no code implementations • 24 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.
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.
no code implementations • 17 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.
no code implementations • 18 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.
no code implementations • 21 Nov 2019 • Mustafa Canim, Cristina Cornelio, Arun Iyengar, Ryan Musa, Mariano Rodrigez Muro
Unstructured enterprise data such as reports, manuals and guidelines often contain tables.
no code implementations • 11 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.
no code implementations • 7 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).
no code implementations • NeurIPS Workshop AIPLANS 2021 • Thabang Lebese, Ndivhuwo Makondo, Cristina Cornelio, Naweed Khan
Automated Theorem Provers (ATPs) are widely used for the verification of logical statements.
no code implementations • 29 Nov 2022 • Kenneth L. Clarkson, Cristina Cornelio, Sanjeeb Dash, Joao Goncalves, Lior Horesh, Nimrod Megiddo
This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms.
no code implementations • 18 Aug 2023 • Ryan Cory-Wright, Cristina Cornelio, Sanjeeb Dash, Bachir El Khadir, Lior Horesh
The optimization techniques leveraged in this paper allow our approach to run in polynomial time with fully correct background theory under an assumption that the complexity of our derivation is bounded), or non-deterministic polynomial (NP) time with partially correct background theory.
no code implementations • 31 Mar 2024 • Cristina Cornelio, Mohammed Diab
Recognizing failures during task execution and implementing recovery procedures is challenging in robotics.
1 code implementation • 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.
Ranked #1 on Automated Theorem Proving on HolStep (Conditional)
1 code implementation • 5 Nov 2019 • Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
1 code implementation • 16 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.
Ranked #1 on Inductive logic programming on RuDaS
Inductive knowledge graph completion Inductive logic programming +2
1 code implementation • 3 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 to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression.
1 code implementation • Findings (ACL) 2021 • Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.