Publications

Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks
Daniel Beaglehole
Ioannis Mitliagkas
Atish Agarwala
2024-02-07
ArXiv (preprint)
doi.org
arxiv.org
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet
Ola Ahmad
Audrey Durand
2024-02-07
ArXiv (preprint)
doi.org
arxiv.org
The Impact of Educational Materials on Parental Anxiety and Productivity: A Clinical Trial in Pediatric Appendicitis
Julia Ferreira
Nadia Safa
Fabio Botelho
Robin Petroze
Hussein Wissanji
Dan Poenaru
Pramod Puligandla
Kenneth Shaw
Maeve Trudeau
Sherif Emil
Elena Guadagno
Jean-Martin Laberge
2024-02-07
Journal of Pediatric Surgery (published)
doi.org
AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments
Tianyi Yang
Tianze Yang
Andrew Liu
Jie Tang
Na An
Shaoshan Liu
Xue (Steve) Liu
2024-02-06
Connection Science (published)
doi.org
arxiv.org
Computing Approximate Nash Equilibria for Integer Programming Games
Aloïs Duguet
Margarida Carvalho
Gabriele Dragotto
Sandra-ulrich Ngueveu
2024-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-02-06
ArXiv (preprint)
doi.org
arxiv.org
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Pablo Lemos
Sammy N. Sharief
Nikolay Malkin
Laurence Perreault-Levasseur
Yashar Hezaveh
2024-02-06
ArXiv (preprint)
doi.org
arxiv.org
Polynomial Lawvere Logic
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon D. Plotkin
2024-02-05
ArXiv (preprint)
doi.org
arxiv.org
Toward Human-AI Alignment in Large-Scale Multi-Player Games
Sugandha Sharma
Guy Davidson
Khimya Khetarpal
Anssi Kanervisto
Udit Arora
Katja Hofmann
Ida Momennejad
Achieving human-AI alignment in complex multi-agent games is crucial for creating trustworthy AI agents that enhance gameplay. We propose a … (see more)method to evaluate this alignment using an interpretable task-sets framework, focusing on high-level behavioral tasks instead of low-level policies. Our approach has three components. First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space. This task space serves as a basis set for a behavior manifold capturing interpretable axes: fight-flight, explore-exploit, and solo-multi-agent. Second, we train an AI agent to play Bleeding Edge using a Generative Pretrained Causal Transformer and measure its behavior. Third, we project human and AI gameplay to the proposed behavior manifold to compare and contrast. This allows us to interpret differences in policy as higher-level behavioral concepts, e.g., we find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. Furthermore, AI agents predominantly engage in solo play, while humans often engage in cooperative and competitive multi-agent patterns. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications. Our study advances the alignment discussion in AI and especially generative AI research, offering a measurable framework for interpretable human-agent alignment in multiplayer gaming.
2024-02-05
ArXiv (preprint)
doi.org
arxiv.org
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera
Jacopo Andreoli
Davide Dalle Pezze
Mirco Ravanelli
Gian Antonio Susto
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has … (see more)improved considerably upon traditional model-based and statistical approaches in terms of predictive performance. However, in order to optimally plan maintenance operations, it is also important to quantify the uncertainty inherent to the predictions. This issue can be addressed by turning standard frequentist neural networks into Bayesian neural networks, which are naturally capable of providing confidence intervals around the estimates. Several methods exist for training those models. Researchers have focused mostly on parametric variational inference and sampling-based techniques, which notoriously suffer from limited approximation power and large computational burden, respectively. In this work, we use Stein variational gradient descent, a recently proposed algorithm for approximating intractable distributions that overcomes the drawbacks of the aforementioned techniques. In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation. Furthermore, we propose a method to enhance performance based on the uncertainty information provided by the Bayesian models. We release the source code at https://github.com/lucadellalib/bdl-rul-svgd.
2024-02-02
ArXiv (preprint)
doi.org
arxiv.org
Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media
Jwen Fai Low
Benjamin Fung
Farkhund Iqbal
Claude Fachkha
2024-02-02
ArXiv (preprint)
doi.org
arxiv.org
A database of the healthy human spinal cord morphometry in the PAM50 template space
Jan Valošek
Sandrine Bédard
Miloš Keřkovský
Tomáš Rohan
Julien Cohen-Adad
Abstract Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of … (see more)spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.
2024-02-02
Imaging Neuroscience (published)
doi.org