
One Down, 699 to Go: or, synthesising compositional desugarings
Programming or scripting languages used in realworld systems are seldom...
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Learning Implicitly with Noisy Data in Linear Arithmetic
Robustly learning in expressive languages with realworld data continues...
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Principles and Practice of Explainable Machine Learning
Artificial intelligence (AI) provides many opportunities to improve priv...
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Logic, Probability and Action: A Situation Calculus Perspective
The unification of logic and probability is a longstanding concern in A...
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Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains
The tension between deduction and induction is perhaps the most fundamen...
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Generating Random Logic Programs Using Constraint Programming
Testing algorithms across a wide range of problem instances is crucial t...
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On Constraint Definability in Tractable Probabilistic Models
Incorporating constraints is a major concern in probabilistic machine le...
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Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations
In recent years, there has been an increasing interest in studying causa...
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SMT + ILP
Inductive logic programming (ILP) has been a deeply influential paradigm...
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Logical Interpretations of Autoencoders
The unification of lowlevel perception and highlevel reasoning is a lo...
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The Quest for Interpretable and Responsible Artificial Intelligence
Artificial Intelligence (AI) provides many opportunities to improve priv...
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Experiential AI
Experiential AI is proposed as a new research agenda in which artists an...
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Implicitly Learning to Reason in FirstOrder Logic
We consider the problem of answering queries about formulas of firstord...
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A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains
Finitestate controllers (FSCs), such as plans with loops, are powerful ...
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Fairness in Machine Learning with Tractable Models
Machine Learning techniques have become pervasive across a range of diff...
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Learning Tractable Probabilistic Models in Open Worlds
Largescale probabilistic representations, including statistical knowled...
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Scaling up Probabilistic Inference in Linear and NonLinear Hybrid Domains by Leveraging Knowledge Compilation
Weighted model integration (WMI) extends weighted model counting (WMC) i...
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Deep Tractable Probabilistic Models for Moral Responsibility
Moral responsibility is a major concern in automated decisionmaking, wi...
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Abstracting Probabilistic Relational Models
Abstraction is a powerful idea widely used in science, to model, reason ...
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Reasoning about Discrete and Continuous Noisy Sensors and Effectors in Dynamical Systems
Among the many approaches for reasoning about degrees of belief in the p...
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On Plans With Loops and Noise
In an influential paper, Levesque proposed a formal specification for an...
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Learning Probabilistic Logic Programs in Continuous Domains
The field of statistical relational learning aims at unifying logic and ...
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Tractable Querying and Learning in Hybrid Domains via SumProduct Networks
Probabilistic representations, such as Bayesian and Markov networks, are...
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Probabilistic Planning by Probabilistic Programming
Automated planning is a major topic of research in artificial intelligen...
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Semiring Programming: A Framework for Search, Inference and Learning
To solve hard problems, AI relies on a variety of disciplines such as lo...
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The Symbolic Interior Point Method
A recent trend in probabilistic inference emphasizes the codification of...
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Robot Location Estimation in the Situation Calculus
Location estimation is a fundamental sensing task in robotic application...
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Reasoning about Probabilities in Dynamic Systems using Goal Regression
Reasoning about degrees of belief in uncertain dynamic worlds is fundame...
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MultiAgent OnlyKnowing Revisited
Levesque introduced the notion of onlyknowing to precisely capture the ...
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Vaishak Belle
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