A Learner-verifier Framework for Learning and Certifying Neural Controllers in Stochastic Systems

Reinforcement learning (RL) presents a promising approach to solving highly non-linear control tasks that are often challenging for classical control methods. In this talk, we present a learner-verifier framework for solving control tasks in stochastic systems with formal guarantees on reachability, safety or reach-avoidance specifications. Given a specification and a…

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Towards an Automatic Proof of the Bakery Algorithm

The Bakery algorithm is a landmark algorithm for ensuring mutual exclusion among N processes that communicate via shared variables. Starting from existing TLA+ specifications, we use the recently developed IC3PO parameterized model checker to automatically compute inductive invariants for two versions of the algorithm. We compare the machine-generated invariants with…

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Computable Functions in Anonymous Networks

In this talk, I present several computability results in anonymous networks with broadcast communications. First, I recall the characterization, given by Hendrickx and Tsitsiklis, of the computable functions when agents have no information on their outgoing neighborhoods. Then I give the characterization of computable functions in networks with either (a)…

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Distributed Decision Problems: Concurrent Specifications beyond Binary Relations

Much discussion exists about what is computation, but less about what is a computational problem. In the sequential setting, Turing’s definition of computational problem was based on functions. In a concurrent setting, various notions of distributed problems have been used, under different models of computation, ranging from models equivalent to…

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A generic framework to coarse-grain stochastic reaction networks by Abstract Interpretation

In the last decades, logical or discrete models have emerged as a successful paradigm for capturing and predicting the behaviors of systems of molecular interactions. Intuitively, they consist in sampling the abundance of each kind of biochemical entity within finite sets of intervals and deriving transitions accordingly. On one hand,…

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Asymptotically Tight Bounds on the Time Complexity of Broadcast and its Variants in Dynamic Networks

Data dissemination is a fundamental task in distributed computing. This talk will explore broadcast problems in various innovative models where the communication network connecting n processes is dynamic (e.g., due to mobility or failures) and controlled by an adversary. In the main model, the processes transitively communicate their ids in…

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Verification and Training of Machine Learning Models by Abstract Interpretation

The aim of this thesis is to explore and develop the application of abstract interpretation techniques to the context of machine learning. While big data and artificial intelligence have become widespread technologies, the lack of formal verification systems represents a serious limitation. Machine learning models can be very accurate but…

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Sound Symbolic Execution via Abstract Interpretation and Its Application to Security

Symbolic execution is a program analysis technique commonly utilized to determine whether programs violate properties and, in case violations are found, to generate inputs that can trigger them. Used in the context of security properties such as noninterference, symbolic execution is precise when looking for counter-example pairs of traces when insecure information…

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Abstract Interpretation-Based Feature Importance for SVMs

In this talk, we will discuss a symbolic representation for support vector machines (SVMs) by means of abstract interpretation. We leverage this abstraction in two ways: (1) to enhance the interpretability of SVMs by deriving a novel feature importance measure called abstract feature importance (AFI), that does not depend in any way on a given dataset of…

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Towards verified inference in probabilistic programs

Probabilistic programming languages aim at designing and implementing algorithms that compute over probability distributions rather than states. They feature dedicated primitives for sampling and conditioning. Moreover, the evaluation of probabilistic programs relies on general purpose inference engines. In the last few years, several languages such as Pyro, Edward or Stan…

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Verifying Asynchronous JavaScript: a Backward Goal-Directed Approach

Asynchronous code is remarkably hard to get right, making it a common source of errors in TypeScript code. However, we would not be able to write TypeScript applications without it, because it enables long-running actions without freezing the current view. Developers usually rely on manual inspection to understand it, which is quite…

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Réduction exacte de modèles biochimiques de signalisation intercellulaire

Les voies de signalisation intracellulaire impliquent des cascades d’interaction entre protéines. Chaque cellule peut alors recevoir des signaux, les propager jusqu’à son noyau, puis les intégrer, ce qui, in fine, influe sur son comportement global. Les protéines s’associent entre elles  puis modifient la structure spatiale de leurs voisines, ce qui a…

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Tropical Abstraction of Biochemical Reaction Networks with Guarantees

Biochemical molecules interact through modification and binding reactions, giving raise to a combinatorial number of possible biochemical species. The time-dependent evolution of concentrations of the species is commonly described by a system of coupled ordinary differential equations (ODEs). However, the analysis of such high-dimensional, non-linear system of equations is often…

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