Publications

Meta's AI translation model embraces overlooked languages.

David Ifeoluwa Adelani

2024-06-05

Nature (published)

doi.org

Noisy Data Visualization using Functional Data Analysis

Haozhe Chen

Andres Felipe Duque Correa

Guy Wolf

Kevin R. Moon

Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many ex… (see more)isting methods fail to capture the underlying structure of the data. The method called Empirical Intrinsic Geometry (EIG) was previously proposed for performing dimensionality reduction on high dimensional dynamical processes while theoretically eliminating all noise. However, implementing EIG in practice requires the construction of high-dimensional histograms, which suffer from the curse of dimensionality. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that adapts the EIG framework while using approaches from functional data analysis to mitigate the curse of dimensionality. We experimentally demonstrate that the resulting method outperforms a variant of EIG designed for visualization in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.

2024-06-05

ArXiv (preprint)

doi.org

arxiv.org

A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians

Piotr Mirowski

Juliette Love

Kory Wallace Mathewson

Shakir Mohamed

2024-06-05

The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)

doi.org

arxiv.org

Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors

Yuan Yu

Matthew J Knight

Diana Gibson

Sheila F O’Brien

David Buckeridge

W Alton Russell

Abstract Background In Canada’s largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. … (see more)No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Methods Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420 319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Results Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Conclusions Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.

2024-06-05

International Journal of Epidemiology (published)

doi.org

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

Melissa Hall

Samuel J. Bell

Candace Ross

Adina Williams

Michal Drozdzal

Adriana Romero Soriano

Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of … (see more)thoroughly evaluating their performance and identifying potential biases. In pursuit of models that generate images that are realistic, diverse, visually appealing, and consistent with the given prompt, researchers and practitioners often turn to automated metrics to facilitate scalable and cost-effective performance profiling. However, commonly-used metrics often fail to account for the full diversity of human preference; often even in-depth human evaluations face challenges with subjectivity, especially as interpretations of evaluation criteria vary across regions and cultures. In this work, we conduct a large, cross-cultural study to study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images from state-of-the art public APIs. We collect over 65,000 image annotations and 20 survey responses. We contrast human annotations with common automated metrics, finding that human preferences vary notably across geographic location and that current metrics do not fully account for this diversity. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. In addition, the utility of automatic evaluations is dependent on assumptions about their set-up, such as the alignment of feature extractors with human perception of object similarity or the definition of"appeal"captured in reference datasets used to ground evaluations. We recommend steps for improved automatic and human evaluations.

2024-06-05

The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)

doi.org

arxiv.org

Efficient Leverage Score Sampling for Tensor Train Decomposition

Vivek Bharadwaj

Beheshteh T. Rakhshan

Osman Asif Malik

Guillaume Rabusseau

2024-06-04

ArXiv (preprint)

doi.org

arxiv.org

Milnor-Myerson Games and The Principles of Artificial Principal-Agent Problems

Manfred Diaz

Joel Z Leibo

Liam Paull

In this paper, we introduce Milnor-Myerson games, a multiplayer interaction structure at the core of machine learning (ML), to shed light on… (see more) the fundamental principles and implications the artificial principal-agent problem has had in landmark ML results like AlphaGo and large language models (LLMs).

2024-06-04

rl-conference.cc/RLC/2024/Workshop/Finding_the_Frame (poster)

openreview.net

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning

Khimya Khetarpal

Zhaohan Daniel Guo

Bernardo Avila Pires

Yunhao Tang

Clare Lyle

Mark Rowland

Nicolas Heess

Diana Borsa

Arthur Guez

Will Dabney

2024-06-04

ArXiv (preprint)

doi.org

arxiv.org

MOSEAC: Streamlined Variable Time Step Reinforcement Learning

Dong Wang

Giovanni Beltrame

2024-06-03

ArXiv (preprint)

doi.org

arxiv.org

AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages

Jiayi Wang

David Ifeoluwa Adelani

Sweta Agrawal

Marek Masiak

Ricardo Rei

Eleftheria Briakou

Marine Carpuat

Xuanli He

Sofia Bourhim

Andiswa Bukula

Muhidin A. Mohamed

Temitayo Olatoye

Tosin Adewumi

Hamam Mokayed

Christine Mwase

Wangui Kimotho

Foutse Yuehgoh

Aremu Anuoluwapo

Jessica Ojo

Shamsuddeen Hassan Muhammad … (see 41 more)

Salomey Osei

Abdul-Hakeem Omotayo

Chiamaka Ijeoma Chukwuneke

Perez Ogayo

Oumaima Hourrane

Salma El Anigri

Lolwethu Ndolela

Thabiso Mangwana

Shafie Abdi Mohamed

Hassan Ayinde

Ayinde Hassan

Oluwabusayo Olufunke Awoyomi

Lama Alkhaled

sana Sabah al-azzawi

Naome Etori

Millicent Ochieng

Clemencia Siro

Njoroge Kiragu

Samuel Njoroge

Eric Muchiri

Wangari Kimotho

Lyse Naomi Wamba

Daud Abolade

Simbiat Ajao

Iyanuoluwa Shode

Ricky Macharm

Ruqayya Nasir Iro

Saheed Salahudeen Abdullahi

Stephen Moore

Bernard Opoku

Zainab Akinjobi

Abeeb Afolabi

Nnaemeka Casmir Obiefuna

Onyekachi Ogbu

Sam Brian

Sam Ochieng’

Verrah Akinyi Otiende

CHINEDU EMMANUEL MBONU

Toadoum Sari Sakayo

Yao Lu

Pontus Stenetorp

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measur… (see more)ing this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).

2024-06-01

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)

doi.org

arxiv.org

Better entity matching with transformers through ensembles

Jwen Fai Low

Benjamin Fung

Pulei Xiong

2024-06-01

Knowledge-Based Systems (published)

doi.org

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Philipp Thölke

Maxine Arcand-Lavigne