The Consistency dimension and distribution-dependent learning from queries

We prove a new combinatorial characterization of polynomial learnability from equivalence queries, and state some of its consequences relating the learnability of a class with the learnability via equivalence and membership queries of its subclasses obtained by restricting the instance space. Then we propose and study two models of query learning in which there is a probability distribution on the instance space, both as an application of the tools developed from the combinatorial characterization and as models of independent interest.Postprint (published version

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

On the learnability of output-DFAs

Preprin

AwaZza, recerca viatjant cap a producte

Conferència emmarcada dins el cicle "Enginy i la Geltrú" que es duu a terme mensualment a l'Escola Politècnica Superior d'Enginyeria de Vilanova i la Geltrú. A càrrec de David Guijarro Guillem, expert tecnològic a Telefónica I+D, doctor en Informàtica per la UPC.La transferència de tecnologia dels laboratoris de recerca fins a l'explotació comercial no és un camí fàcil ni tant sols per als productes de software. AwaZza ens serveix d'exemple de les fases i dificultats trobades pel camí. Eines de desenvolupament àgil, la metodologia "lean startup", la mentalitat experimental --assaig-error-- i, sobretot, la passió per la tecnologia són elements que fan més fàcil el trajecte. Tots aquests elements formen part de l'ADN de Telefónica I+D

On the learnability of output-DFA: a proof and an implementation

This paper presents an algorithm that learns Output-DFA by making Evaluation and Equivalence queries. The correctness and termination of the algorithm are discussed. A description of the implementation of the algorithm is also included.Postprint (published version

On the learnability of output-DFA: a proof and an implementation

This paper presents an algorithm that learns Output-DFA by making Evaluation and Equivalence queries. The correctness and termination of the algorithm are discussed. A description of the implementation of the algorithm is also included

Learning ordered binary decision diagrams

We study the learnability of ordered binary decision diagrams (obdds). We give a polynomial-time algorithm using membership and equivalence queries that finds the minimum obdd for the target respecting a given ordering. We also prove that both types of queries and the restriction to a given ordering are necessary if we want minimality in the output, unless P=NP. If learning has to occur with respect to the optimal variable ordering, polynomial-time learnability implies the approximability of two NP-hard optimization problems: the problem of finding the optimal variable ordering for a given obdd and the Optimal Linear Arrangement problem on graphs

Learning ordered binary decision diagrams

We study the learnability of ordered binary decision diagrams (obdds). We give a polynomial-time algorithm using membership and equivalence queries that finds the minimum obdd for the target respecting a given ordering. We also prove that both types of queries and the restriction to a given ordering are necessary if we want minimality in the output, unless P=NP. If learning has to occur with respect to the optimal variable ordering, polynomial-time learnability implies the approximability of two NP-hard optimization problems: the problem of finding the optimal variable ordering for a given obdd and the Optimal Linear Arrangement problem on graphs.Postprint (published version

Learning ordered binary decision diagrams

We study the learnability of ordered binary decision diagrams (obdds). We give a polynomial-time algorithm using membership and equivalence queries that finds the minimum obdd for the target respecting a given ordering. We also prove that both types of queries and the restriction to a given ordering are necessary if we want minimality in the output, unless P=NP. If learning has to occur with respect to the optimal variable ordering, polynomial-time learnability implies the approximability of two NP-hard optimization problems: the problem of finding the optimal variable ordering for a given obdd and the Optimal Linear Arrangement problem on graphs

Query, PACS and simple-PAC learning

We study a distribution dependent form of PAC learning that uses probability distributions related to Kolmogorov complexity. We relate the PACS model, defined by Denis, D'Halluin and Gilleron, with the standard simple-PAC model and give a general technique that subsumes the results of Denis et al and Parekh and Honavar