4 citations · 7 across the 11 of their papers we have counts for
11 papers
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…
Lightweight Robust Direct Preference Optimization
Cheol Woo Kim, Shresth Verma, Mauricio Tec +1
Direct Preference Optimization (DPO) has become a popular method for fine-tuning large language models (LLMs) due to its stability and simplicity. However, it is also known to be s…
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…
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…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
TopoBench: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernardez, Marco Montagna +34
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL int…
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…
Causal Estimation of Exposure Shifts with Neural Networks
Mauricio Tec, Kevin Josey, Oladimeji Mudele +1
A fundamental task in causal inference is estimating the effect of distribution shift in the treatment variable. We refer to this problem as shift-response function (SRF) estimatio…
Random clique covers for graphs with local density and global sparsity
Sinead A. Williamson, Mauricio Tec
Large real-world graphs tend to be sparse, but they often contain many densely connected subgraphs and exhibit high clustering coefficients. While recent random graph models can ca…