← Search

Mateus de Oliveira Oliveira

8 accepted papers

2025

Sound Over-Approximation of Equational Reasoning with Variable-Preserving Rules Parameterized by Derivation Depth

AAAI 2025technical

Equational reasoning is one of the most intuitive and widely used types of symbolic reasoning. In this setting, the goal is to determine whether a given ground equation t=t' follows as a consequence of a set of equational axioms E using the process of replacing equals with equals. An equation t=t' i…

Cited by 0SourcePDFScholar
2024

Optimal Extended Formulations from Optimal Dynamic Programming Algorithms

IJCAI 2024poster

Vertex Subset Problems (VSPs) are a class of combinatorial optimization problems on graphs where the goal is to find a subset of vertices satisfying a predefined condition. Two prominent approaches for solving VSPs are dynamic programming over tree-like structures, such as tree-decompositions or cli…

Cited by 0SourcePDFScholar
2023

From Width-Based Model Checking to Width-Based Automated Theorem Proving

AAAI 2023technical

In the field of parameterized complexity theory, the study of graph width measures has been intimately connected with the development of width-based model checking algorithms for combinatorial properties on graphs. In this work, we introduce a general framework to convert a large class of width-base…

Cited by 4SourcePDFScholar
2023

Synchronization and Diversity of Solutions

AAAI 2023technical

A central computational problem in the realm of automata theory is the problem of determining whether a finite automaton A has a synchronizing word. This problem has found applications in a variety of subfields of artificial intelligence, including planning, robotics, and multi-agent systems. In thi…

Cited by 5SourcePDFScholar
2021

Diversity in Kemeny Rank Aggregation: A Parameterized Approach

IJCAI 2021poster

In its most traditional setting, the main concern of optimization theory is the search for optimal solutions for instances of a given computational problem. A recent trend of research in artificial intelligence, called solution diversity, has focused on the development of notions of optimality that…

Cited by 10SourcePDFScholar
2021

Unitary Branching Programs: Learnability and Lower Bounds

ICML 2021spotlight

Bounded width branching programs are a formalism that can be used to capture the notion of non-uniform constant-space computation. In this work, we study a generalized version of bounded width branching programs where instructions are defined by unitary matrices of bounded dimension. We introduce a…

2020

Diversity of Solutions: An Exploration Through the Lens of Fixed-Parameter Tractability Theory

IJCAI 2020poster

When modeling an application of practical relevance as an instance of a combinatorial problem X, we are often interested not merely in finding one optimal solution for that instance, but in finding a sufficiently diverse collection of good solutions. In this work we initiate a systematic study of di…

Cited by 0SourcePDFScholar