Zobrazeno 1 - 10
of 212
pro vyhledávání: '"ZILLES, SANDRA"'
This paper studies the design and analysis of approximation algorithms for aggregating preferences over combinatorial domains, represented using Conditional Preference Networks (CP-nets). Its focus is on aggregating preferences over so-called \emph{s
Externí odkaz:
http://arxiv.org/abs/2312.09162
Autor:
Mansouri, Farnam, Zilles, Sandra
This paper presents a construction of a proper and stable labelled sample compression scheme of size $O(\VCD^2)$ for any finite concept class, where $\VCD$ denotes the Vapnik-Chervonenkis Dimension. The construction is based on a well-known model of
Externí odkaz:
http://arxiv.org/abs/2212.12631
The success of deep active learning hinges on the choice of an effective acquisition function, which ranks not yet labeled data points according to their expected informativeness. Many acquisition functions are (partly) based on the uncertainty that
Externí odkaz:
http://arxiv.org/abs/2206.09798
Publikováno v:
Logical Methods in Computer Science, Volume 19, Issue 2 (April 20, 2023) lmcs:8899
We study the learnability of symbolic finite state automata (SFA), a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results
Externí odkaz:
http://arxiv.org/abs/2112.14252
Publikováno v:
In Theoretical Computer Science 29 July 2024 1007
We revisit the complexity of procedures on SFAs (such as intersection, emptiness, etc.) and analyze them according to the measures we find suitable for symbolic automata: the number of states, the maximal number of transitions exiting a state, and th
Externí odkaz:
http://arxiv.org/abs/2011.05389
Formal models of learning from teachers need to respect certain criteria to avoid collusion. The most commonly accepted notion of collusion-freeness was proposed by Goldman and Mathias (1996), and various teaching models obeying their criterion have
Externí odkaz:
http://arxiv.org/abs/1903.04012
In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is presented as varying along a dimension. The collection of dimensions then form the problem space of machine teaching, such that existing teaching problems can
Externí odkaz:
http://arxiv.org/abs/1801.05927
Learning of user preferences, as represented by, for example, Conditional Preference Networks (CP-nets), has become a core issue in AI research. Recent studies investigate learning of CP-nets from randomly chosen examples or from membership and equiv
Externí odkaz:
http://arxiv.org/abs/1801.03968
The efficient solution of state space search problems is often attempted by guiding search algorithms with heuristics (estimates of the distance from any state to the goal). A popular way for creating heuristic functions is by using an abstract versi
Externí odkaz:
http://arxiv.org/abs/1711.05105