Publications

Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation
Amin Abyaneh
Hsiu-Chin Lin
Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert’s behavior. However… (see more), relying solely on the expert’s data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided utilizing nonlinear dynamical systems, acting as high-level motion planners, in conjunction with the Lyapunov stability theorem. Yet, these methods are prone to inaccurate policies, high computational cost, sample inefficiency, or quasi stability when replicating complex and highly nonlinear trajectories. To mitigate this problem, we present an approach for learning a globally stable nonlinear dynamical system as a motion planning policy. We model the nonlinear dynamical system as a parametric polynomial and learn the polynomial’s coefficients jointly with a Lyapunov candidate. To showcase its success, we compare our method against the state of the art in simulation and conduct real-world experiments with the Kinova Gen3 Lite manipulator arm. Our experiments demonstrate the sample efficiency and reproduction accuracy of our method for various expert trajectories, while remaining stable in the face of perturbations.
2023-12-01
Proceedings of The 7th Conference on Robot Learning (published)
doi.org
proceedings.mlr.press
Advancing Clinical Psychiatry: Integration of Clinical and Omics Data Using Machine Learning
Bill Qi
Yannis Trakadis
2023-11-30
Biological Psychiatry (published)
doi.org
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Hassan Muhammad
Idris Abdulmumin
Abinew Ayele
Nedjma OUSIDHOUM
David Ifeoluwa Adelani
Seid Muhie Yimam
Ibrahim Ahmad
Meriem Beloucif
Saif Mohammad
Sebastian Ruder
Oumaima Hourrane
Alipio Jorge
Pavel Brazdil
Felermino Ali
Davis David
Salomey Osei
Bello Shehu-Bello
Falalu Lawan
Tajuddeen Gwadabe
Samuel Rutunda … (see 7 more)
Tadesse Belay
Wendimu Baye Messelle
Hailu Balcha
Sisay Adugna Chala
Hagos Gebremichael
Bernard Opoku
Stephen Arthur
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (published)
doi.org
openreview.net
Anastomotic leak rates after repair of mesenteric bucket-handle injuries: A multi-center retrospective cohort study.
Chathurika S. Dhanasekara
Brianna Marschke
Erin Morris
Bryan S. Bashrum
K. Shrestha
Robyn E. Richmond
S. Dissanaike
A. Ko
Lakshika Tennakoon
Eric M. Campion
Frank N. Wood
Maggie Brandt
Grace Ng
Justin L Regner
Stacey Keith
M. Mcnutt
Heather Kregel
R. Gandhi
Thomas J. Schroeppel
D. Margulies … (see 5 more)
Y. Hashim
Joseph A. Herrold
Mallory Goetz
LeRone Simpson
Doan Xuan-Lan
2023-11-30
The American Journal of Surgery (published)
doi.org
Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge
Vincent Andrearczyk
Valentin Oreiller
Sarah Boughdad
Catherine Cheze Le Rest
Olena Tankyevych
Hesham M. Elhalawani
Mario Jreige
John O. Prior
Martin Vallières
Dimitris Visvikis
Mathieu Hatt
Adrien Depeursinge
2023-11-30
Medical Image Analysis (published)
doi.org
Balaur: Language Model Pretraining with Lexical Semantic Relations
Andrei Mircea
Jackie CK Cheung
2023-11-30
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)
doi.org
openreview.net
Better Quality Pre-training Data and T5 Models for African Languages
Akintunde Oladipo
Mofetoluwa Adeyemi
Orevaoghene Ahia
Abraham Toluwase Owodunni
Odunayo Ogundepo
David Ifeoluwa Adelani
Jimmy Lin
In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawl… (see more)s have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (published)
doi.org
openreview.net
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Parishad BehnamGhader
Santiago Miret
Siva Reddy
Augmenting pretrained language models with retrievers to select the supporting documents has shown promise in effectively solving common NLP… (see more) problems, including language modeling and question answering, in an interpretable way. In this paper, we first study the strengths and weaknesses of different retriever-augmented language models (REALM,
2023-11-30
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)
doi.org
openreview.net
Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo
Tajuddeen Gwadabe
Clara Rivera
Jonathan Clark
Sebastian Ruder
David Adelani
Bonaventure Dossou
Abdou Diop
Claytone Sikasote
Gilles Hacheme
Happy Buzaaba
Ignatius Ezeani
Rooweither Mabuya
Salomey Osei
Chris Emezue
Albert Kahira
Shamsuddeen Muhammad
Akintunde Oladipo
Abraham Owodunni
Atnafu Tonja … (see 24 more)
Iyanuoluwa Shode
Akari Asai
Anuoluwapo Aremu
Ayodele Awokoya
Bernard Opoku
Chiamaka Chukwuneke
Christine Mwase
Clemencia Siro
Stephen Arthur
Tunde Ajayi
Verrah Otiende
Andre Rubungo
Boyd Sinkala
Daniel Ajisafe
Emeka Onwuegbuzia
Falalu Lawan
Ibrahim Ahmad
Jesujoba Alabi
Chinedu Mbonu
Mofetoluwa Adeyemi
Mofya Phiri
Orevaoghene Ahia
Ruqayya Iro
Sonia Adhiambo
2023-11-30
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)
doi.org
openreview.net
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Shakeeb Murtaza
Soufiane Belharbi
Marco Pedersoli
Aydin Sarraf
Éric Granger
2023-11-30
Image and Vision Computing (published)
doi.org
arxiv.org
Dissociable influences of maternal vs paternal Alzheimer’s risk on neurocognitive and cardiovascular health in men and women
Chloé Savignac
Frederic St‐Onge
Sylvia Villeneuve
AmanPreet Badhwar
Sarah A Gagliano Taliun
Sali Farhan
Maiya R. Geddes
Yasser Iturria Medina
Judes Poirier
R. Nathan Spreng
Danilo Bzdok
We uncovered IPs of AD susceptibility differently expressed in male and female probands and affected by the diagnosed parent’s sex. Matern… (see more)al inheritance highlighted memory performance in both sexes, whereas paternal inheritance was particularly linked to cardiovascular health in males. The inheritance of the IPs was reflected in the brain structure at both superficial and deeper layers of the cortex. As the first study of its kind, our cross‐generational analysis of matri‐ vs. patrilinear AD risk bridges the epidemiological and clinical literature by leveraging the power of ∼1,000 patient visits. Our completely data‐driven framework ultimately dissociated phenotypes of maternal and paternal AD risk single‐handedly expressed in male and female probands.
2023-11-30
Alzheimer's & Dementia (published)
doi.org
EpiK-Eval: Evaluation for Language Models as Epistemic Models
Gabriele Prato
Jerry Huang
Prasanna Parthasarathi
Shagun Sodhani
A. Chandar
In the age of artificial intelligence, the role of large language models (LLMs) is becoming increasingly central. Despite their growing prev… (see more)alence, their capacity to consolidate knowledge from different training documents—a crucial ability in numerous applications—remains unexplored. This paper presents the first study examining the capability of LLMs to effectively combine such information within their parameter space. We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives. Evaluations across various LLMs reveal significant weaknesses in this domain. We contend that these shortcomings stem from the intrinsic nature of prevailing training objectives. Consequently, we advocate for refining the approach towards knowledge consolidation, as it harbors the potential to dramatically improve their overall effectiveness and performance. The findings from this study offer insights for developing more robust and reliable LLMs. Our code and benchmark are available at https://github.com/chandar-lab/EpiK-Eval
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (published)
doi.org
openreview.net