Portrait of Samira Ebrahimi Kahou

Samira Ebrahimi Kahou

Affiliate Member
Canada CIFAR AI Chair
Assistant Professor, University of Calgary, Deparment of Electrical and Software Engineering
Adjunct Professor, École de technologie suprérieure, School of Computer Science
Adjunct Professor, McGill University, School of Computer Science
Website
Google Scholar
X

Biography

I am an Assistant Professor at the Schulich School of Engineering's Department of Electrical and Software Engineering at the University of Calgary. I am also an adjunct professor at the Department of Computer Engineering and Information Technology of ÉTS and an adjunct professor at the Computer School of McGill. Before joining ÉTS, I was a postdoctoral fellow working with Professor Doina Precup at McGill/Mila. Before my postdoc, I was a researcher at Microsoft Research Montréal.

I received my Ph.D. from Polytechnique Montréal/Mila in 2016 under the supervision of Professor Chris Pal. During my Ph.D. studies, I worked on computer vision and deep learning applied to emotion recognition, object tracking and knowledge distillation.

Current Students

Aamer Abdul Rahman
Master's Research - École de technologie suprérieure
aamer.abdul-rahman@mila.quebec
Website
Github
Google Scholar
Aayush Bajaj
Professional Master's - Université de Montréal
Principal supervisor :
Yoshua Bengio
aayush.bajaj@mila.quebec
Charles Bricout
Master's Research - École de technologie suprérieure
charles.bricout@mila.quebec
Ivan Anokhin
PhD - Université de Montréal
Principal supervisor :
Irina Rish
ivan.anokhin@mila.quebec
Github
Google Scholar
Jithendaraa Subramanian
Master's Research - McGill University
Principal supervisor :
Derek Nowrouzezahrai
jithendaraa.subramanian@mila.quebec
Website
Github
Google Scholar
Meghana Bhange
Master's Research - École de technologie suprérieure
Principal supervisor :
Ulrich Aivodji
meghana.bhange@mila.quebec
Website
Github
Google Scholar
Patrik Kenfack
PhD - École de technologie suprérieure
Principal supervisor :
Ulrich Aivodji
patrik.kenfack@mila.quebec
Github
Google Scholar
Pranav Agarwal
PhD - École de technologie suprérieure
pranav.agarwal@mila.quebec
Website
Github
Google Scholar
Rambod Azimi
Research Intern - McGill University
rambod.azimi@mila.quebec
Website
Github
Rishav Rishav
Master's Research - École de technologie suprérieure
rishav.rishav@mila.quebec
Google Scholar
Shivakanth Sujit
Master's Research - École de technologie suprérieure
shivakanth.sujit@mila.quebec
Website
Github
Google Scholar
Somjit Nath
PhD - McGill University
Co-supervisor :
Derek Nowrouzezahrai
somjit.nath@mila.quebec
Website
Google Scholar
Zihan Wang
Master's Research - McGill University
Principal supervisor :
Narges Armanfard
zihan.wang@mila.quebec
Website

