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Found 4 papers, 1 papers with code
Learning temporal formulas from examples is hard
We study the problem of learning linear temporal logic (LTL) formulas from examples, as a first step towards expressing a property separating positive and negative instances in a way that is comprehensible for humans.
Scaling Neural Program Synthesis with Distribution-based Search
We investigate how to augment probabilistic and neural program synthesis methods with new search algorithms, proposing a framework called distribution-based search.
The Complexity of Learning Linear Temporal Formulas from Examples
In this paper we initiate the study of the computational complexity of learning linear temporal logic (LTL) formulas from examples.
On Efficient Low Distortion Ultrametric Embedding
In this paper, we provide a new algorithm which takes as input a set of points $P$ in $\mathbb{R}^d$, and for every $c\ge 1$, runs in time $n^{1+\frac{\rho}{c^2}}$ (for some universal constant $\rho>1$) to output an ultrametric $\Delta$ such that for any two points $u, v$ in $P$, we have $\Delta(u, v)$ is within a multiplicative factor of $5c$ to the distance between $u$ and $v$ in the "best" ultrametric representation of $P$.
