102 citations · 136 across the 12 of their papers we have counts for
6 papers · 1 filter
A Labelled Sample Compression Scheme of Size at Most Quadratic in the VC Dimension
Farnam Mansouri, Sandra Zilles
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-Cherv…
Optimal Collusion-Free Teaching
David Kirkpatrick, Hans U. Simon, Sandra Zilles
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 Mat…
An Overview of Machine Teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles +1
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 proble…
Preference-based Teaching
Ziyuan Gao, Christoph Ries, Hans Ulrich Simon +1
We introduce a new model of teaching named "preference-based teaching" and a corresponding complexity parameter---the preference-based teaching dimension (PBTD)---representing the…
Interactive Learning from Multiple Noisy Labels
Shankar Vembu, Sandra Zilles
Interactive learning is a process in which a machine learning algorithm is provided with meaningful, well-chosen examples as opposed to randomly chosen examples typical in standard…
Combining Models of Approximation with Partial Learning
Ziyuan Gao, Frank Stephan, Sandra Zilles
In Gold's framework of inductive inference, the model of partial learning requires the learner to output exactly one correct index for the target object and only the target object…