arXiv subjects

Alexandra Silva

Publications and source records attributed to Alexandra Silva.

At least 55 records · Page 3Linked to original sources

arXiv2023

Deterministic stream-sampling for probabilistic programming: semantics and verification

Probabilistic programming languages rely fundamentally on some notion of sampling, and this is doubly true for probabilistic programming languages which perform Bayesian inference using Monte Carlo techniques. Verifying samplers - proving that they generate samples from the correct distribution - is crucial to the use of probabilistic programming languages for statistical modelling and inference. However, the typical denotational semantics of probabilistic programs is incompatible with deterministic notions of sampling. This is problematic, considering that most statistical inference is performed using pseudorandom number generators. We present a higher-order probabilistic programming language centred on the notion of samplers and sampler operations. We give this language an operational and denotational semantics in terms of continuous maps between topological spaces. Our language also supports discontinuous operations, such as comparisons between reals, by using the type system to track discontinuities. This feature might be of independent interest, for example in the context of differentiable programming. Using this language, we develop tools for the formal verification of sampler correctness. We present an equational calculus to reason about equivalence of samplers, and a sound calculus to prove semantic correctness of samplers, i.e. that a sampler correctly targets a given measure by construction.

cs.PL↗

arXiv2023

A Coalgebraic Approach to Reducing Finitary Automata

Compact representations of automata are important for efficiency. In this paper, we study methods to compute reduced automata, in which no two states accept the same language. We do this for finitary automata (FA), an abstract definition that encompasses probabilistic and weighted automata. Our procedure makes use of Milius' locally finite fixpoint. We present a reduction algorithm that instantiates to probabilistic and S-linear weighted automata (WA) for a large class of semirings. Moreover, we propose a potential connection between properness of a semiring and our provided reduction algorithm for WAs, paving the way for future work in connecting the reduction of automata to the properness of their associated coalgebras.

cs.FL↗

arXiv2023

Outcome Logic: A Unifying Foundation for Correctness and Incorrectness Reasoning

Program logics for bug-finding (such as the recently introduced Incorrectness Logic) have framed correctness and incorrectness as dual concepts requiring different logical foundations. In this paper, we argue that a single unified theory can be used for both correctness and incorrectness reasoning. We present Outcome Logic (OL), a novel generalization of Hoare Logic that is both monadic (to capture computational effects) and monoidal (to reason about outcomes and reachability). OL expresses true positive bugs, while retaining correctness reasoning abilities as well. To formalize the applicability of OL to both correctness and incorrectness, we prove that any false OL specification can be disproven in OL itself. We also use our framework to reason about new types of incorrectness in nondeterministic and probabilistic programs. Given these advances, we advocate for OL as a new foundational theory of correctness and incorrectness.

cs.LO↗

arXiv2023

Guarded Kleene Algebra with Tests: Automata Learning

Guarded Kleene Algebra with Tests (GKAT) is the fragment of Kleene Algebra with Tests (KAT) that arises by replacing the union and iteration operations of KAT with predicate-guarded variants. GKAT is more efficiently decidable than KAT and expressive enough to model simple imperative programs, making it attractive for applications to e.g. network verification. In this paper, we further explore GKAT's automata theory, and present GL*, an algorithm for learning the GKAT automaton representation of a black-box, by observing its behaviour. A complexity analysis shows that it is more efficient to learn a representation of a GKAT program with GL* than with Angluin's existing L* algorithm. We implement GL* and L* in OCaml and compare their performances on example programs.

cs.FL↗

arXiv2022

Tree-Based Adaptive Model Learning

We extend the Kearns-Vazirani learning algorithm to be able to handle systems that change over time. We present a new learning algorithm that can reuse and update previously learned behavior, implement it in the LearnLib library, and evaluate it on large examples, to which we make small adjustments between two runs of the algorithm. In these experiments our algorithm significantly outperforms both the classic Kearns-Vazirani learning algorithm and the current state-of-the-art adaptive algorithm.