Publications

Auxiliary Losses for Learning Generalizable Concept-based Models
Ivaxi Sheth
Samira Ebrahimi Kahou
openreview.net
Learning from uncertain concepts via test time interventions
Ivaxi Sheth
Aamer Abdul Rahman
Laya Rafiee Sevyeri
Mohammad Havaei
Samira Ebrahimi Kahou
With neural networks applied to safety-critical applications, it has become increasingly important to understand the defining features of de… (see more)cision-making. Therefore, the need to uncover the black boxes to rational representational space of these neural networks is apparent. Concept bottleneck model (CBM) encourages interpretability by predicting human-understandable concepts. They predict concepts from input images and then labels from concepts. Test time intervention, a salient feature of CBM, allows for human-model interactions. However, these interactions are prone to information leakage and can often be ineffective inappropriate communication with humans. We propose a novel uncertainty based strategy, \emph{SIUL: Single Interventional Uncertainty Learning} to select the interventions. Additionally, we empirically test the robustness of CBM and the effect of SIUL interventions under adversarial attack and distributional shift. Using SIUL, we observe that the interventions suggested lead to meaningful corrections along with mitigation of concept leakage. Extensive experiments on three vision datasets along with a histopathology dataset validate the effectiveness of our interventional learning.
2022-11-20
NeurIPS.cc/2022/Workshop/TSRML (accepted)
openreview.net
openreview.net
Learning Latent Structural Causal Models
Jithendaraa Subramanian
Yashas Annadani
Ivaxi Sheth
Nan Rosemary Ke
Tristan Deleu
Stefan Bauer
Derek Nowrouzezahrai
Samira Ebrahimi Kahou
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.
2022-10-24
ArXiv (preprint)
doi.org
openreview.net
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Moslem Yazdanpanah
Aamer Abdul Rahman
Muawiz Chaudhary
Christian Desrosiers
Mohammad Havaei
Eugene Belilovsky
Samira Ebrahimi Kahou
Batch normalization is a staple of computer vision models, including those employed in few-shot learning. Batch nor-malization layers in con… (see more)volutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine parameters
2022-06-18
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)
doi.org
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier
Pierre Delaunay
Mirko Bronzi
Assya Trofimov
Brennan Nichyporuk
Justin Szeto
Naz Sepah
Edward Raff
Kanika Madan
Vikram Voleti
Samira Ebrahimi Kahou
Vincent Michalski
Dmitriy Serdyuk
Tal Arbel
Chris Pal
Gael Varoquaux
Pascal Vincent
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.
2021-01-01
MLSys (published)
arxiv.org
arxiv.org
Towards Deep Conversational Recommendations
Raymond Li
Samira Ebrahimi Kahou
Hannes Schulz
Vincent Michalski
Laurent Charlin
Chris Pal
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendat… (see more)ion is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.
arxiv.org
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Guillaume Alain
Amjad Almahairi
Christof Angermüller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
Yoshua Bengio
Arnaud Bergeron
J. Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson
Olivier Breuleux … (see 92 more)
pierre luc carrier
Kyunghyun Cho
Jan Chorowski
Paul F. Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
M'elanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
Dumitru Erhan
Ziye Fan
Orhan Firat
Mathieu Germain
Xavier Glorot
Ian J. Goodfellow
Matthew Graham
Caglar Gulcehre
Philippe Hamel
Iban Harlouchet
Jean-philippe Heng
Balázs Hidasi
Sina Honari
Arjun Jain
S'ebastien Jean
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric P. Larsen
César Laurent
S. Lee
Simon-mark Lefrancois
Simon Lemieux
Nicholas Léonard
Zhouhan Lin
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
R. McGibbon
Roland Memisevic
Bart van Merriënboer
Vincent Michalski
Mehdi Mirza
Alberto Orlandi
Chris Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Adriana Romero Soriano
Markus Dr. Roth
Peter Sadowski
John Salvatier
Francois Savard
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jérémie Tanguay
Gijs van Tulder
Joseph P. Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
Harm de Vries
David Warde-Farley
Dustin J. Webb
M. Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
2016-05-09
ArXiv (preprint)
arxiv.org
arxiv.org
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Guillaume Alain
Amjad Almahairi
Christof Angermüller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
Yoshua Bengio
Arnaud Bergeron
J. Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson
Olivier Breuleux … (see 92 more)
pierre luc carrier
Kyunghyun Cho
Jan Chorowski
Paul F. Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
M'elanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
Dumitru Erhan
Ziye Fan
Orhan Firat
Mathieu Germain
Xavier Glorot
Ian G Goodfellow
Matthew Graham
Caglar Gulcehre
Philippe Hamel
Iban Harlouchet
Jean-philippe Heng
Balázs Hidasi
Sina Honari
Arjun Jain
S'ebastien Jean
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric Larsen
César Laurent
S. Lee
Simon-mark Lefrancois
Simon Lemieux
Nicholas Léonard
Zhouhan Lin
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
R. McGibbon
Roland Memisevic
Bart van Merriënboer
Vincent Michalski
Mehdi Mirza
Alberto Orlandi
Chris Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Adriana Romero Soriano
Markus Dr. Roth
Peter Sadowski
John Salvatier
Francois Savard
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jérémie Tanguay
Gijs van Tulder
Joseph P. Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
Harm de Vries
David Warde-Farley
Dustin J. Webb
M. Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
2016-05-09
ArXiv (preprint)
arxiv.org
arxiv.org