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Found 12 papers, 2 papers with code
Approximation Algorithms for Preference Aggregation Using CP-Nets
We first analyze a trivial 2-approximation algorithm that simply outputs the best of the given input preferences, and establish a structural condition under which the approximation ratio of this algorithm is improved to $4/3$.
A Labelled Sample Compression Scheme of Size at Most Quadratic in the VC Dimension
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.
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning
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.
On the Complexity of Symbolic Finite-State Automata
We revisit the complexity of procedures on SFAs (such as intersection, emptiness, etc.)
Optimal Collusion-Free Teaching
In addition to formulating an optimal model of collusion-free teaching, our main results are on the computational complexity of deciding whether $\mathrm{NCTD}^+(\mathcal{C})=k$ (or $\mathrm{NCTD}(\mathcal{C})=k$) for given $\mathcal{C}$ and $k$.
An Overview of Machine Teaching
In this paper we try to organize machine teaching as a coherent set of ideas.
The Complexity of Learning Acyclic Conditional Preference Networks
To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of classes of CP-nets, it is helpful to calculate certain learning-theoretic information complexity parameters.
An Empirical Study of the Effects of Spurious Transitions on Abstraction-based Heuristics
However, the quality of abstraction-based heuristic functions, and thus the speed of search, can suffer from spurious transitions, i. e., state transitions in the abstract state space for which no corresponding transitions in the reachable component of the original state space exist.
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions.
Preference-based Teaching
We introduce a new model of teaching named "preference-based teaching" and a corresponding complexity parameter---the preference-based teaching dimension (PBTD)---representing the worst-case number of examples needed to teach any concept in a given concept class.
Interactive Learning from Multiple Noisy Labels
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 supervised learning.
Combining Models of Approximation with Partial Learning
Here three variants of approximate learning will be introduced and investigated with respect to the question whether they can be combined with partial learning.
