Samira Ebrahimi Kahou

Biography

Samira is an Associate Professor at the University of Calgary in the Schulich School of Engineering. She is also an Adjunct Professor at École de technologie supérieure (ÉTS) in the Department of Software Engineering and Information Technology and McGill University in the School of Computer Science. She is an academic member of Mila - Québec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair. Samira received her Ph.D. in Computer Engineering from Polytechnique Montréal/Mila with an award for the best thesis in the department. Samira also worked as a Postdoctoral Fellow at the McGill School of Computer Science and as a Researcher at Microsoft Research Montréal.

Samira and her group work on solving fundamental problems in representation learning for decision making, with a broad focus on explainability, generalization and efficient learning. Her work has been published in top-tier venues, such as NeurIPS, ICLR, ICML, ICCV, CVPR, TMLR and CoRL. Samira received the 2024 Early Career Excellence in Research Award from the Schulich School of Engineering. Her impactful work in multi-modal learning was recognized twice by ACM ICMI Ten-Year Technical Impact Awards, 2023 as Runner-up and 2025 as Winner.

Current Students

Ivan Anokhin

PhD - Université de Montréal

Principal supervisor :

Irina Rish

ivan.anokhin@mila.quebec

Github

meghana.bhange@mila.quebec

Meghana Bhange

PhD - École de technologie suprérieure

Principal supervisor :

Ulrich Aivodji

PhD - École de technologie suprérieure

Principal supervisor :

Ulrich Aivodji

patrik.kenfack@mila.quebec

Website

Github

PhD - McGill University

Co-supervisor :

Derek Nowrouzezahrai

somjit.nath@mila.quebec

Website

Priyesh Vijayan

PhD - McGill University

Principal supervisor :

Publications

Handling Delay in Real-Time Reinforcement Learning

Ivan Anokin

Rishav Rishav

Matthew Riemer

Stephen Chung

Irina Rish

Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second… (see more) due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of

2025-01-21

International Conference on Learning Representations (poster)

Learning to Play Atari in a World of Tokens

Pranav Agarwal

Sheldon Andrews

Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extende… (see more)d context, resulting in more accurate world models. However, for complex reasoning and planning tasks, these methods primarily rely on continuous representations. This complicates modeling of discrete properties of the real world such as disjoint object classes between which interpolation is not plausible. In this work, we introduce discrete abstract representations for transformer-based learning (DART), a sample-efficient method utilizing discrete representations for modeling both the world and learning behavior. We incorporate a transformer-decoder for auto-regressive world modeling and a transformer-encoder for learning behavior by attending to task-relevant cues in the discrete representation of the world model. For handling partial observability, we aggregate information from past time steps as memory tokens. DART outperforms previous state-of-the-art methods that do not use look-ahead search on the Atari 100k sample efficiency benchmark with a median human-normalized score of 0.790 and beats humans in 9 out of 26 games. We release our code at https://pranaval.github.io/DART/.

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization

Jordi Armengol-Estapé

Vincent Michalski

Ramnath Kumar

Pierre-Luc St-Charles

Doina Precup

Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross… (see more)-modal learning can improve representations for few-shot classification. More specifically, language is a rich modality that can be used to guide visual learning. In this work, we experiment with a multi-modal architecture for few-shot learning that consists of three components: a classifier, an auxiliary network, and a bridge network. While the classifier performs the main classification task, the auxiliary network learns to predict language representations from the same input, and the bridge network transforms high-level features of the auxiliary network into modulation parameters for layers of the few-shot classifier using conditional batch normalization. The bridge should encourage a form of lightweight semantic alignment between language and vision which could be useful for the classifier. However, after evaluating the proposed approach on two popular few-shot classification benchmarks we find that a) the improvements do not reproduce across benchmarks, and b) when they do, the improvements are due to the additional compute and parameters introduced by the bridge network. We contribute insights and recommendations for future work in multi-modal meta-learning, especially when using language representations.

2024-05-28

arXiv (preprint)

Prioritizing Samples in Reinforcement Learning with Reducible Loss

Shivakanth Sujit

Pedro H.M. Braga

Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed i… (see more)n the past. Not all samples carry the same amount of significance and simply assigning equal importance to each of the samples is a naïve strategy. In this paper, we propose a method to prioritize samples based on how much we can learn from a sample. We define the learn-ability of a sample as the steady decrease of the training loss associated with this sample over time. We develop an algorithm to prioritize samples with high learn-ability, while assigning lower priority to those that are hard-to-learn, typically caused by noise or stochasticity. We empirically show that our method is more robust than random sampling and also better than just prioritizing with respect to the training loss, i.e. the temporal difference loss, which is used in prioritized experience replay.

2023-12-11

Neural Information Processing Systems (Accept (poster))

Spectral Temporal Contrastive Learning

Sacha Morin

Guy Wolf

Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, pa… (see more)rticularly contrastive learning (CL), have proven successful by leveraging data augmentations to define positive pairs. This success has prompted a number of theoretical studies to better understand CL and investigate theoretical bounds for downstream linear probing tasks. This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts. In this paper, we adapt recent work on Spectral CL to formulate Spectral Temporal Contrastive Learning (STCL). We discuss a population loss based on a state graph derived from a time-homogeneous reversible Markov chain with uniform stationary distribution. The STCL loss enables to connect the linear probing performance to the spectral properties of the graph, and can be estimated by considering previously observed data sequences as an ensemble of MCMC chains.

