Publications

From Plane Crashes to Algorithmic Harm: Applicability of Safety Engineering Frameworks for Responsible ML
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Edgar Jatho
Joshua A. Kroll
AJung Moon
Negar Rostamzadeh
2023-04-18
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (publié)
doi.org
openreview.net
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
Mohammad Tahaei
Marios Constantinides
Daniele Quercia
Sean Kennedy
Michael Muller
Simone Stumpf
Q. Vera Liao
Ricardo Baeza-Yates
Lora Aroyo
Jess Holbrook
Ewa Luger
Michael Madaio
Ilana Golbin Blumenfeld
Maria De-Arteaga
Jessica Vitak
A.R. Olteanu
2023-04-18
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (publié)
doi.org
arxiv.org
Investigating the Nature of 3D Generalization in Deep Neural Networks
Shoaib Ahmed Siddiqui
David M. Krueger
Thomas M. Breuel
2023-04-18
ArXiv (prépublication)
doi.org
arxiv.org
Playing the System: Can Puzzle Players Teach us How to Solve Hard Problems?
Renata Mutalova
Roman Sarrazin-Gendron
Eddie Cai
Gabriel Richard
Parham Ghasemloo Gheidari
Sébastien Caisse
Rob Knight
Mathieu Blanchette
Attila Szantner
Jérôme Waldispühl
With nearly three billion players, video games are more popular than ever. Casual puzzle games are among the most played categories. These g… (voir plus)ames capitalize on the players’ analytical and problem-solving skills. Can we leverage these abilities to teach ourselves how to solve complex combinatorial problems? In this study, we harness the collective wisdom of millions of players to tackle the classical NP-hard problem of multiple sequence alignment, relevant to many areas of biology and medicine. We show that Borderlands Science players propose solutions to multiple sequence alignment tasks that perform as well or better than standard approaches, while exploring a much larger area of the Pareto-optimal solution space. We also show the strategies of the players, although highly heterogeneous, follow a collective logic that can be mimicked with Behavioral Cloning with minimal performance loss, allowing the players’ collective wisdom to be leveraged for alignment of any sequences.
2023-04-18
International Conference on Human Factors in Computing Systems (publié)
doi.org
Circulating proteins to predict COVID-19 severity
Chen-Yang Su
Sirui Zhou
Edgar Gonzalez-Kozlova
Guillaume Butler-Laporte
Elsa Brunet-Ratnasingham
Tomoko Nakanishi
Wonseok Jeon
David R. Morrison
Laetitia Laurent
Jonathan Afilalo
Marc Afilalo
Danielle Henry
Yiheng Chen
Julia Carrasco-Zanini
Yossi Farjoun
Maik Pietzner
Nofar Kimchi
Zaman Afrasiabi
Nardin Rezk
Meriem Bouab … (voir 43 de plus)
Louis Petitjean
Charlotte Guzman
Xiaoqing Xue
Chris Tselios
Branka Vulesevic
Olumide Adeleye
Tala Abdullah
Noor Almamlouk
Yara Moussa
Chantal DeLuca
Naomi Duggan
Erwin Schurr
Diane Marie Del Valle
Ryan Thompson
Mario A. Cedillo
Eric Schadt
Kai Nie
Nicole W. Simons
Konstantinos Mouskas
Nicolas Zaki
Manishkumar Patel
Hui Xie
Jocelyn Harris
Robert Marvin
Esther Cheng
Kevin Tuballes
Kimberly Argueta
Ieisha Scott
Celia M. T. Greenwood
Clare Paterson
Michael A. Hinterberg
Claudia Langenberg
Vincenzo Forgetta
Joelle Pineau
Vincent Mooser
Thomas Marron
Noam D. Beckmann
Seunghee Kim-schulze
Alexander W. Charney
Sacha Gnjatic
Daniel E. Kaufmann
Miriam Merad
J. Brent Richards
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measu… (voir plus)red 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.
2023-04-16
Scientific Reports (publié)
doi.org
Meta Pseudo Labels for Anomaly Detection via Partially Observed Anomalies
Sinong Zhao
Zhaoyang Yu
Xiaofei Wang
T. Marbach
Gang Wang
Xiaoguang Liu
2023-04-13
Database Systems for Advanced Applications (publié)
doi.org
VulANalyzeR: Explainable Binary Vulnerability Detection with Multi-task Learning and Attentional Graph Convolution
Litao Li
Steven H. H. Ding
Yuan Tian
Benjamin C. M. Fung
Philippe Charland
Weihan Ou
Leo Song
Congwei Chen
2023-04-13
ACM Transactions on Privacy and Security (publié)
doi.org
SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
Shamsuddeen Hassan Muhammad
Idris Abdulmumin
Seid Muhie Yimam
David Ifeoluwa Adelani
Ibrahim Ahmad
Nedjma OUSIDHOUM
Abinew Ayele
Saif Mohammad
Meriem Beloucif
2023-04-12
ArXiv (prépublication)
doi.org
arxiv.org
Adaptive patch foraging in deep reinforcement learning agents
Nathan Wispinski
Andrew Butcher
Kory Mathewson
Craig S Chapman
Matthew Botvinick
Patrick M. Pilarski
Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biologic… (voir plus)al intelligence, this behavioral optimization problem is understudied in artificial intelligence research. Patch foraging is especially amenable to study given that it has a known optimal solution, which may be difficult to discover given current techniques in deep reinforcement learning. Here, we investigate deep reinforcement learning agents in an ecological patch foraging task. For the first time, we show that machine learning agents can learn to patch forage adaptively in patterns similar to biological foragers, and approach optimal patch foraging behavior when accounting for temporal discounting. Finally, we show emergent internal dynamics in these agents that resemble single-cell recordings from foraging non-human primates, which complements experimental and theoretical work on the neural mechanisms of biological foraging. This work suggests that agents interacting in complex environments with ecologically valid pressures arrive at common solutions, suggesting the emergence of foundational computations behind adaptive, intelligent behavior in both biological and artificial agents.
2023-04-11
TMLR (accepté)
doi.org
openreview.net
Simplicity and learning to distinguish arguments from modifiers
Leon Bergen
E. Gibson
Timothy J. O'Donnell
2023-04-11
Journal of Language Modelling (publié)
doi.org
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
Gandharv Patil
Prashanth L.A.
Dheeraj M. Nagaraj
Doina Precup
We study the finite-time behaviour of the popular temporal difference (TD) learning algorithm, when combined with tail-averaging. We derive … (voir plus)finite time bounds on the parameter error of the tail-averaged TD iterate under a step-size choice that does not require information about the eigenvalues of the matrix underlying the projected TD fixed point. Our analysis shows that tail-averaged TD converges at the optimal O (1/t) rate, both in expectation and with high probability. In addition, our bounds exhibit a sharper rate of decay for the initial error (bias), which is an improvement over averaging all iterates. We also propose and analyse a variant of TD that incorporates regularisation, and show that this variant fares favourably in problems with ill-conditioned features.
2023-04-10
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces
Charline Le Lan
Joshua Greaves
Jesse Farebrother
Mark Rowland
Fabian Pedregosa
Rishabh Agarwal
Bellemare Marc-Emmanuel
In this paper, we derive an algorithm that learns a principal subspace from sample entries, can be applied when the approximate subspace i… (voir plus)s represented by a neural network, and hence can bescaled to datasets with an effectively infinite number of rows and columns. Our method consistsin defining a loss function whose minimizer is the desired principal subspace, and constructing agradient estimate of this loss whose bias can be controlled.
2023-04-10
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press