41 citations · 68 across the 26 of their papers we have counts for
10 papers · 1 filter
Generalised Transportability via Causal Abstractions
Yorgos Felekis, Paris Giampouras, Fabio Massimo Zennaro +1
Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference. The theory of transportability provides a criterion fo…
Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer, Nicholas Bishop, Anisoara Calinescu +2
Although real-world decision-making problems can often be encoded as causal multi-armed bandits (CMABs) at different levels of abstraction, a general methodology exploiting the inf…
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele D'Acunto, Fabio Massimo Zennaro, Yorgos Felekis +1
Structural causal models (SCMs) allow us to investigate complex systems at multiple levels of resolution. The causal abstraction (CA) framework formalizes the mapping between high-…
Causally Abstracted Multi-armed Bandits
Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer +4
Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems. The majority of prior work typically studies and solves individual MAB and…
Causal Optimal Transport of Abstractions
Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini +1
Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them. T…
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems
Fabio Massimo Zennaro, Audun Jøsang
The multi-armed bandit problem is a classical decision-making problem where an agent has to learn an optimal action balancing exploration and exploitation. Properly managing this t…
Towards Further Understanding of Sparse Filtering via Information Bottleneck
Fabio Massimo Zennaro, Ke Chen
In this paper we examine a formalization of feature distribution learning (FDL) in information-theoretic terms relying on the analytical approach and on the tools already used in t…
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings
Fabio Massimo Zennaro
In this paper we offer a preliminary study of the application of Bayesian coresets to network security data. Network intrusion detection is a field that could take advantage of Bay…
On the Use of Sparse Filtering for Covariate Shift Adaptation
Fabio Massimo Zennaro, Ke Chen
In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating…
Towards Understanding Sparse Filtering: A Theoretical Perspective
Fabio Massimo Zennaro, Ke Chen
In this paper we present a theoretical analysis to understand sparse filtering, a recent and effective algorithm for unsupervised learning. The aim of this research is not to show…