cs.FL↗

arXiv2022

Long-Term Mentoring for Computer Science Researchers

Early in the pandemic, we -- leaders in the research areas of programming languages (PL) and computer architecture (CA) -- realized that we had a problem: the only way to form new lasting connections in the community was to already have lasting connections in the community. Both of our academic communities had wonderful short-term mentoring programs to address this problem, but it was clear that we needed long-term mentoring programs. Those of us in CA approached this scientifically, making an evidence-backed case for community-wide long-term mentoring. In the meantime, one of us in PL had impulsively launched an unofficial long-term mentoring program, founded on chaos and spreadsheets. In January 2021, the latter grew to an official cross-institutional long-term mentoring program called SIGPLAN-M; in January 2022, the former grew to Computer Architecture Long-term Mentoring (CALM). The impacts have been strong: SIGPLAN-M reaches 328 mentees and 234 mentors across 41 countries, and mentees have described it as "life changing" and "a career saver." And while CALM is in its pilot phase -- with 13 mentors and 21 mentees across 7 countries -- it has received very positive feedback. The leaders of SIGPLAN-M and CALM shared our designs, impacts, and challenges along the way. Now, we wish to share those with you. We hope this will kick-start a larger long-term mentoring effort across all of computer science.

cs.CY↗

arXiv2022

Processes Parametrised by an Algebraic Theory

We develop a (co)algebraic framework to study a family of process calculi with monadic branching structures and recursion operators. Our framework features a uniform semantics of process terms and a complete axiomatisation of semantic equivalence. We show that there are uniformly defined fragments of our calculi that capture well-known examples from the literature like regular expressions modulo bisimilarity and guarded Kleene algebra with tests. We also derive new calculi for probabilistic and convex processes with an analogue of Kleene star.

cs.LO↗

arXiv2022

Concurrent NetKAT: Modeling and analyzing stateful, concurrent networks

We introduce Concurrent NetKAT (CNetKAT), an extension of NetKAT with operators for specifying and reasoning about concurrency in scenarios where multiple packets interact through state. We provide a model of the language based on partially-ordered multisets (pomsets), which are a well-established mathematical structure for defining the denotational semantics of concurrent languages. We provide a sound and complete axiomatization of this model, and we illustrate the use of CNetKAT through examples. More generally, CNetKAT can be understood as an algebraic framework for reasoning about programs with both local state (in packets) and global state (in a global store).

cs.PL↗

arXiv2022

A Categorical Framework for Learning Generalised Tree Automata

Automata learning is a popular technique used to automatically construct an automaton model from queries. Much research went into devising ad hoc adaptations of algorithms for different types of automata. The CALF project seeks to unify these using category theory in order to ease correctness proofs and guide the design of new algorithms. In this paper, we extend CALF to cover learning of algebraic structures that may not have a coalgebraic presentation. Furthermore, we provide a detailed algorithmic account of an abstract version of the popular L* algorithm, which was missing from CALF. We instantiate the abstract theory to a large class of Set functors, by which we recover for the first time practical tree automata learning algorithms from an abstract framework and at the same time obtain new algorithms to learn algebras of quotiented polynomial functors.

cs.FL↗

arXiv2022

On Star Expressions and Coalgebraic Completeness Theorems

An open problem posed by Milner asks for a proof that a certain axiomatisation, which Milner showed is sound with respect to bisimilarity for regular expressions, is also complete. One of the main difficulties of the problem is the lack of a full Kleene theorem, since there are automata that can not be specified, up to bisimilarity, by an expression. Grabmayer and Fokkink (2020) characterise those automata that can be expressed by regular expressions without the constant 1, and use this characterisation to give a positive answer to Milner's question for this subset of expressions. In this paper, we analyse Grabmayer and Fokkink's proof of completeness from the perspective of universal coalgebra, and thereby give an abstract account of their proof method. We then compare this proof method to another approach to completeness proofs from coalgebraic language theory. This culminates in two abstract proof methods for completeness, what we call the local and global approaches, and a description of when one method can be used in place of the other.

cs.LO↗

arXiv2021

Canonical Automata via Distributive Law Homomorphisms

The classical powerset construction is a standard method converting a non-deterministic automaton into a deterministic one recognising the same language. Recently, the powerset construction has been lifted to a more general framework that converts an automaton with side-effects, given by a monad, into a deterministic automaton accepting the same language. The resulting automaton has additional algebraic properties, both in the state space and transition structure, inherited from the monad. In this paper, we study the reverse construction and present a framework in which a deterministic automaton with additional algebraic structure over a given monad can be converted into an equivalent succinct automaton with side-effects. Apart from recovering examples from the literature, such as the canonical residual finite-state automaton and the átomaton, we discover a new canonical automaton for a regular language by relating the free vector space monad over the two element field to the neighbourhood monad. Finally, we show that every regular language satisfying a suitable property parametric in two monads admits a size-minimal succinct acceptor.

cs.FL↗

arXiv2021

Prognosis: Closed-Box Analysis of Network Protocol Implementations

We present Prognosis, a framework offering automated closed-box learning and analysis of models of network protocol implementations. Prognosis can learn models that vary in abstraction level from simple deterministic automata to models containing data operations, such as register updates, and can be used to unlock a variety of analysis techniques -- model checking temporal properties, computing differences between models of two implementations of the same protocol, or improving testing via model-based test generation. Prognosis is modular and easily adaptable to different protocols (e.g., TCP and QUIC) and their implementations. We use Prognosis to learn models of (parts of) three QUIC implementations -- Quiche (Cloudflare), Google QUIC, and Facebook mvfst -- and use these models to analyze the differences between the various implementations. Our analysis provides insights into different design choices and uncovers potential bugs. Concretely, we have found critical bugs in multiple QUIC implementations, which have been acknowledged by the developers.

cs.NI↗

arXiv2021

Learning Weighted Automata over Principal Ideal Domains

In this paper, we study active learning algorithms for weighted automata over a semiring. We show that a variant of Angluin's seminal \LStar\ algorithm works when the semiring is a principal ideal domain, but not for general semirings such as the natural numbers.

cs.FL↗

arXiv2021

Equivalence checking for weak bi-Kleene algebra

Pomset automata are an operational model of weak bi-Kleene algebra, which describes programs that can fork an execution into parallel threads, upon completion of which execution can join to resume as a single thread. We characterize a fragment of pomset automata that admits a decision procedure for language equivalence. Furthermore, we prove that this fragment corresponds precisely to series-rational expressions, i.e., rational expressions with an additional operator for bounded parallelism. As a consequence, we obtain a new proof that equivalence of series-rational expressions is decidable.

cs.FL↗

arXiv2021

Distribution Bisimilarity via the Power of Convex Algebras

Probabilistic automata (PA), also known as probabilistic nondeterministic labelled transition systems, combine probability and nondeterminism. They can be given different semantics, like strong bisimilarity, convex bisimilarity, or (more recently) distribution bisimilarity. The latter is based on the view of PA as transformers of probability distributions, also called belief states, and promotes distributions to first-class citizens. We give a coalgebraic account of distribution bisimilarity, and explain the genesis of the belief-state transformer from a PA. To do so, we make explicit the convex algebraic structure present in PA and identify belief-state transformers as transition systems with state space that carries a convex algebra. As a consequence of our abstract approach, we can give a sound proof technique which we call bisimulation up-to convex hull.

cs.LO↗

arXiv2021

Guarded Kleene Algebra with Tests: Coequations, Coinduction, and Completeness

Guarded Kleene Algebra with Tests (GKAT) is an efficient fragment of KAT, as it allows for almost linear decidability of equivalence. In this paper, we study the (co)algebraic properties of GKAT. Our initial focus is on the fragment that can distinguish between unsuccessful programs performing different actions, by omitting the so-called early termination axiom. We develop an operational (coalgebraic) and denotational (algebraic) semantics and show that they coincide. We then characterize the behaviors of GKAT expressions in this semantics, leading to a coequation that captures the covariety of automata corresponding to behaviors of GKAT expressions. Finally, we prove that the axioms of the reduced fragment are sound and complete w.r.t. the semantics, and then build on this result to recover a semantics that is sound and complete w.r.t. the full set of axioms.

cs.LO↗

arXiv2021

A Bunched Logic for Conditional Independence

Independence and conditional independence are fundamental concepts for reasoning about groups of random variables in probabilistic programs. Verification methods for independence are still nascent, and existing methods cannot handle conditional independence. We extend the logic of bunched implications (BI) with a non-commutative conjunction and provide a model based on Markov kernels; conditional independence can be directly captured as a logical formula in this model. Noting that Markov kernels are Kleisli arrows for the distribution monad, we then introduce a second model based on the powerset monad and show how it can capture join dependency, a non-probabilistic analogue of conditional independence from database theory. Finally, we develop a program logic for verifying conditional independence in probabilistic programs.

cs.LO↗

arXiv2021

Learning Pomset Automata

We extend the L* algorithm to learn bimonoids recognising pomset languages. We then identify a class of pomset automata that accepts precisely the class of pomset languages recognised by bimonoids and show how to convert between bimonoids and automata.

cs.FL↗