7 citations · 11 across the 15 of their papers we have counts for
15 papers
Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models
Mario Leiva, Yue Ma, Qinru Qiu +2
Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as m…
AI Debaters are More Persuasive when Arguing in Alignment with Their Own Beliefs
María Victoria Carro, Denise Alejandra Mester, Facundo Nieto +9
The core premise of AI debate as a scalable oversight technique is that it is harder to lie convincingly than to refute a lie, enabling the judge to identify the correct position.…
Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment
María Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco +4
Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process…
Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning
Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Dyuman Aditya +5
We introduce Lattice Annotated Temporal (LAT) Logic, an extension of Generalized Annotated Logic Programs (GAPs) that incorporates temporal reasoning and supports open-world semant…
A Conceptual Framework for AI Capability Evaluations
María Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco +7
As AI systems advance and integrate into society, well-designed and transparent evaluations are becoming essential tools in AI governance, informing decisions by providing evidence…
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments
Mario Leiva, Noel Ngu, Joshua Shay Kricheli +6
The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence a…
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection
Aditya Taparia, Noel Ngu, Mario Leiva +6
Although fusing multiple sensor modalities can enhance object detection performance, existing fusion approaches often overlook subtle variations in environmental conditions and sen…
Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
Noel Ngu, Aditya Taparia, Gerardo I. Simari +5
Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often l…
Abduction of Domain Relationships from Data for VQA
Al Mehdi Saadat Chowdhury, Paulo Shakarian, Gerardo I. Simari
In this paper, we study the problem of visual question answering (VQA) where the image and query are represented by ASP programs that lack domain data. We provide an approach that…
Probabilistic Foundations for Metacognition via Hybrid-AI
Paulo Shakarian, Gerardo I. Simari, Nathaniel D. Bastian
Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and,…
Do Large Language Models Show Biases in Causal Learning?
Maria Victoria Carro, Francisca Gauna Selasco, Denise Alejandra Mester +4
Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process…
Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing
Divyagna Bavikadi, Dyuman Aditya, Devendra Parkar +4
The ability to generate artificial human movement patterns while meeting location and time constraints is an important problem in the security community, particularly as it enables…
Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach
Gaston Zanitti, Yamil Soto, Valentin Iovene +4
Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the b…
On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)
Jose N. Paredes, Juan Carlos L. Teze, Gerardo I. Simari +1
With the availability of large datasets and ever-increasing computing power, there has been a growing use of data-driven artificial intelligence systems, which have shown their pot…
Belief Revision in Structured Probabilistic Argumentation
Paulo Shakarian, Gerardo I. Simari, Marcelo A. Falappa
In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradic…