10 citations · 71 across the 49 of their papers we have counts for
7 papers · 1 filter
Learning Implicitly with Noisy Data in Linear Arithmetic
Alexander P. Rader, Ionela G. Mocanu, Vaishak Belle +1
Robust learning in expressive languages with real-world data continues to be a challenging task. Numerous conventional methods appeal to heuristics without any assurances of robust…
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…
Logic, Probability and Action: A Situation Calculus Perspective
Vaishak Belle
The unification of logic and probability is a long-standing concern in AI, and more generally, in the philosophy of science. In essence, logic provides an easy way to specify prope…
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains
Vaishak Belle
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concer…
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…
Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations
Ioannis Papantonis, Vaishak Belle
In recent years, there has been an increasing interest in studying causality-related properties in machine learning models generally, and in generative models in particular. While…
SMT + ILP
Vaishak Belle
Inductive logic programming (ILP) has been a deeply influential paradigm in AI, enjoying decades of research on its theory and implementations. As a natural descendent of the field…