Jorge A. Pérez

Andreia Mordido, Jorge A. Pérez

We tackle the problem of statically ensuring that message-passing programs never run into deadlocks. We focus on concurrent functional programs governed by context-free session types, which can express rich tree-like structures not expressible in standard session types. We propose a new type system based on context-free session types: it enforces both protocol conformance and deadlock freedom, also for programs implementing cyclic communication topologies with recursion and polymorphism. We show how the priority-based approach to deadlock freedom can be extended to this expressive setting. We prove that well-typed concurrent programs respect their protocols and never deadlock.

Pablo Barceló, Mikaël Monet, Jorge Pérez et al.

In spite of several claims stating that some models are more interpretable than others -- e.g., "linear models are more interpretable than deep neural networks" -- we still lack a principled notion of interpretability to formally compare among different classes of models. We make a step towards such a notion by studying whether folklore interpretability claims have a correlate in terms of computational complexity theory. We focus on local post-hoc explainability queries that, intuitively, attempt to answer why individual inputs are classified in a certain way by a given model. In a nutshell, we say that a class $\mathcal{C}_1$ of models is more interpretable than another class $\mathcal{C}_2$, if the computational complexity of answering post-hoc queries for models in $\mathcal{C}_2$ is higher than for those in $\mathcal{C}_1$. We prove that this notion provides a good theoretical counterpart to current beliefs on the interpretability of models; in particular, we show that under our definition and assuming standard complexity-theoretical assumptions (such as P$\neq$NP), both linear and tree-based models are strictly more interpretable than neural networks. Our complexity analysis, however, does not provide a clear-cut difference between linear and tree-based models, as we obtain different results depending on the particular post-hoc explanations considered. Finally, by applying a finer complexity analysis based on parameterized complexity, we are able to prove a theoretical result suggesting that shallow neural networks are more interpretable than deeper ones.

Ilaria Castellani, Mariangiola Dezani-Ciancaglini, Jorge A. Pérez

We present initial results on a comprehensive model of structured communications, in which self- adaptation and security concerns are jointly addressed. More specifically, we propose a model of self-adaptive, multiparty communications with secure information flow guarantees. In this model, security violations occur when processes attempt to read or write messages of inappropriate security levels within directed exchanges. Such violations trigger adaptation mechanisms that prevent the violations to occur and/or to propagate their effect in the choreography. Our model is equipped with local and global mechanisms for reacting to security violations; type soundness results ensure that global protocols are still correctly executed, while the system adapts itself to preserve security.