees are practically important because they ensure that end users will not be overburdened with unnecessary questions. We present SPEX -- a learning algorithm that addresses the above challenge. SPEX considers the hypothesis space of formulas over first-order predicates and learns the correct hypothesis by only asking the user simple membership queries for concrete examples. Thus, SPEX is directly applicable to any learning problem that fits its hypothesis space and uses membership queries. SPEX works by iteratively eliminating candidate hypotheses from the space until converging to the target hypothesis. The main idea is to use the implication order between hypotheses to guarantee that in each step the question presented to the user obtains maximal pruning of the space. This problem is particularly challenging when predicates are potentially correlated. To show that SPEX is practically useful, we expressed two rather different applications domains in its framework: learning programs for the domain of technical analysts (stock trading) and learning data structure specifications. The experimental results show that SPEX's optimality guarantee is effective: it drastically reduces the number of questions posed to the user while successfully learning the exact hypothesis.

cs.PL↗

arXiv2014

Exploiting Social Navigation

We present an effective Sybil attack against social location based services. Our attack is based on creating a large number of reputed "bot drivers", and controlling their reported locations using fake GPS reports. We show how this attack can be used to influence social navigation systems by applying it to Waze - a prominent social navigation application used by over 50 million drivers. We show that our attack can fake traffic jams and dramatically influence routing decisions. We present several techniques for preventing the attack, and show that effective mitigation likely requires the use of additional carrier information.

cs.CR↗