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Pierluigi Nuzzo

7 accepted papers

2025

A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation

AISTATS 2025poster

Constrained Markov decision processes (CMDPs) models are increasingly important in many applications with multiple objectives. When the model is unknown and must be learned online, it is desirable to ensure that the constraint is met, or at least the violation is bounded with time. In recent literat…

Cited by 0SourceScholar
2025

Efficient Counterexample-Guided Fairness Verification and Repair of Neural Networks Using Satisfiability Modulo Convex Programming

IJCAI 2025

Ensuring fairness is essential for ethical decision-making in various domains. Informally, a neural network is considered fair if and only if it treats similar individuals similarly in a given task. We introduce FaVeR (Fairness Verification and Repair), a framework for efficiently verifying and repa

Cited by 0SourcePDFScholar
2024

Analyzing Adversarial Vulnerabilities of Graph Lottery Tickets

ICASSP 2024accepted

Graph neural networks (GNNs) have displayed significant potential in various graph-based learning tasks. However, the computational demands of deploying GNNs on large-scale graphs can grow exponentially. A recent method, termed unified graph sparsification (UGS), shows that there exists a pair consi…

Cited by 0SourceScholar
2023

Task Assignment, Scheduling, and Motion Planning for Automated Warehouses for Million Product Workloads

IROS 2023poster

We address the Warehouse Servicing Problem (WSP) in automated warehouses, which use teams of mobile robots to move products from shelves to packaging stations. Given a list of products, the WSP amounts to finding a motion plan which brings every product on the list from a shelf to a packaging statio…

Cited by 3SourceScholar
2022

Optimal control of partially observable Markov decision processes with finite linear temporal logic constraints

UAI 2022poster

Autonomous agents often operate in environments where the state is partially observed. In addition to maximizing their cumulative reward, agents must execute complex tasks with rich temporal and logical structures. These tasks can be expressed using temporal logic languages like finite linear tempo…

Cited by 8SourcePDFScholar
2021

A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints

AAAI 2021technical

Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various cost functions. In this paper, we consider the setting of episodic fixed-horizon CMDPs. We propose an online algorithm wh…

Cited by 60SourcePDFScholar
2019

DoS-Resilient Multi-Robot Temporal Logic Motion Planning

ICRA 2019poster

We propose an efficient multi-robot motion planning algorithm for missions captured by linear temporal logic (LTL) specifications, in the presence of bounded disturbances and denial-of-service (DoS) attacks against the communication between robots and base stations. Given an LTL formula Ψ, our goal…

Cited by 10SourceScholar