Guy Wolf

Biographie

Guy Wolf est professeur agrégé au Département de mathématiques et de statistique de l'Université de Montréal. Ses intérêts de recherche se situent au carrefour de l'apprentissage automatique, de la science des données et des mathématiques appliquées. Il s'intéresse particulièrement aux méthodes d'exploration de données qui utilisent l'apprentissage multiple et l'apprentissage géométrique profond, ainsi qu'aux applications pour l'analyse exploratoire des données biomédicales.

Ses recherches portent sur l'analyse exploratoire des données, avec des applications en bio-informatique. Ses approches sont multidisciplinaires et combinent l'apprentissage automatique, le traitement du signal et les outils mathématiques appliqués. En particulier, ses travaux récents utilisent une combinaison de géométries de diffusion et d'apprentissage profond pour trouver des modèles émergents, des dynamiques et des structures dans les mégadonnées à grande dimension (par exemple, dans la génomique et la protéomique de la cellule unique).

Étudiants actuels

Ria Arora

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

semihcanturk00@gmail.com

Collaborateur·rice alumni

Enrique Fita Sanmartin

Collaborateur·rice alumni - UdeM

Doctorat - UdeM

Will Hua

Collaborateur·rice alumni - McGill

Xiaolong Huang

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Paul Janson

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Charles-Etienne Joseph

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

M. Elyes Kanoun

Stagiaire de recherche - UdeM

Vincent Létourneau

Collaborateur·rice alumni - UdeM

Myriam Lizotte

Doctorat - UdeM

Philippe Martin

Doctorat - UdeM

Co-superviseur⋅e :

Paul François

Paria Mehrbod

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Lydia Mezrag

Doctorat - UdeM

Kevin Moon

Visiteur de recherche indépendant

Sacha Morin

Doctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - Concordia

Superviseur⋅e principal⋅e :

geraldin.nanfack@mila.quebec

Shuang Ni

Doctorat - UdeM

stephanie.zandee@mcgill.ca

Albert Orozco Camacho

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Matthew Scicluna

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Pedro Vianna

Collaborateur·rice de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Postdoctorat - UdeM

Collaborateur·rice de recherche - McGill (assistant professor)

Analyser le paradoxe des interférons inhérent à la COVID-19 au moyen de la réduction de la dimensionnalité et du regroupement

Billets de blogue

Graph and representation of working methodology, and graph of data on deaths 60 days after onset of symptoms.

19 février 2025

par

Sacha Morin

Elsa Brunet-Ratnasingham

Guy Wolf

Lire l'article

Publications

Random Forest Autoencoders for Guided Representation Learning

Kevin R. Moon

Jake S. Rhodes

Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization…

2025-02-18

ArXiv (prépublication)

Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation

Pedro Vianna

Muawiz Chaudhary

Paria Mehrbod

An Tang

Guy Cloutier

Michael Eickenberg

Deep neural networks have useful applications in many different tasks, however their performance can be severely affected by changes in the … (voir plus)data distribution. For example, in the biomedical field, their performance can be affected by changes in the data (different machines, populations) between training and test datasets. To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust models to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks. It is implemented by recalculating batch normalization statistics on test batches. Prior work has focused on analysis with test data that has the same label distribution as the training data. However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure. This presents a risk in applying test time adaptation methods in deployment. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. Our selection scheme is based on two principles that we empirically motivate: (1) later layers of networks are more sensitive to label shift (2) individual features can be sensitive to specific classes. We apply the proposed technique to three classification tasks, including CIFAR10-C, Imagenet-C, and diagnosis of fatty liver, where we explore both covariate and label distribution shifts. We find that our method allows to bring the benefits of TTA while significantly reducing the risk of failure common in other methods, while being robust to choice in hyperparameters.

2025-02-17

Proceedings of The 3rd Conference on Lifelong Learning Agents (publié)

Principal Curvatures Estimation with Applications to Single Cell Data

Yanlei Zhang

Lydia Mezrag

Xingzhi Sun

Charles Xu

Kincaid MacDonald

Dhananjay Bhaskar

Bastian Rieck

The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datas… (voir plus)ets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation.

