Search Results for author:
Found 8 papers, 1 papers with code
Learning to Prune Instances of Steiner Tree Problem in Graphs
In this paper, we use this learning framework on the Steiner tree problem and show that even on this problem, the learning-to-prune framework results in computing near-optimal solutions at a fraction of the time required by commercial ILP solvers.
Learning to Sparsify Travelling Salesman Problem Instances
We demonstrate that our approach can reliably prune a large fraction of the variables in TSP instances from TSPLIB/MATILDA (>85%$) while preserving most of the optimal tour edges.
Towards Quantifying the Distance between Opinions
Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity
Learning fine-grained search space pruning and heuristics for combinatorial optimization
For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99 % of nodes in case of sparse graphs) and still detect almost all of the maximum cliques.
Learning Multi-Stage Sparsification for Maximum Clique Enumeration
Compared to the state-of-the-art heuristics and preprocessing strategies, the advantages of our approach are that (i) it does not require any estimate on the maximum clique size at runtime and (ii) we demonstrate it to be effective also for dense graphs.
Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs
In this paper, we focus on measures that leverage structural properties of the knowledge hierarchy graph to assess the temporal changes.
Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs
We therefore propose the novel notion of an any-k ranking algorithm: for a given time budget, re- turn as many of the top-ranked results as possible.
Social and Information Networks Databases Data Structures and Algorithms
Distributed Entity Disambiguation with Per-Mention Learning
Entity disambiguation, or mapping a phrase to its canonical representation in a knowledge base, is a fundamental step in many natural language processing applications.
