2 citations · 2 across the 4 of their papers we have counts for
6 papers
Lower Bounds for the Algorithmic Complexity of Learned Indexes
Luis Alberto Croquevielle, Roman Sokolovskii, Thomas Heinis
Learned index structures aim to accelerate queries by training machine learning models to approximate the rank function associated with a database attribute. While effective in pra…
Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models
Gabriel della Maggiora, Luis Alberto Croquevielle, Harry Horsley +2
Phase imaging is gaining importance due to its applications in fields like biomedical imaging and material characterization. In biomedical applications, it can provide quantitative…
Querying in Constant Expected Time with Learned Indexes
Luis Croquevielle, Guang Yang, Liang Liang +2
Learned indexes leverage machine learning models to accelerate query answering in databases, showing impressive practical performance. However, theoretical understanding of these m…
Conditional Variational Diffusion Models
Gabriel della Maggiora, Luis Alberto Croquevielle, Nikita Deshpande +3
Inverse problems aim to determine parameters from observations, a crucial task in engineering and science. Lately, generative models, especially diffusion models, have gained popul…
When is Approximate Counting for Conjunctive Queries Tractable?
Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram +1
Conjunctive queries are one of the most common class of queries used in database systems, and the best studied in the literature. A seminal result of Grohe, Schwentick, and Segoufi…
$\text{#NFA}$ admits an FPRAS: Efficient Enumeration, Counting, and Uniform Generation for Logspace Classes
Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram +1
In this work, we study two simple yet general complexity classes, based on logspace Turing machines, which provide a unifying framework for efficient query evaluation in areas like…