12 citations · 48 across the 23 of their papers we have counts for
29 papers
Scaling Weisfeiler-Leman Expressiveness Analysis to Massive Graphs with GPUs
Filippo Biondi, Mirco Tribastone, Max Tschaikowski
The stable coloring of the Weisfeiler-Leman (1-WL) test is a cornerstone of Graph Neural Networks because it provides an upper bound to the expressive power of message-passing arch…
Neural Network Compression by Approximate Differential Equivalence
Ravi Dhiman, Andrea Passarella, Mirco Tribastone +1
Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a complementary approach that comp…
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
Riccardo Porcedda, Giuseppe Squillace, Bastian Epping +4
While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood information, the performance of…
DeGAS: Gradient-Based Optimization of Probabilistic Programs without Sampling
Francesca Randone, Romina Doz, Mirco Tribastone +1
We present DeGAS, a differentiable Gaussian approximate semantics for loopless probabilistic programs that enables sample-free, gradient-based optimization in models with both cont…
Certified Inductive Synthesis for Online Mixed-Integer Optimization
Marco Zamponi, Emilio Incerto, Daniele Masti +1
In fields such as autonomous and safety-critical systems, online optimization plays a crucial role in control and decision-making processes, often requiring the integration of cont…
Efficient Network Embedding by Approximate Equitable Partitions
Giuseppe Squillace, Mirco Tribastone, Max Tschaikowski +1
Structural network embedding is a crucial step in enabling effective downstream tasks for complex systems that aims to project a network into a lower-dimensional space while preser…
Proper Lumping for Positive Bilinear Control Systems
Antonio Jiménez-Pastor, Daniele Toller, Mirco Tribastone +2
Positive systems naturally arise in situations where the model tracks physical quantities. Although the linear case is well understood, analysis and controller design for nonlinear…
Coarse-graining Complex Networks for Control Equivalence
Daniele Toller, Mirco Tribastone, Max Tschaikowski +1
The ability to control complex networks is of crucial importance across a wide range of applications in natural and engineering sciences. However, issues of both theoretical and nu…
Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures
Francesca Randone, Luca Bortolussi, Emilio Incerto +1
Computing the posterior distribution of a probabilistic program is a hard task for which no one-fit-for-all solution exists. We propose Gaussian Semantics, which approximates the e…
Forward and Backward Constrained Bisimulations for Quantum Circuits using Decision Diagrams
Lukas Burgholzer, Antonio Jiménez-Pastor, Kim G. Larsen +3
Efficient methods for the simulation of quantum circuits on classic computers are crucial for their analysis due to the exponential growth of the problem size with the number of qu…
Certification of the proximal gradient method under fixed-point arithmetic for box-constrained QP problems
Pablo Krupa, Omar Inverso, Mirco Tribastone +1
In safety-critical applications that rely on the solution of an optimization problem, the certification of the optimization algorithm is of vital importance. Certification and subo…
Optimality-preserving Reduction of Chemical Reaction Networks
Kim G. Larsen, Daniele Toller, Mirco Tribastone +2
Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled nonlinear ordinary differential equations. In many app…
Minimization of Dynamical Systems over Monoids
Georgios Argyris, Alberto Lluch Lafuente, Alexander Leguizamon Robayo +3
Quantitative notions of bisimulation are well-known tools for the minimization of dynamical models such as Markov chains and ordinary differential equations (ODEs). In \emph{forwar…
Reducing Boolean Networks with Backward Boolean Equivalence
Georgios Argyris, Alberto Lluch Lafuente, Mirco Tribastone +2
Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium to large BNs due to the state-space e…
Efficient Local Computation of Differential Bisimulations via Coupling and Up-to Methods
Giorgio Bacci, Giovanni Bacci, Kim G. Larsen +3
We introduce polynomial couplings, a generalization of probabilistic couplings, to develop an algorithm for the computation of equivalence relations which can be interpreted as a l…
Exact maximal reduction of stochastic reaction networks by species lumping
Luca Cardelli, Isabel Cristina Perez-Verona, Mirco Tribastone +3
Motivation: Stochastic reaction networks are a widespread model to describe biological systems where the presence of noise is relevant, such as in cell regulatory processes. Unfort…
Improved estimations of stochastic chemical kinetics by finite state expansion
Tabea Waizmann, Luca Bortolussi, Andrea Vandin +1
Stochastic reaction networks are a fundamental model to describe interactions between species where random fluctuations are relevant. The master equation provides the evolution of…
CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations
Alexey Ovchinnikov, Isabel Cristina Pérez Verona, Gleb Pogudin +1
Motivation: Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to…
Learning Queuing Networks by Recurrent Neural Networks
Giulio Garbi, Emilio Incerto, Mirco Tribastone
It is well known that building analytical performance models in practice is difficult because it requires a considerable degree of proficiency in the underlying mathematics. In thi…
PID Control of Biochemical Reaction Networks
Max Whitby, Luca Cardelli, Marta Kwiatkowska +3
Principles of feedback control have been shown to naturally arise in biological systems and successfully applied to build synthetic circuits. In this work we consider Biochemical R…
From Electric Circuits to Chemical Networks
Luca Cardelli, Mirco Tribastone, Max Tschaikowski
Electric circuits manipulate electric charge and magnetic flux via a small set of discrete components to implement useful functionality over continuous time-varying signals represe…
Guaranteed Error Bounds on Approximate Model Abstractions through Reachability Analysis
Luca Cardelli, Mirco Tribastone, Max Tschaikowski +1
It is well known that exact notions of model abstraction and reduction for dynamical systems may not be robust enough in practice because they are highly sensitive to the specific…
Syntactic Markovian Bisimulation for Chemical Reaction Networks
Luca Cardelli, Mirco Tribastone, Max Tschaikowski +1
In chemical reaction networks (CRNs) with stochastic semantics based on continuous-time Markov chains (CTMCs), the typically large populations of species cause combinatorially larg…
Proceedings 14th International Workshop Quantitative Aspects of Programming Languages and Systems
Mirco Tribastone, Herbert Wiklicky
This volume contains the post-proceedings of the 14th International Workshop on Quantitative Aspects of Programming Languages and Systems (QAPL), held as a satellite workshop of ET…