2023-11-30

arXiv (preprint)

Empowering Clinicians with MeDT: A Framework for Sepsis Treatment

Aamer Abdul Rahman

Pranav Agarwal

Vincent Michalski

Rita Noumeir

Philippe Jouvet

2023-11-01

GCRL @ Neural Information Processing Systems (published)

Discovering Object-Centric Generalized Value Functions From Pixels

Gopeshh Raaj Subbaraj

Khimya Khetarpal

Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using ha… (see more)nd-crafted auxiliary tasks and pseudo rewards. Automatically learning such representations in an object-centric manner geared towards control and fast adaptation remains an open research problem. In this paper, we introduce a method that tries to discover meaningful features from objects, translating them to temporally coherent "question" functions and leveraging the subsequent learned general value functions for control. We compare our approach with state-of-the-art techniques alongside other ablations and show competitive performance in both stationary and non-stationary settings. Finally, we also investigate the discovered general value functions and through qualitative analysis show that the learned representations are not only interpretable but also, centered around objects that are invariant to changes across tasks facilitating fast adaptation.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (published)

proceedings.mlr.press

Learning Latent Structural Causal Models

Jithendaraa Subramanian

Yashas Annadani

Ivaxi Sheth

Nan Rosemary Ke

Tristan Deleu

Stefan Bauer

Derek Nowrouzezahrai

Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better e… (see more)xplanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, \textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model.

2022-10-23

arXiv (preprint)

From legacy to microservices: A type-based approach for microservices identification using machine learning and semantic analysis.

Imen Trabelsi

Manel Abdellatif

Abdalgader Abubaker

Naouel Moha

Sébastien Mosser

Yann-Gaël Guéhéneuc

The microservices architecture (MSA) style has been gaining interest in recent years because of its high scalability, ability to be deployed… (see more) in the cloud, and suitability for DevOps practices. While new applications can adopt MSA from their inception, many legacy monolithic systems must be migrated to an MSA to benefit from the advantages of this architectural style. To support the migration process, we propose MicroMiner, a microservices identification approach that is based on static‐relationship analyses between code elements as well as semantic analyses of the source code. Our approach relies on machine learning (ML) techniques and uses service types to guide the identification of microservices from legacy monolithic systems. We evaluate the efficiency of our approach on four systems and compare our results to ground‐truths and to those of two state‐of‐the‐art approaches. We perform a qualitative evaluation of the resulted microservices by analyzing the business capabilities of the identified microservices. Also a quantitative analysis using the state‐of‐the‐art metrics on independence of functionality and modularity of services was conducted. Our results show the effectiveness of our approach to automate one of the most time‐consuming steps in the migration of legacy systems to microservices. The proposed approach identifies architecturally significant microservices with a 68.15% precision and 77% recall.

2022-09-03

Journal of Software Evolution and Process (unknown)

Towards Policy-Guided Conversational Recommendation with Dialogue Acts

Paul Crook

Y-Lan Boureau

J. Weston

Akbar Karimi

Leonardo Rossi

Andrea Prati

Wenqiang Lei

Xiangnan He

Qingyun Yisong Miao

Richang Wu

Min-Yen Hong

Kan Tat-Seng

Raymond Li

Hannes Schulz

Zujie Liang

Huang Hu

Can Xu

Jian Miao

Lizi Liao … (see 47 more)

Ryuichi Takanobu

Yunshan Ma

Xun Yang

Wenchang Ma

Minlie Huang

Minghao Tu

Iulian Serban

Aaron C. Courville

David Silver

Julian Schrittwieser

K. Simonyan

Ioannis Antonoglou

Aja Huang

A. Guez

Hanlin Zhu

O. Vinyals

Igor Babuschkin

Junyoung Chung

M. Mathieu

Max Jaderberg

Wojciech M. Czar-725 necki

A. Dudzik

Petko Georgiev

Richard Powell

T. Ewalds

Dan Horgan

M. Kroiss

Ivo Danihelka

J. Agapiou

Junhyuk Oh

Valentin Dalibard

David Choi

L. Sifre

Yury Sulsky

Sasha Vezhnevets

James Molloy

Trevor Cai

D. Budden

T. Paine

Caglar Gulçehre

Ziyu Wang

Tobias Pfaff

Tobias Pohlen

2021-12-31

(published)

www.semanticscholar.org

Simple Video Generation using Neural ODEs

David Kanaa

Vikram Voleti

Christopher Pal

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely chall… (see more)enging. It is a common belief that a key step towards solving this task resides in modelling accurately both spatial and temporal information in video signals. A promising direction to do so has been to learn latent variable models that predict the future in latent space and project back to pixels, as suggested in recent literature. Following this line of work and building on top of a family of models introduced in prior work, Neural ODE, we investigate an approach that models time-continuous dynamics over a continuous latent space with a differential equation with respect to time. The intuition behind this approach is that these trajectories in latent space could then be extrapolated to generate video frames beyond the time steps for which the model is trained. We show that our approach yields promising results in the task of future frame prediction on the Moving MNIST dataset with 1 and 2 digits.

2021-09-06

arXiv (preprint)

Accounting for Variance in Machine Learning Benchmarks

Mirko Bronzi

Naz Sepah

Edward Raff

Kanika Madan

Vikram Voleti

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (see more)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.

2020-12-31

MLSys (published)