6 citations · 6 across the 7 of their papers we have counts for
8 papers
How hard is learning to cut? Trade-offs and sample complexity
Sammy Khalife, Andrea Lodi
In the recent years, branch-and-cut algorithms have been the target of data-driven approaches designed to enhance the decision making in different phases of the algorithm such as b…
Is uniform expressivity too restrictive? Towards efficient expressivity of graph neural networks
Sammy Khalife, Josué Tonelli-Cueto
Uniform expressivity guarantees that a Graph Neural Network (GNN) can express a query without the parameters depending on the size of the input graphs. This property is desirable i…
Sequence graphs realizations and ambiguity in language models
Sammy Khalife, Yann Ponty, Laurent Bulteau
Several popular language models represent local contexts in an input text as bags of words. Such representations are naturally encoded by a sequence graph whose vertices are th…
Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut
Hongyu Cheng, Sammy Khalife, Barbara Fiedorowicz +1
Data-driven algorithm design is a paradigm that uses statistical and machine learning techniques to select from a class of algorithms for a computational problem an algorithm that…
The logic of rational graph neural networks
Sammy Khalife
The expressivity of Graph Neural Networks (GNNs) can be described via appropriate fragments of the first order logic. Any query of the two variable fragment of graded modal logic (…
On the power of graph neural networks and the role of the activation function
Sammy Khalife, Amitabh Basu
In this article we present new results about the expressivity of Graph Neural Networks (GNNs). We prove that for any GNN with piecewise polynomial activations, whose architecture s…
Neural networks with linear threshold activations: structure and algorithms
Sammy Khalife, Hongyu Cheng, Amitabh Basu
In this article we present new results on neural networks with linear threshold activation functions. We precisely characterize the class of functions that are representable by suc…
Scalable graph-based individual named entity identification
Sammy Khalife, Michalis Vazirgiannis
Named entity discovery (NED) is an important information retrieval problem that can be decomposed into two sub-problems. The first sub-problem, named entity recognition (NER), aims…