Publications

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
Angela Yuan
Chris Junchi Li
Gauthier Gidel
Michael Jordan
Quanquan Gu
Simon Shaolei Du
We consider the problem of solving stochastic monotone variational inequalities with a separable structure using a stochastic first-order or… (voir plus)acle. Building on standard extragradient for variational inequalities we propose a novel algorithm---stochastic \emph{accelerated gradient-extragradient} (AG-EG)---for strongly monotone variational inequalities (VIs). Our approach combines the strengths of extragradient and Nesterov acceleration. By showing that its iterates remain in a bounded domain and applying scheduled restarting, we prove that AG-EG has an optimal convergence rate for strongly monotone VIs. Furthermore, when specializing to the particular case of bilinearly coupled strongly-convex-strongly-concave saddle-point problems, including bilinear games, our algorithm achieves fine-grained convergence rates that match the respective lower bounds, with the stochasticity being characterized by an additive statistical error term that is optimal up to a constant prefactor.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
openreview.net
openreview.net
Retrieval-Augmented Multiple Instance Learning
Yufei Cui
Ziquan Liu
Yixin CHEN
Yuchen Lu
Xinyue Yu
Xue Liu
Tei-Wei Kuo
Miguel R. D. Rodrigues
Chun Jason Xue
Antoni B. Chan
Multiple Instance Learning (MIL) is a crucial weakly supervised learning method applied across various domains, e.g., medical diagnosis base… (voir plus)d on whole slide images (WSIs). Recent advancements in MIL algorithms have yielded exceptional performance when the training and test data originate from the same domain, such as WSIs obtained from the same hospital. However, this paper reveals a performance deterioration of MIL models when tested on an out-of-domain test set, exemplified by WSIs sourced from a novel hospital. To address this challenge, this paper introduces the Retrieval-AugMented MIL (RAM-MIL) framework, which integrates Optimal Transport (OT) as the distance metric for nearest neighbor retrieval. The development of RAM-MIL is driven by two key insights. First, a theoretical discovery indicates that reducing the input's intrinsic dimension can minimize the approximation error in attention-based MIL. Second, previous studies highlight a link between input intrinsic dimension and the feature merging process with the retrieved data. Empirical evaluations conducted on WSI classification demonstrate that the proposed RAM-MIL framework achieves state-of-the-art performance in both in-domain scenarios, where the training and retrieval data are in the same domain, and more crucially, in out-of-domain scenarios, where the (unlabeled) retrieval data originates from a different domain. Furthermore, the use of the transportation matrix derived from OT renders the retrieval results interpretable at the instance level, in contrast to the vanilla
2023-09-20
NeurIPS.cc/2023/Conference (poster)
openreview.net
openreview.net
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
Sokhna Diarra Mbacke
Florence Clerc
Pascal Germain
2023-09-20
NeurIPS.cc/2023/Conference (spotlight)
doi.org
openreview.net
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
Fuyuan Lyu
Xing Tang
Dugang Liu
Chen Ma
Weihong Luo
Liang Chen
xiuqiang He
Xue Liu
2023-09-20
NeurIPS.cc/2023/Conference (poster)
openreview.net
openreview.net
Conserving avian evolutionary history can effectively safeguard future benefits for people
Rikki Gumbs
Claudia L. Gray
Michael Hoffmann
Rafael Molina-Venegas
Nisha Owen
Laura J. Pollock
Phylogenetic diversity (PD)—the evolutionary history of a set of species—is conceptually linked to the maintenance of yet-to-be-discover… (voir plus)ed benefits from biodiversity or “option value.” We used global phylogenetic and utilization data for birds to test the PD option value link, under the assumption that the performance of sets of PD-maximizing species at capturing known benefits is analogous to selecting the same species at a point in human history before these benefits were realized. PD performed better than random at capturing utilized bird species across 60% of tests, with performance linked to the phylogenetic dispersion and prevalence of each utilization category. Prioritizing threatened species for conservation by the PD they encapsulate performs comparably to prioritizing by their functional distinctiveness. However, species selected by each metric show low overlap, indicating that we should conserve both components of biodiversity to effectively conserve a variety of uses. Our findings provide empirical support for the link between evolutionary history and benefits for future generations.
2023-09-19
Science Advances (publié)
doi.org
Distinctive Whole-brain Cell-Types Predict Tissue Damage Patterns in Thirteen Neurodegenerative Conditions
Veronika Pak
Quadri Adewale
Danilo Bzdok
Mahsa Dadar
Yashar Zeighami
Yasser Iturria-Medina
Abstract For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most whole-brain neu… (voir plus)rodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell-types’ contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell-types extensively predicts tissue damage in eleven neurodegenerative disorders, including early- and late-onset Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, frontotemporal dementia, and tauopathies. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell-types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, on spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorders pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.
2023-09-18
eLife (publié)
doi.org
M-TAG: A modular teaching-aid for Geant4
Liam Carroll
S. Enger
2023-09-18
Heliyon (publié)
doi.org
Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction
Hadi Hojjati
Mohammadreza Sadeghi
Narges Armanfard
2023-09-16
Lecture Notes in Computer Science (publié)
doi.org
Estimating the population effectiveness of interventions against COVID-19 in France: a modelling study
Iris Ganser
David L Buckeridge
Jane M Heffernan
M. Prague
Rodolphe Thiébaut
Background Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty … (voir plus)persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness. Methods To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout. Results The first lockdown was the most effective, reducing transmission by 84% (95% confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74% (69-77) and 11% (9-18), respectively). A 6pm curfew was more effective than one at 8 pm (68% (66-69) vs. 48% (45-49) reduction), while school closures reduced transmission by 15% (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 194% (95% prediction interval (PI) 74-424) more deaths and 1,488,000 or 340% (136-689) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted. Conclusion Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the CEPI initiative for vaccine availability.
2023-09-13
medRxiv (prépublication)
doi.org
Addressing uncertainty when projecting marine species' distributions under climate change
Sarah C. Davies
Patrick L. Thompson
Catalina Gomez
Jessica Nephin
Anders Knudby
Ashley E. Park
Sarah K. Friesen
Laura J. Pollock
Emily M. Rubidge
Sean C. Anderson
Josephine C. Iacarella
Devin A. Lyons
Andrew MacDonald
Andrew McMillan
Eric J. Ward
Amber M. Holdsworth
Neil Swart
Jeff Price
Karen L. Hunter
2023-09-12
Ecography (publié)
doi.org
Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review
Lily Puterman-salzman
Jory Katz
Howard Bergman
Roland Grad
Vladimir Khanassov
Genevieve Gore
Isabelle Vedel
Machelle Wilchesky
Narges Armanfard
Negar Ghourchian
S. A. Rahimi
Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence … (voir plus)(AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O’Malley’s and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson’s or Huntington’s disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.
2023-09-12
Dementia and Geriatric Cognitive Disorders Extra (publié)
doi.org
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
Hao-Jun Michael Shi
Tsung-Hsien Lee
Shintaro Iwasaki
Jose Gallego-Posada
Zhijing Li
Kaushik Rangadurai
Dheevatsa Mudigere
Michael G. Rabbat
2023-09-11
ArXiv (prépublication)
doi.org
arxiv.org