Portrait de Adam Ibrahim

Adam Ibrahim

Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e
Ioannis Mitliagkas
Co-supervisor
Irina Rish
Sujets de recherche
Apprentissage multimodal
Apprentissage profond
Modèles génératifs
Optimisation
Systèmes distribués
Théorie de l'apprentissage automatique
Traitement du langage naturel
Vision par ordinateur
Site web
Google Scholar

Publications

Towards Out-of-Distribution Adversarial Robustness
Adam Ibrahim
Charles Guille-escuret
Ioannis Mitliagkas
Irina Rish
David Scott Krueger
Pouya Bashivan
Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fail… (voir plus)s to transfer to other attacks. While prior work establishes a theoretical trade-off in robustness against different
2023-06-20
ICML.cc/2023/Workshop/AdvML-Frontiers (publié)
doi.org
openreview.net
Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2022-09-30
ArXiv (preprint)
doi.org
openreview.net
Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound
Charles Guille-escuret
Baptiste Goujaud
Adam Ibrahim
Ioannis Mitliagkas
The study of first-order optimization is sensitive to the assumptions made on the objective functions. These assumptions induce complexity c… (voir plus)lasses which play a key role in worst-case analysis, including the fundamental concept of algorithm optimality. Recent work argues that strong convexity and smoothness—popular assumptions in literature—lead to a pathological definition of the condition number. Motivated by this result, we focus on the class of functions satisfying a lower restricted secant inequality and an upper error bound. On top of being robust to the aforementioned pathological behavior and including some non-convex functions, this pair of conditions displays interesting geometrical properties. In particular, the necessary and sufficient conditions to interpolate a set of points and their gradients within the class can be separated into simple conditions on each sampled gradient. This allows the performance estimation problem (PEP) to be solved analytically, leading to a lower bound on the convergence rate that proves gradient descent to be exactly optimal on this class of functions among all first-order algorithms.
openreview.net
Adversarial Feature Desensitization
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Kartik Ahuja
Mojtaba Faramarzi
Touraj Laleh
Blake Richards
Irina Rish
Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can … (voir plus)drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. Our code is available at https://github.com/BashivanLab/afd.
openreview.net
Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim
Waiss Azizian
Gauthier Gidel
Ioannis Mitliagkas
Recent successes of game-theoretic formulations in ML have caused a resurgence of research interest in differentiable games. Overwhelmingly,… (voir plus) that research focuses on methods and upper bounds on their speed of convergence. In this work, we approach the question of fundamental iteration complexity by providing lower bounds to complement the linear (i.e. geometric) upper bounds observed in the literature on a wide class of problems. We cast saddle-point and min-max problems as 2-player games. We leverage tools from single-objective convex optimisation to propose new linear lower bounds for convex-concave games. Notably, we give a linear lower bound for
2020-11-21
Proceedings of the 37th International Conference on Machine Learning (publié)
proceedings.mlr.press