Publications

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Gaurav Sahu
Abhay Puri
Juan A. Rodriguez
Alexandre Drouin
Perouz Taslakian
Valentina Zantedeschi
Alexandre Lacoste
David Vazquez
Nicolas Chapados
Chris Pal
Sai Rajeswar
Issam Hadj Laradji
2024-07-08
ArXiv (preprint)
doi.org
arxiv.org
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng
Florence Regol
Mark Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (see more) intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. The model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPPs.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
Jinsoo Yoo
Yunpeng Liu
Frank Wood
Geoff Pleiss
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
Listenable Maps for Audio Classifiers
Francesco Paissan
Mirco Ravanelli
Cem (Yusuf) Subakan
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
Lookbehind-SAM: k steps back, 1 step forward
Goncalo Mordido
Pranshu Malviya
Aristide Baratin
Sarath Chandar
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
proceedings.mlr.press
openreview.net
Memory Efficient Neural Processes via Constant Memory Attention Block
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
proceedings.mlr.press
openreview.net
Modeling Caption Diversity in Contrastive Vision-Language Pretraining
Samuel Lavoie
Polina Kirichenko
Mark Ibrahim
Mahmoud Assran
Andrew Gordon Wilson
Aaron Courville
Nicolas Ballas
There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its … (see more)caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an image. In this work, we introduce Llip, Latent Language Image Pretraining, which models the diversity of captions that could match an image. Llip's vision encoder outputs a set of visual features that are mixed into a final representation by conditioning on information derived from the text. We show that Llip outperforms non-contextualized baselines like CLIP and SigLIP on a variety of tasks even with large-scale encoders. Llip improves zero-shot classification by an average of 2.9\% zero-shot classification benchmarks with a ViT-G/14 encoder. Specifically, Llip attains a zero-shot top-1 accuracy of 83.5\% on ImageNet outperforming a similarly sized CLIP by 1.4\%. We also demonstrate improvement on zero-shot retrieval on MS-COCO by 6.0\%. We provide a comprehensive analysis of the components introduced by the method and demonstrate that Llip leads to richer visual representations.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
Nearest Neighbour Score Estimators for Diffusion Generative Models
Matthew Niedoba
Dylan Green
Saeid Naderiparizi
Vasileios Lioutas
Jonathan Wilder Lavington
Xiaoxuan Liang
Yunpeng Liu
Ke Zhang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank Wood
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most com… (see more)monly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a novel nearest neighbour score function estimator which utilizes multiple samples from the training set to dramatically decrease estimator variance. We leverage our low variance estimator in two compelling applications. Training consistency models with our estimator, we report a significant increase in both convergence speed and sample quality. In diffusion models, we show that our estimator can replace a learned network for probability-flow ODE integration, opening promising new avenues of future research. Code will be released upon paper acceptance.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
A Persuasive Approach to Combating Misinformation
Safwan Hossain
Andjela Mladenovic
Yiling Chen
Gauthier Gidel
Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine… (see more) learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user's future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi
Mohammad Havaei
Negar Rostamzadeh
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning
Frédéric Beaupré
Anthony Bilodeau
Theresa Wiesner
Gabriel Leclerc
Mado Lemieux
Gabriel Nadeau
Katrine Castonguay
Bolin Fan
Simon Labrecque
Renée Hložek
Paul De Koninck
Christian Gagné
Flavie Lavoie-Cardinal
Ca2+ imaging methods are widely used for studying cellular activity in the brain, allowing detailed analysis of dynamic processes across var… (see more)ious scales. Enhanced by high-contrast optical microscopy and fluorescent Ca2+ sensors, this technique can be used to reveal localized Ca2+ fluctuations within neurons, including in sub-cellular compartments, such as the dendritic shaft or spines. Despite advances in Ca2+ sensors, the analysis of miniature Synaptic Calcium Transients (mSCTs), characterized by variability in morphology and low signal-to-noise ratios, remains challenging. Traditional threshold-based methods struggle with the detection and segmentation of these small, dynamic events. Deep learning (DL) approaches offer promising solutions but are limited by the need for large annotated datasets. Positive Unlabeled (PU) learning addresses this limitation by leveraging unlabeled instances to increase dataset size and enhance performance. This approach is particularly useful in the case of mSCTs that are scarce and small, associated with a very small proportion of the foreground pixels. PU learning significantly increases the effective size of the training dataset, improving model performance. Here, we present a PU learning-based strategy for detecting and segmenting mSCTs. We evaluate the performance of two 3D deep learning models, StarDist-3D and 3D U-Net, which are well established for the segmentation of small volumetric structures in microscopy datasets. By integrating PU learning, we enhance the 3D U-Net’s performance, demonstrating significant gains over traditional methods. This work pioneers the application of PU learning in Ca2+ imaging analysis, offering a robust framework for mSCT detection and segmentation. We also demonstrate how this quantitative analysis pipeline can be used for subsequent mSCTs feature analysis. We characterize morphological and kinetic changes of mSCTs associated with the application of chemical long-term potentiation (cLTP) stimulation in cultured rat hippocampal neurons. Our data-driven approach shows that a cLTP-inducing stimulus leads to the emergence of new active dendritic regions and differently affects mSCTs subtypes.
2024-07-08
bioRxiv (preprint)
doi.org
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet
Ola Ahmad
Audrey Durand
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net