4 citations · 6 across the 14 of their papers we have counts for
9 papers · 1 filter
When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
Uljad Berdica, Fernando Acero, Anton Ipsen +3
We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.g., recommendation sy…
Beyond Manual Planning: Seating Allocation for Large Organizations
Anton Ipsen, Michael Cashmore, Kirsty Fielding +4
We introduce the Hierarchical Seating Allocation Problem (HSAP) which addresses the optimal assignment of hierarchically structured organizational teams to physical seating arrange…
The Subset Sum Matching Problem
Yufei Wu, Manuel R. Torres, Parisa Zehtabi +4
This paper presents a new combinatorial optimisation task, the Subset Sum Matching Problem (SSMP), which is an abstraction of common financial applications such as trades reconcili…
Temporal Fairness in Decision Making Problems
Manuel R. Torres, Parisa Zehtabi, Michael Cashmore +2
In this work we consider a new interpretation of fairness in decision making problems. Building upon existing fairness formulations, we focus on how to reason over fairness from a…
Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Fernando Acero, Parisa Zehtabi, Nicolas Marchesotti +3
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. Traditionally, some form of mean-variance…
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Saeid Amiri, Parisa Zehtabi, Danial Dervovic +1
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits. In this paper, we exam…
Contrastive Explanations of Centralized Multi-agent Optimization Solutions
Parisa Zehtabi, Alberto Pozanco, Ayala Bloch +2
In many real-world scenarios, agents are involved in optimization problems. Since most of these scenarios are over-constrained, optimal solutions do not always satisfy all agents.…
Explaining Preference-driven Schedules: the EXPRES Framework
Alberto Pozanco, Francesca Mosca, Parisa Zehtabi +2
Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences about the assignments they would like to get.…
Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans
Michael Cashmore, Alessandro Cimatti, Daniele Magazzeni +2
One of the major limitations for the employment of model-based planning and scheduling in practical applications is the need of costly re-planning when an incongruence between the…