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When developing AI systems that interact with humans, it is essential to design both a system that can understand humans, and a system that humans can understand. Most deep network based agent-modeling approaches are 1) not interpretable…

Machine Learning · Computer Science 2021-07-14 Ini Oguntola , Dana Hughes , Katia Sycara

By linking conceptual theories with observed data, generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular…

Methodology · Statistics 2022-08-15 Kris Sankaran , Susan P. Holmes

Strip-plot designs are very useful when the treatments have a factorial structure and the factors levels are hard-to-change. We develop a randomization-based theory of causal inference from such designs in a potential outcomes framework.…

Statistics Theory · Mathematics 2018-05-18 Fatemah A. Alquallaf , S. Huda , Rahul Mukerjee

Inferential relations govern our concept use. In order to understand a concept it has to be located in a space of implications. There are different kinds of conditions for statements, i.e. that the conditions represent different kinds of…

Artificial Intelligence · Computer Science 2020-07-07 Florian Richter

We establish nonparametric identification in a class of so-called index models using a novel approach that relies on general topological results. Our proof strategy requires substantially weaker conditions on the functions and distributions…

Econometrics · Economics 2020-04-20 Mogens Fosgerau , Dennis Kristensen

Heyting-Lewis Logic is the extension of intuitionistic propositional logic with a strict implication connective that satisfies the constructive counterparts of axioms for strict implication provable in classical modal logics. Variants of…

Logic · Mathematics 2026-03-02 Jim de Groot , Tadeusz Litak , Dirk Pattinson

Starting with two supercompact cardinals we produce a generic extension of the universe in which a principle that we call ${\rm GM}^+(\omega_3,\omega_1)$ holds. This principle implies ${\rm ISP}(\omega_2)$ and ${\rm ISP}(\omega_3)$, and…

Logic · Mathematics 2019-05-21 Rahman Mohammadpour , Boban Velickovic

A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure and prior to favor interpretable models. Fundamentally,…

Machine Learning · Computer Science 2020-09-08 Homayun Afrabandpey , Tomi Peltola , Juho Piironen , Aki Vehtari , Samuel Kaski

The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions. Uncovering such implications typically amounts…

Computation and Language · Computer Science 2019-06-19 Johannes Bjerva , Yova Kementchedjhieva , Ryan Cotterell , Isabelle Augenstein

Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models. However, we raise a fundamental question regarding the reliability of the…

Computation and Language · Computer Science 2023-05-25 Yuxin Ren , Qipeng Guo , Zhijing Jin , Shauli Ravfogel , Mrinmaya Sachan , Bernhard Schölkopf , Ryan Cotterell

Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph. Causal models can be viewed as a special class of graphical models that not only represent the distribution of the observed system…

Methodology · Statistics 2017-06-29 Christina Heinze-Deml , Marloes H. Maathuis , Nicolai Meinshausen

In this paper we construct a Beth model for intuitionistic functionals of high types and use it to create a relatively strong theory SLP containg intuitionistic principles for functionals, in particular, the theory of the "creating…

Logic · Mathematics 2014-03-13 Farida Kachapova

We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates…

'Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an object's pose and that of its constituent parts. This modelling assumption should lead to robustness to viewpoint changes since…

Machine Learning · Computer Science 2021-01-07 Lewis Smith , Lisa Schut , Yarin Gal , Mark van der Wilk

The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to…

Artificial Intelligence · Computer Science 2023-08-09 Amina Ghoul , Itheri Yahiaoui , Anne Verroust-Blondet , Fawzi Nashashibi

We show that the intuitionistic propositional logic with a Galois connection (IntGC), introduced by the authors, has the finite model property.

Logic · Mathematics 2014-03-26 Wojciech Dzik , Jouni Järvinen , Michiro Kondo

Imprimitivity theorems provide a fundamental tool for studying the representation theory and structure of crossed-product C*-algebras. In this work, we show that the Imprimitivity Theorem for induced algebras, Green's Imprimitivity Theorem…

Operator Algebras · Mathematics 2007-05-23 Siegfried Echterhoff , S. Kaliszewski , John Quigg , Iain Raeburn

We introduce a very natural topology on the set of total orderings of monomials of any algebra having a countable basis over a field. This topological space and some notable subspaces are compact. This topological framework allows us to…

Rings and Algebras · Mathematics 2011-06-02 Roberto Boldini

Most major retailers today have multiple divisions focused on various aspects, such as marketing, supply chain, online customer experience, store customer experience, employee productivity, and vendor fulfillment. They also regularly…

Machine Learning · Computer Science 2025-12-16 Pranav Gupta , Nithin Surendran

The pursuit of interpretable artificial intelligence has led to significant advancements in the development of methods that aim to explain the decision-making processes of complex models, such as deep learning systems. Among these methods,…

Machine Learning · Computer Science 2024-10-29 Yihao Zhang
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