| Links' Seminars and Public Events |   |
| 2024 | |
|---|---|
Fri 7th Jun 10:00 am 11:00 am | Séminaire Sam Van Gool dualité de Stone |
Thu 30th May to Fri 31st May all day | Pysemigroup Hackaton |
Fri 24th May 11:00 am 11:30 am | Séminaire Sophie Tison Speaker: Sophie Tison Title: Containment of Regular Path Queries Under Constraints |
Thu 16th May 2:00 pm 4:00 pm | Seminar Arkaprava Title: Efficient Optimization of Network Metrics in Large Uncertain Graphs Abstract: Graphs constitute an omnipresent data structure that can model objects and their relationships in a wide variety of real-world scenarios. The optimization of network metrics finds use in a plethora of real-world applications. Most of the exact techniques for such tasks turn out to be prohibitively time-consuming and memory-intensive for the huge graphs that are usually encountered. Thus, there is a need for efficient approximation algorithms. This talk focuses on the efficient optimization of network metrics in large uncertain graphs, and specifically the following three research problems. The first problem aims to find, between a given pair of nodes in an uncertain graph, the path having the highest probability of being a shortest path. The second problem aims to find, in an uncertain graph, the subgraph having the highest probability of being densest. The third problem is a novel variant of the well-known opinion maximization problem where, given a social network of users with real-valued opinions (about different candidates), the goal is to choose the top-k seed users maximizing a specific voting-based score at a given finite time horizon. Best Regards, Arkaprava "Lieu : Lille, Salle : B11" |
Fri 19th Apr 11:00 am 12:00 pm | Seminar Pierre Lermusiaux Speaker: Pierre Lermusiaux (plermusi.github.io/) Title: Detection of Uncaught Exceptions in Functional Programs by Abstract Interpretation Abstract: Exception handling is a key feature in modern programming languages. Exceptions can be used to deal with errors, or as a means to control the flow of execution of a program. Since they might unexpectedly terminate a program, unhandled exceptions are a serious safety concern. We propose a static analysis to detect uncaught exceptions in functional programs, that is defined as an abstract interpreter. It computes a description of the values potentially returned by a program using a novel abstract domain, that can express inductively defined sets of values. Simultaneously, the analysis infers the possibly raised exceptions, by computing in the abstract exception monad. This abstract interpreter has been implemented as an effective static analyser for a large subset of OCaml programs, that supports mutable data types, the OCaml module system, and dynamically extensible data types such as the exception type. The analyser has been evaluated on several hundreds of OCaml programs. |
Fri 5th Apr 10:30 am 11:30 am | Séminaire Guillaume Lagarde Titre: Scaling Neural Program Synthesis with Distribution-based Search Abstract: In this talk, we will discuss the problem of automatically constructing computer programs from input-output examples, especially when the target language is domain-specific and defined using a context-free grammar. I will introduce a theoretical framework called distribution-based search, discuss its challenges, and present several search strategies based on learning the weights of a probabilistic context-free grammar (PCFG) and then using this PCFG to enumerate the most promising candidate programs efficiently. The presentation will be based on the following paper published at AAAI'2022: arxiv.org/abs/2110.12485 Joint work with Nathanaël Fijalkow, Théo Matricon, Kevin Ellis, Pierre Ohlmann, Akarsh Potta |
