Institution profile

University of Milan

Academic institutioneurope · it
Official website
Research library221linked papers
Opportunities0open roles
Selected work

Representative Papers

Large datasets for the Euro Area and its member countries and the dynamic effects of the common monetary policy

Oct 07, 2024

This study investigates heterogeneous responses to common monetary policy shocks across Eurozone countries, focusing on asymmetric transmission mechanisms in prices, interest rates, real output, and equity prices. We construct and publicly release EA-MD-QD—a novel high-frequency macroeconomic database covering the Eurozone aggregate and ten member states, updated monthly/quarterly and continuously revised since January 2000 (comprising 800+ time series). Employing state-of-the-art methods—including Common Component VAR, instrumental variable estimation, and sign-restricted identification—we systematically uncover structural disparities in monetary transmission between core and peripheral countries, and demonstrate that real—not nominal—variables predominantly drive business cycle synchronization across members. Our key contributions are threefold: (i) the first integrated, high-frequency, publicly available Eurozone macrodatabase; (ii) the first empirical characterization of intra-Eurozone monetary policy transmission asymmetries; and (iii) identification of the underlying drivers of such asymmetries.

3 citationsRead paper

A Contextual Online Learning Theory of Brokerage

May 22, 2024arXiv.org

This paper studies the context-aware online bilateral trading problem: a broker must dynamically set transaction prices for privately informed buyers and sellers, leveraging asset- and market-related contextual features, to maximize expected revenue. We establish the first theoretical learning framework for online brokerage with contextual information, distinguishing between two realistic feedback models—full feedback (where both agents’ valuations are revealed) and binary feedback (where only the transaction outcome is observed). Under a bounded-density assumption on valuations, we propose algorithms based on linear contextual modeling and online convex optimization. In the full-feedback setting, our algorithm achieves the optimal regret bound of $O(Ld ln T)$; under binary feedback, it attains $O(sqrt{LdT ln T})$ regret, and we prove a matching lower bound of $Omega(sqrt{LdT})$. Furthermore, we show that the problem becomes statistically unlearnable without the bounded-density condition.

2 citationsRead paper

Prioritizing Configuration Relevance via Compiler-Based Refined Feature Ranking

Jan 22, 2026

This work addresses the intractability of program analysis, testing, and optimization in modern languages like Rust due to combinatorial explosion in configuration spaces. To tackle this challenge, the authors propose a compiler-based approach for prioritizing configurations. By instrumenting the Rust compiler to extract intermediate representations, they construct a configuration dependency graph and refine configuration rankings using graph centrality metrics combined with code impact scope. A SAT solver is then employed to generate a high-relevance subset of configurations while preserving their semantic validity. The prototype system, RustyEx, efficiently produces valid configuration subsets of specified sizes on mainstream Rust projects, significantly improving the exploration efficiency of large configuration spaces under resource constraints.

1 citationsRead paper

Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence

Jan 13, 2026

This work addresses key limitations in existing zero-shot activities of daily living (ADL) recognition methods based on large language models (LLMs), which typically rely on temporal segmentation that poorly aligns with LLMs’ contextual reasoning capabilities and lack effective confidence estimation mechanisms. To overcome these issues, the authors propose an event-driven contextual segmentation strategy that replaces conventional fixed time windows, along with a novel confidence estimation algorithm capable of distinguishing between correct and incorrect predictions. Experimental results on complex real-world datasets demonstrate that the proposed approach not only significantly outperforms current zero-shot methods but also surpasses several supervised baselines. Moreover, the introduced confidence metric effectively reflects the reliability of model predictions, offering a practical tool for assessing prediction trustworthiness in deployment scenarios.

1 citationsRead paper
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