b'Vaishak Belle'

research

∙ 08/30/2023

Deep Inductive Logic Programming meets Reinforcement Learning

One approach to explaining the hierarchical levels of understanding with...

0 Andreas Bueff, et al. ∙

research

∙ 06/08/2023

Learnability with PAC Semantics for Multi-agent Beliefs

The tension between deduction and induction is perhaps the most fundamen...

0 Ionela G. Mocanu, et al. ∙

research

∙ 06/08/2023

Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?

In this paper, our aim is to briefly survey and articulate the logical a...

0 Vaishak Belle, et al. ∙

research

∙ 06/08/2023

Toward A Logical Theory Of Fairness and Bias

Fairness in machine learning is of considerable interest in recent years...

0 Vaishak Belle, et al. ∙

research

∙ 06/07/2023

Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures

First-order model counting (FOMC) is a computational problem that asks t...

0 Paulius Dilkas, et al. ∙

research

∙ 06/04/2023

Agency and legibility for artists through Experiential AI

Experiential AI is an emerging research field that addresses the challen...

0 Drew Hemment, et al. ∙

research

∙ 06/01/2023

Experiential AI: A transdisciplinary framework for legibility and agency in AI

Experiential AI is presented as a research agenda in which scientists an...

0 Drew Hemment, et al. ∙

research

∙ 04/27/2023

Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration

AI and ML models have already found many applications in critical domain...

0 Ioannis Papantonis, et al. ∙

research

∙ 07/26/2022

Using Abstraction for Interpretable Robot Programs in Stochastic Domains

A robot's actions are inherently stochastic, as its sensors are noisy an...

0 Till Hofmann, et al. ∙

research

∙ 04/07/2022

Abstracting Noisy Robot Programs

Abstraction is a commonly used process to represent some low-level syste...

0 Till Hofmann, et al. ∙

research

∙ 02/21/2022

Explainability in Machine Learning: a Pedagogical Perspective

Given the importance of integrating of explainability into machine learn...

0 Andreas Bueff, et al. ∙

research

∙ 01/27/2022

Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities

Using large pre-trained models for image recognition tasks is becoming i...

3 Xin Du, et al. ∙

research

∙ 01/17/2022

Principled Diverse Counterfactuals in Multilinear Models

Machine learning (ML) applications have automated numerous real-life tas...

0 Ioannis Papantonis, et al. ∙

research

∙ 11/02/2021

MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks

We propose a novel way to incorporate expert knowledge into the training...

0 Nicholas Hoernle, et al. ∙

research

∙ 10/06/2021

Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief

Many AI applications involve the interaction of multiple autonomous agen...

10 Christian Muise, et al. ∙

research

∙ 09/13/2021

One Down, 699 to Go: or, synthesising compositional desugarings

Programming or scripting languages used in real-world systems are seldom...

0 Sándor Bartha, et al. ∙

research

∙ 10/23/2020

Learning Implicitly with Noisy Data in Linear Arithmetic

Robustly learning in expressive languages with real-world data continues...

0 Alexander Philipp Rader, et al. ∙

research

∙ 09/18/2020

Principles and Practice of Explainable Machine Learning

Artificial intelligence (AI) provides many opportunities to improve priv...

48 Vaishak Belle, et al. ∙

research

∙ 06/17/2020

Logic, Probability and Action: A Situation Calculus Perspective

The unification of logic and probability is a long-standing concern in A...

0 Vaishak Belle, et al. ∙

research

∙ 06/15/2020

Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains

The tension between deduction and induction is perhaps the most fundamen...

14 Vaishak Belle, et al. ∙

research

∙ 06/02/2020

Generating Random Logic Programs Using Constraint Programming

Testing algorithms across a wide range of problem instances is crucial t...

7 Paulius Dilkas, et al. ∙

research

∙ 01/29/2020

On Constraint Definability in Tractable Probabilistic Models

Incorporating constraints is a major concern in probabilistic machine le...

