10 citations · 71 across the 49 of their papers we have counts for
10 papers · 1 filter
Program Semantic Inequivalence Game with Large Language Models
Antonio Valerio Miceli-Barone, Vaishak Belle, Ali Payani
Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.…
Deep Inductive Logic Programming meets Reinforcement Learning
Andreas Bueff, Vaishak Belle
One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data effic…
Principled Diverse Counterfactuals in Multilinear Models
Ioannis Papantonis, Vaishak Belle
Machine learning (ML) applications have automated numerous real-life tasks, improving both private and public life. However, the black-box nature of many state-of-the-art models po…
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks
Nicholas Hoernle, Rafael Michael Karampatsis, Vaishak Belle +1
We propose a novel way to incorporate expert knowledge into the training of deep neural networks. Many approaches encode domain constraints directly into the network architecture,…
Principles and Practice of Explainable Machine Learning
Vaishak Belle, Ioannis Papantonis
Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a…
On Constraint Definability in Tractable Probabilistic Models
Ioannis Papantonis, Vaishak Belle
Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, fr…
Fairness in Machine Learning with Tractable Models
Michael Varley, Vaishak Belle
Machine Learning techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit-…
Learning Credal Sum-Product Networks
Amelie Levray, Vaishak Belle
Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly…
Learning Probabilistic Logic Programs in Continuous Domains
Stefanie Speichert, Vaishak Belle
The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilisti…
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks
Andreas Bueff, Stefanie Speichert, Vaishak Belle
Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly…