Publications

Bugs in the Data: How ImageNet Misrepresents Biodiversity
Alexandra Luccioni
David Rolnick
ImageNet-1k is a dataset often used for benchmarking machine learning (ML) models and evaluating tasks such as image recognition and object … (see more)detection. Wild animals make up 27% of ImageNet-1k but, unlike classes representing people and objects, these data have not been closely scrutinized. In the current paper, we analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set, with the participation of expert ecologists. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled, with some classes having >90% of images incorrect. We also find that both the wildlife-related labels and images included in ImageNet-1k present significant geographical and cultural biases, as well as ambiguities such as artificial animals, multiple species in the same image, or the presence of humans. Our findings highlight serious issues with the extensive use of this dataset for evaluating ML systems, the use of such algorithms in wildlife-related tasks, and more broadly the ways in which ML datasets are commonly created and curated.
2023-01-01
AAAI (published)
doi.org
arxiv.org
Cache-Efficient Dynamic Programming MDP Solver
Jaël Champagne Gareau
Guillaume Gosset
Éric Beaudry
Vladimir Makarenkov
2023-01-01
European Conference on Artificial Intelligence (published)
doi.org
Can AI Read the Minds of Corporate Executives?
Nicolas Chapados
Zhenzhen Fan
Ruslan Goyenko
Issam Hadj Laradji
Fred Liu
Chengyu Zhang
2023-01-01
SSRN Electronic Journal (published)
doi.org
Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?
Shreya Chowdhary
Anna Kawakami
Jina Suh
Mary L Gray
Alexandra Olteanu
Koustuv Saha
2023-01-01
FAccT (published)
doi.org
arxiv.org
A circulating proteome-informed prognostic model of COVID-19 disease activity that relies on 1 routinely available clinical laboratories 2
William Ma
Antoine Soulé
Karine Tremblay
Simon Rousseau
Amin Emad
Abstract
2023-01-01
(published)
www.semanticscholar.org
Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian FATRAS
Guy Wolf
Yoshua Bengio
2023-01-01
arXiv.org (preprint)
doi.org
Constant Memory Attentive Neural Processes
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2023-01-01
arXiv.org (preprint)
doi.org
Contrast-agnostic deep learning–based registration pipeline: Validation in spinal cord multimodal MRI data
E. Beal
Julien Cohen-Adad
2023-01-01
Aperture Neuro (published)
doi.org
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Fine-grained Understanding
Le Zhang
Md. Rabiul Awal
Aishwarya Agrawal
2023-01-01
arXiv.org (preprint)
doi.org
Contrastive Positive Unlabeled Learning
Anish Acharya
Sujay Sanghavi
Li Jing
Bhargav Bhushanam
Michael Rabbat
I. Dhillon
Self-supervised pretraining on unlabeled data followed by supervised fine-tuning on labeled data is a popular paradigm for learning from lim… (see more)ited labeled examples. We extend this paradigm to the classical positive unlabeled (PU) setting, where the task is to learn a binary classifier given only a few labeled positive samples, and (often) a large amount of unlabeled samples (which could be positive or negative). We first propose a simple extension of standard infoNCE family of contrastive losses, to the PU setting; and show that this learns superior representations, as compared to existing unsupervised and supervised approaches. We then develop a simple methodology to pseudo-label the unlabeled samples using a new PU-specific clustering scheme; these pseudo-labels can then be used to train the final (positive vs. negative) classifier. Our method handily outperforms state-of-the-art PU methods over several standard PU benchmark datasets, while not requiring a-priori knowledge of any class prior (which is a common assumption in other PU methods). We also provide a simple theoretical analysis that motivates our methods.
2023-01-01
(published)
www.semanticscholar.org
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
Eduard Gorbunov
Adrien Taylor
Samuel Horváth
Gauthier Gidel
Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone… (see more) machine learning applications, we follow the line of works (Diakonikolas et al., 2021; Lee & Kim, 2021; Pethick et al., 2022; Bohm,2022) aiming at going beyond monotonicity by considering the weaker *negative comonotonicity* assumption. In this work, we provide tight complexity analyses for the Proximal Point (PP), Extragradient (EG), and Optimistic Gradient (OG) methods in this setup, closing several questions on their working guarantees beyond monotonicity. In particular, we derive the first non-asymptotic convergence rates for PP under negative comonotonicity and star-negative comonotonicity and show their tightness via constructing worst-case examples; we also relax the assumptions for the last-iterate convergence guarantees for EG and OG and prove the tightness of the existing best-iterate guarantees for EG and OG via constructing counter-examples.
2023-01-01
ICML (published)
openreview.net
openreview.net
Cutting Planes from the Branch-and-Bound Tree: Challenges and Opportunities
Claudio Contardo
Andrea Lodi
Andrea Tramontani
2023-01-01
INFORMS J. Comput. (published)
doi.org