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Francesca Dominici

7 accepted papers

2026

Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding

ICLR 2026poster

Graph Neural Networks (GNNs) excel at learning from pairwise interactions but often overlook multi-way and hierarchical relationships. Topological Deep Learning (TDL) addresses this limitation by leveraging combinatorial topological spaces, such as simplicial or cell complexes. However, existing TDL…

Cited by 0SourcecodeScholar
2026

Rule-Bottleneck RL: Learning to Decide and Explain for Sequential Resource Allocation via LLM Agents in Public Health

IJCAI 2026

Reducing preventable maternal mortality remains a global health priority. Under Sustainable Development Goal (SDG) target 3.1, the WHO emphasizes timely and equitable allocation of limited maternal health resources. Motivated by Department of Obstetrics and Gynecology at several important hospitals

Cited by 0Scholar
2025

E(n) Equivariant Topological Neural Networks

ICLR 2025poster

Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing this issue. TDL enables the principled modeling of arbitrary multi-way,…

2025

Higher-Order Topological Directionality and Directed Simplicial Neural Networks

ICASSP 2025accepted

Topological Deep Learning (TDL) has emerged as a paradigm to process and learn from signals defined on higher-order combinatorial topological spaces, such as simplicial or cell complexes. Although many complex systems have an asymmetric relational structure, most TDL models forcibly symmetrize these…

Cited by 0SourceScholar
2025

Optimizing Heat Alert Issuance with Reinforcement Learning

AAAI 2025technical

A key strategy in societal adaptation to climate change is using alert systems to prompt preventative action and reduce the adverse health impacts of extreme heat events. This paper implements and evaluates reinforcement learning (RL) as a tool to optimize the effectiveness of such systems. Our cont…

2024

SpaCE: The Spatial Confounding Environment

ICLR 2024poster

Spatial confounding poses a significant challenge in scientific studies involving spatial data, where unobserved spatial variables can influence both treatment and outcome, possibly leading to spurious associations. To address this problem, we introduce SpaCE: The Spatial Confounding Environment, th…

2018

Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors

NeurIPS 2018poster

Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. We speed up the al…