2 citations · 4 across the 16 of their papers we have counts for
16 papers
From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs
Tina Vartziotis, Rodopi Kosteli, Elli Vartziotis +5
The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprint of deployed AI systems. Ho…
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
Kartik Tandon, Julian Gould, Tanishq Bhatia +3
Modern deep learning architectures increasingly contend with sophisticated signals that are natively infinite-dimensional, such as time series, probability distributions, or operat…
A Statistical Framework for Algorithmic Collective Action with Multiple Collectives
Claudio Battiloro, Pietro Greiner, Dario Rancati +3
As learning systems increasingly shape everyday decisions, Algorithmic Collective Action (ACA), i.e., users coordinating changes to shared data to steer model behavior, offers a co…
Guided Transfer Learning for Discrete Diffusion Models
Julian Kleutgens, Claudio Battiloro, Lingkai Kong +3
Discrete diffusion models (DMs) have achieved strong performance in language and other discrete domains, offering a compelling alternative to autoregressive modeling. Yet this perf…
Algorithmic Collective Action with Multiple Collectives
Claudio Battiloro, Pietro Greiner, Bret Nestor +2
As learning systems increasingly influence everyday decisions, user-side steering via Algorithmic Collective Action (ACA)-coordinated changes to shared data-offers a complement to…
Understanding Spatial Regression Models from a Weighting Perspective in an Observational Study of Superfund Remediation
Sophie M. Woodward, Francesca Dominici, Jose R. Zubizarreta
A key challenge in environmental health research is unmeasured spatial confounding, driven by unobserved spatially structured variables that influence both treatment and outcome. A…
A structural nested rate model for estimating the effects of time-varying exposure on recurrent event outcomes in the presence of death
Daniel Mork, Robert L. Strawderman, Michelle Audirac +2
Assessing the causal effect of time-varying exposures on recurrent event processes is challenging in the presence of a terminating event. Our objective is to estimate both the shor…
Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding
Manuel Lecha, Andrea Cavallo, Francesca Dominici +5
Graph Neural Networks (GNNs) excel at learning from pairwise interactions but often overlook multi-way and hierarchical relationships. Topological Deep Learning (TDL) addresses thi…
Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents
Mauricio Tec, Guojun Xiong, Haichuan Wang +2
Deep Reinforcement Learning (RL) is remarkably effective in addressing sequential resource allocation problems in domains such as healthcare, public policy, and resource management…
Quantum Simplicial Neural Networks
Simone Piperno, Claudio Battiloro, Andrea Ceschini +3
Graph Neural Networks (GNNs) excel at learning from graph-structured data but are limited to modeling pairwise interactions, insufficient for capturing higher-order relationships p…
An Instrumental Variables Framework to Unite Spatial Confounding Methods
Sophie M. Woodward, Mauricio Tec, Francesca Dominici
Studies investigating the causal effects of spatially varying exposures on outcomes often rely on observational and spatially indexed data. A prevalent challenge is unmeasured spat…
Towards a Health-Based Power Grid Optimization in the Artificial Intelligence Era
Claudio Battiloro, Gianluca Guidi, Falco J. Bargagli-Stoffi +1
The electric power sector is one of the largest contributors to greenhouse gas emissions in the world. In recent years, there has been an unprecedented increase in electricity dema…
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
Manuel Lecha, Andrea Cavallo, Francesca Dominici +2
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 com…
E(n) Equivariant Topological Neural Networks
Claudio Battiloro, Ege Karaismailoğlu, Mauricio Tec +3
Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emer…
Optimizing Heat Alert Issuance with Reinforcement Learning
Ellen M. Considine, Rachel C. Nethery, Gregory A. Wellenius +2
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 pape…
SpaCE: The Spatial Confounding Environment
Mauricio Tec, Ana Trisovic, Michelle Audirac +4
Spatial confounding poses a significant challenge in scientific studies involving spatial data, where unobserved spatial variables can influence both treatment and outcome, possibl…