Search Results for author: Cristina Cornelio

Found 12 papers, 2 papers with code

AI Descartes: Combining Data and Theory for Derivable Scientific Discovery

no code implementations3 Sep 2021 Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler Josephson, Joao Goncalves, Kenneth Clarkson, Nimrod Megiddo, Bachir El Khadir, Lior Horesh

We develop a method for combining logical reasoning with symbolic regression, enabling principled derivations of models of natural phenomena.

Automated Theorem Proving

Learning to Guide a Saturation-Based Theorem Prover

no code implementations7 Jun 2021 Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni, Vernon Austil, Cristina Cornelio, Shajith Ikbal, Pavan Kapanipathi, Ndivhuwo Makondo, Kavitha Srinivas, Michael Witbrock, Achille Fokoue

In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).

Automated Theorem Proving

Symbolic Regression using Mixed-Integer Nonlinear Optimization

no code implementations11 Jun 2020 Vernon Austel, Cristina Cornelio, Sanjeeb Dash, Joao Goncalves, Lior Horesh, Tyler Josephson, Nimrod Megiddo

The Symbolic Regression (SR) problem, where the goal is to find a regression function that does not have a pre-specified form but is any function that can be composed of a list of operators, is a hard problem in machine learning, both theoretically and computationally.

Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling

no code implementations arXiv 2020 Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus, Achille Fokoue

Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.

Automated Theorem Proving

Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis

no code implementations18 Sep 2019 Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources.

Model Selection

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools

1 code implementation16 Sep 2019 Cristina Cornelio, Veronika Thost

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form.

Inductive knowledge graph completion Inductive logic programming +2

Using deceased-donor kidneys to initiate chains of living donor kidney paired donations: algorithms and experimentation

no code implementations17 Dec 2018 Cristina Cornelio, Lucrezia Furian, Antonio Nicolo', Francesca Rossi

We design a flexible algorithm that exploits deceased donor kidneys to initiate chains of living donor kidney paired donations, combining deceased and living donor allocation mechanisms to improve the quantity and quality of kidney transplants.

Identifying the Discourse Function of News Article Paragraphs

no code implementations COLING 2018 W. Victor Yarlott, Cristina Cornelio, Tian Gao, Mark Finlayson

We test two hypotheses: first, that people can reliably annotate news articles with van Dijk{'}s theory; second, that we can reliably predict these labels using machine learning.


Logical Conditional Preference Theories

no code implementations24 Apr 2015 Cristina Cornelio, Andrea Loreggia, Vijay Saraswat

CP-nets represent the dominant existing framework for expressing qualitative conditional preferences between alternatives, and are used in a variety of areas including constraint solving.

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