10 Ioannis Papantonis, et al. ∙

research

∙ 01/29/2020

Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations

In recent years, there has been an increasing interest in studying causa...

49 Ioannis Papantonis, et al. ∙

research

∙ 01/15/2020

SMT + ILP

Inductive logic programming (ILP) has been a deeply influential paradigm...

15 Vaishak Belle, et al. ∙

research

∙ 11/26/2019

Logical Interpretations of Autoencoders

The unification of low-level perception and high-level reasoning is a lo...

60 Anton Fuxjaeger, et al. ∙

research

∙ 10/10/2019

The Quest for Interpretable and Responsible Artificial Intelligence

Artificial Intelligence (AI) provides many opportunities to improve priv...

0 Vaishak Belle, et al. ∙

research

∙ 08/06/2019

Experiential AI

Experiential AI is proposed as a new research agenda in which artists an...

5 Drew Hemment, et al. ∙

research

∙ 06/24/2019

Implicitly Learning to Reason in First-Order Logic

We consider the problem of answering queries about formulas of first-ord...

0 Vaishak Belle, et al. ∙

research

∙ 05/16/2019

A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains

Finite-state controllers (FSCs), such as plans with loops, are powerful ...

0 Laszlo Treszkai, et al. ∙

research

∙ 05/16/2019

Fairness in Machine Learning with Tractable Models

Machine Learning techniques have become pervasive across a range of diff...

0 Michael Varley, et al. ∙

research

∙ 01/17/2019

Learning Tractable Probabilistic Models in Open Worlds

Large-scale probabilistic representations, including statistical knowled...

0 Amelie Levray, et al. ∙

research

∙ 11/29/2018

Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation

Weighted model integration (WMI) extends weighted model counting (WMC) i...

0 Anton Fuxjaeger, et al. ∙

research

∙ 10/08/2018

Deep Tractable Probabilistic Models for Moral Responsibility

Moral responsibility is a major concern in automated decision-making, wi...

0 Lewis Hammond, et al. ∙

research

∙ 10/04/2018

Abstracting Probabilistic Relational Models

Abstraction is a powerful idea widely used in science, to model, reason ...

0 Vaishak Belle, et al. ∙

research

∙ 09/14/2018

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...

0 Vaishak Belle, et al. ∙

research

∙ 09/14/2018

On Plans With Loops and Noise

In an influential paper, Levesque proposed a formal specification for an...

0 Vaishak Belle, et al. ∙

research

∙ 07/15/2018

Learning Probabilistic Logic Programs in Continuous Domains

The field of statistical relational learning aims at unifying logic and ...

0 Stefanie Speichert, et al. ∙

research

∙ 07/14/2018

Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks

Probabilistic representations, such as Bayesian and Markov networks, are...

0 Andreas Bueff, et al. ∙

research

∙ 01/25/2018

Probabilistic Planning by Probabilistic Programming

Automated planning is a major topic of research in artificial intelligen...

0 Vaishak Belle, et al. ∙

research

∙ 09/21/2016

Semiring Programming: A Framework for Search, Inference and Learning

To solve hard problems, AI relies on a variety of disciplines such as lo...

0 Vaishak Belle, et al. ∙

research

∙ 05/26/2016

The Symbolic Interior Point Method

A recent trend in probabilistic inference emphasizes the codification of...

0 Martin Mladenov, et al. ∙

research

∙ 02/28/2014

Robot Location Estimation in the Situation Calculus

Location estimation is a fundamental sensing task in robotic application...

0 Vaishak Belle, et al. ∙

research

∙ 09/26/2013

Reasoning about Probabilities in Dynamic Systems using Goal Regression

Reasoning about degrees of belief in uncertain dynamic worlds is fundame...

0 Vaishak Belle, et al. ∙

research

∙ 09/10/2010

Multi-Agent Only-Knowing Revisited

Levesque introduced the notion of only-knowing to precisely capture the ...

0 Vaishak Belle, et al. ∙

Vaishak Belle

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