2025-02-06

ArXiv (prépublication)

Principal Curvatures Estimation with Applications to Single Cell Data

Yanlei Zhang

Lydia Mezrag

Xingzhi Sun

Charles Xu

Kincaid MacDonald

Dhananjay Bhaskar

Bastian Rieck

2025-02-06

ArXiv (prépublication)

AdaFisher: Adaptive Second Order Optimization via Fisher Information

Damien MARTINS GOMES

Yanlei Zhang

Mahdi S. Hosseini

First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limi… (voir plus)ted curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD. However, their practicality in training DNNs are still limited due to increased per-iteration computations and suboptimal accuracy compared to the first order methods. We present AdaFisher--an adaptive second-order optimizer that leverages a block-diagonal approximation to the Fisher information matrix for adaptive gradient preconditioning. AdaFisher aims to bridge the gap between enhanced convergence capabilities and computational efficiency in second-order optimization framework for training DNNs. Despite the slow pace of second-order optimizers, we showcase that AdaFisher can be reliably adopted for image classification, language modelling and stand out for its stability and robustness in hyperparameter tuning. We demonstrate that AdaFisher outperforms the SOTA optimizers in terms of both accuracy and convergence speed. Code available from \href{https://github.com/AtlasAnalyticsLab/AdaFisher}{https://github.com/AtlasAnalyticsLab/AdaFisher}

2025-01-22

ICLR.cc/2025/Conference (poster)

openreview.net

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Chen Liu

Guillaume Huguet

Ian Adelstein

Tim G. J. Rudner

2025-01-22

aistats.org/AISTATS/2025/Conference (poster)

openreview.net

Geometry-Aware Generative Autoencoder for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Guillaume Huguet

Ian Adelstein

Tim G. J. Rudner

Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportu… (voir plus)nities for scientific discovery, but it also presents unique computational and statistical challenges. Traditional methods struggle with geometry-aware data generation, interpolation along meaningful trajectories, and transporting populations via feasible paths. To address these issues, we introduce Geometry-Aware Generative Autoencoder (GAGA), a novel framework that combines extensible manifold learning with generative modeling. GAGA constructs a neural network embedding space that respects the intrinsic geometries discovered by manifold learning and learns a novel warped Riemannian metric on the data space. This warped metric is derived from both the points on the data manifold and negative samples off the manifold, allowing it to characterize a meaningful geometry across the entire latent space. Using this metric, GAGA can uniformly sample points on the manifold, generate points along geodesics, and interpolate between populations across the learned manifold. GAGA shows competitive performance in simulated and real-world datasets, including a 30% improvement over SOTA in single-cell population-level trajectory inference.

2025-01-22

aistats.org/AISTATS/2025/Conference (poster)

openreview.net

Non-Uniform Parameter-Wise Model Merging

Albert Manuel Orozco Camacho

Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Tradit… (voir plus)ional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good results in some settings and have gained popularity. However, merging models initialized differently that do not share a part of their training trajectories can yield worse results than simply using the base models, even after aligning their neurons. In this paper, we introduce a novel approach, Non-uniform Parameter-wise Model Merging, or NP Merge, which merges models by learning the contribution of each parameter to the final model using gradient-based optimization. We empirically demonstrate the effectiveness of our method for merging models of various architectures in multiple settings, outperforming past methods. We also extend NP Merge to handle the merging of multiple models, showcasing its scalability and robustness.

2024-12-20

ArXiv (prépublication)

Non-Uniform Parameter-Wise Model Merging

Albert M. Orozco Camacho

2024-12-15

ArXiv (prépublication)

Non-Uniform Parameter-Wise Model Merging

Albert Manuel Orozco Camacho

2024-12-15

2024 IEEE International Conference on Big Data (BigData) (publié)

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

Victor Geadah

Giancarlo Kerg

Guillaume Lajoie

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptatio… (voir plus)n are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We find that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we find that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time.

2024-12-13

PLOS Computational Biology (publié)

Reaction-conditioned De Novo Enzyme Design with GENzyme

Chenqing Hua

Jiarui Lu

Yong Liu

Odin Zhang

Jian Tang

Rex Ying

Wengong Jin

Doina Precup

Shuangjia Zheng

The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interact… (voir plus)ion prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.

2024-11-10

ArXiv (prépublication)