Publications

GRAND for Rayleigh Fading Channels
Syed Mohsin Abbas
Marwan Jalaleddine
Warren Gross
Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND at… (voir plus)tempts to guess the channel-induced noise by generating Test Error Patterns (TEPs), and the sequence of TEP generation is the primary distinction between GRAND variants. In this work, we extend the application of GRAND to multipath frequency non-selective Rayleigh fading communication channels, and we refer to this GRAND variant as Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading conditions of the underlying communication channel, outperforming traditional channel code decoders in scenarios with L spatial diversity branches as well as scenarios with no diversity. Numerical simulation results show that the Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for decoding BCH code (127, 106) and BCH code (127, 113) by
2022-04-29
ArXiv (preprint)
doi.org
arxiv.org
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu
Bhaskar Mitra
Chen Ma
Fernando Diaz
Xue (Steve) Liu
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or… (voir plus) groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric---that incorporates existing user browsing models that have previously been developed for information retrieval---to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.
2022-04-29
ArXiv (preprint)
doi.org
arxiv.org
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Oualid Benkarim
Casey Paquola
Bo-yong Park
Valeria Kebets
Seok-Jun Hong
Reinder Vos de Wael
Shaoshi Zhang
B.T. Thomas Yeo
Michael Eickenberg
Tian Ge
Jean-Baptiste Poline
Boris C Bernhardt
Danilo Bzdok
2022-04-29
PLoS Biology (publié)
doi.org
Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Samantha Dequanter
Maaike Fobelets
Iris Steenhout
Marie-Pierre Gagnon
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Ronald Buyl
Ellen Gorus
2022-04-28
BMC Geriatrics (publié)
doi.org
Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Samantha Dequanter
Maaike Fobelets
Iris Steenhout
Marie-Pierre Gagnon
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Ronald Buyl
Ellen Gorus
2022-04-28
BMC Geriatrics (published)
doi.org
Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
H. Bai
H. Aerts
Martin Vallières
D. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-28
American Journal of Neuroradiology (published)
doi.org
Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
Hai-Yang Bai
H. Aerts
Martin Vallières
David A. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-28
American Journal of Neuroradiology (publié)
doi.org
Two types of human TCR differentially regulate reactivity to self and non-self antigens
Assya Trofimov
Philippe Brouillard
Jean-David Larouche
Jonathan Y. Séguin
Jean-Philippe Laverdure
A. Brasey
Grégory Ehx
D. Roy
Lambert Busque
Silvy Lachance
Sébastien Lemieux
Claude Perreault
2022-04-28
bioRxiv (preprint)
doi.org
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
Luke Y. Prince
Roy Henha Eyono
Ellen Boven
Arna Ghosh
Joseph Pemberton
Franz Scherr
Claudia Clopath
Rui Ponte Costa
Wolfgang Maass
Blake Richards
Cristina Savin
Katharina Wilmes
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them… (voir plus) with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.
2022-04-27
Neurons, Behavior, Data analysis, and Theory (publié)
doi.org
arxiv.org
Don't Freeze Your Embedding: Lessons from Policy Finetuning in Environment Transfer
Victoria Dean
Daniel Toyama
Doina Precup
A common occurrence in reinforcement learning (RL) research is making use of a pretrained vision stack that converts image observations to l… (voir plus)atent vectors. Using a visual embedding in this way leaves open questions, though: should the vision stack be updated with the policy? In this work, we evaluate the effectiveness of such decisions in RL transfer settings. We introduce policy update formulations for use after pretraining in a different environment and analyze the performance of such formulations. Through this evaluation, we also detail emergent metrics of benchmark suites and present results on Atari and AndroidEnv.
2022-04-27
ICLR.cc/2022/Workshop/GPL (poster)
openreview.net
openreview.net
A Probabilistic Perspective on Reinforcement Learning via Supervised Learning
Alexandre Piché
Rafael Pardinas
David Vazquez
Chris Pal
2022-04-27
ICLR.cc/2022/Workshop/GPL (poster)
openreview.net
openreview.net
Accepted Tutorials at The Web Conference 2022
Riccardo Tommasini
Senjuti Basu Roy
Xuan Wang
Hongwei Wang
Heng Ji
Jiawei Han
Preslav Nakov
Giovanni Da San Martino
Firoj Alam
Markus Schedl
Elisabeth Lex
Akash Bharadwaj
Graham Cormode
Milan Dojchinovski
Jan Forberg
Johannes Frey
Pieter Bonte
Marco Balduini
Matteo Belcao
Emanuele Della Valle … (voir 53 de plus)
Junliang Yu
Hongzhi Yin
Tong Chen
Haochen Liu
Yiqi Wang
Wenqi Fan
Xiaorui Liu
Jamell Dacon
Lingjuan Lye
Jiliang Tang
Aristides Gionis
Stefan Neumann
Bruno Ordozgoiti
Simon Razniewski
Hiba Arnaout
Shrestha Ghosh
Fabian Suchanek
Lingfei Wu
Yu Chen
Yunyao Li
Bang Liu
Filip Ilievski
Daniel Garijo
Hans Chalupsky
Pedro Szekely
Ilias Kanellos
Dimitris Sacharidis
Thanasis Vergoulis
Nurendra Choudhary
Nikhil Rao
Karthik Subbian
Srinivasan Sengamedu
Chandan K. Reddy
Friedhelm Victor
Bernhard Haslhofer
George Katsogiannis- Meimarakis
Georgia Koutrika
Shengmin Jin
Danai Koutra
Reza Zafarani
Yulia Tsvetkov
Vidhisha Balachandran
Sachin Kumar
Xiangyu Zhao
Bo Chen
Huifeng Guo
Yejing Wang
Ruiming Tang
Yang Zhang
Wenjie Wang
Peng Wu
Fuli Feng
Xiangnan He
This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture st… (voir plus)yle, and 15% of these are hands on.
2022-04-25
The Web Conference (publié)
doi.org