Ioannis Mitliagkas

Biography

Ioannis Mitliagkas (Γιάννης Μητλιάγκας) is an associate professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, as well as a Core Academic member of Mila – Quebec Artificial Intelligence Institute and a Canada CIFAR AI Chair. He holds a part-time position as a staff research scientist at Google DeepMind Montréal.

Previously, he was a postdoctoral scholar in the Departments of statistics and computer science at Stanford University. He obtained his PhD from the Department of Electrical and Computer Engineering at the University of Texas at Austin.

His research includes topics in machine learning, with emphasis on optimization, deep learning theory, statistical learning. His recent work includes methods for efficient and adaptive optimization, studying the interaction between optimization and the dynamics of large-scale learning systems and the dynamics of games.

Current Students

Mehdi Inane Ahmed

PhD - Université de Montréal

Github

Ange-Clément Akazan

Research Intern - Université de Montréal

Oscar Clivio

Research Intern - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Website

Kilian Fatras

Postdoctorate - McGill University

Principal supervisor :

Charles Guille-escuret

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Irina Rish

Website

youry.macius@umontreal.ca

Zichu Liu

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

Youry Macius

Professional Master's - Université de Montréal

PhD - Université de Montréal

Hiroki Naganuma

PhD - Université de Montréal

Master's Research - Université de Montréal

Github

Blog Posts

March 18, 2024

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Read the article

Publications

Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection

Charles Guille-Escuret

Pierre-Andre Noel

David Vazquez

Joao Monteiro

Improving the reliability of deployed machine learning systems often involves developing methods to detect out-of-distribution (OOD) inputs.… (see more) However, existing research often narrowly focuses on samples from classes that are absent from the training set, neglecting other types of plausible distribution shifts. This limitation reduces the applicability of these methods in real-world scenarios, where systems encounter a wide variety of anomalous inputs. In this study, we categorize five distinct types of distribution shifts and critically evaluate the performance of recent OOD detection methods on each of them. We publicly release our benchmark under the name BROAD (Benchmarking Resilience Over Anomaly Diversity). Our findings reveal that while these methods excel in detecting unknown classes, their performance is inconsistent when encountering other types of distribution shifts. In other words, they only reliably detect unexpected inputs that they have been specifically designed to expect. As a first step toward broad OOD detection, we learn a generative model of existing detection scores with a Gaussian mixture. By doing so, we present an ensemble approach that offers a more consistent and comprehensive solution for broad OOD detection, demonstrating superior performance compared to existing methods. Our code to download BROAD and reproduce our experiments is publicly available.

2023-08-22

ArXiv (preprint)

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

Sébastien Lachapelle

Divyat Mahajan

Simon Lacoste-Julien

We tackle the problems of latent variables identification and ``out-of-support'' image generation in representation learning. We show that b… (see more)oth are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of object-specific images. We provide conditions under which exactly solving the reconstruction problem using an additive decoder is guaranteed to identify the blocks of latent variables up to permutation and block-wise invertible transformations. This guarantee relies only on very weak assumptions about the distribution of the latent factors, which might present statistical dependencies and have an almost arbitrarily shaped support. Our result provides a new setting where nonlinear independent component analysis (ICA) is possible and adds to our theoretical understanding of OCRL methods. We also show theoretically that additive decoders can generate novel images by recombining observed factors of variations in novel ways, an ability we refer to as Cartesian-product extrapolation. We show empirically that additivity is crucial for both identifiability and extrapolation on simulated data.

2023-07-05

ArXiv (preprint)

arxiv.org

Towards Out-of-Distribution Adversarial Robustness

Adam Ibrahim

Charles Guille-Escuret

Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fail… (see more)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 (published)

Empirical Study on Optimizer Selection for Out-of-Distribution Generalization

Hiroki Naganuma

Kartik Ahuja

Shiro Takagi

Tetsuya Motokawa

Rio Yokota

Kohta Ishikawa

Ikuro Sato

Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution.… (see more) While much promising work has been accomplished to address this fragility, a systematic study of the role of optimizers and their out-of-distribution generalization performance has not been undertaken. In this study, we examine the performance of popular first-order optimizers for different classes of distributional shift under empirical risk minimization and invariant risk minimization. We address this question for image and text classification using DomainBed, WILDS, and Backgrounds Challenge as testbeds for studying different types of shifts---namely correlation and diversity shift. We search over a wide range of hyperparameters and examine classification accuracy (in-distribution and out-of-distribution) for over 20,000 models. We arrive at the following findings, which we expect to be helpful for practitioners: i) adaptive optimizers (e.g., Adam) perform worse than non-adaptive optimizers (e.g., SGD, momentum SGD) on out-of-distribution performance. In particular, even though there is no significant difference in in-distribution performance, we show a measurable difference in out-of-distribution performance. ii) in-distribution performance and out-of-distribution performance exhibit three types of behavior depending on the dataset---linear returns, increasing returns, and diminishing returns. For example, in the training of natural language data using Adam, fine-tuning the performance of in-distribution performance does not significantly contribute to the out-of-distribution generalization performance.

2023-06-16

TMLR (accepted)

A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods

Tiago Salvador

Kilian FATRAS

Adam M. Oberman

Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled target images leveraging source labeled ones. In the case of an extreme l… (see more)abel shift scenario between the source and target domains, where we have extra source classes not present in the target domain, the UDA problem becomes a harder problem called Partial Domain Adaptation (PDA). While different methods have been developed to solve the PDA problem, most successful algorithms use model selection strategies that rely on target labels to find the best hyper-parameters and/or models along training. These strategies violate the main assumption in PDA: only unlabeled target domain samples are available. In addition, there are also experimental inconsistencies between developed methods - different architectures, hyper-parameter tuning, number of runs - yielding unfair comparisons. The main goal of this work is to provide a realistic evaluation of PDA methods under different model selection strategies and a consistent evaluation protocol. We evaluate 6 state-of-the-art PDA algorithms on 2 different real-world datasets using 7 different model selection strategies. Our two main findings are: (i) without target labels for model selection, the accuracy of the methods decreases up to 30 percentage points; (ii) only one method and model selection pair performs well on both datasets. Experiments were performed with our PyTorch framework, BenchmarkPDA, which we open source.

2023-06-11

TMLR (accepted)

Neural Networks Efficiently Learn Low-Dimensional Representations with SGD

Alireza Mousavi-Hosseini

Sejun Park

Manuela Girotti

Murat A Erdogdu

We study the problem of training a two-layer neural network (NN) of arbitrary width using stochastic gradient descent (SGD) where the input …

2023-02-01

ICLR.cc/2023/Conference (notable)

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

Samuel Sokota

Ryan D'Orazio

J Zico Kolter

Nicolas Loizou

Marc Lanctot

Noam Brown

Christian Kroer

2023-02-01

ICLR.cc/2023/Conference (poster)

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

Samuel Sokota

Ryan D'Orazio

J Zico Kolter

Nicolas Loizou

Marc Lanctot

Noam Brown

Christian Kroer

This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gra… (see more)dient algorithm. Our contribution is demonstrating the virtues of magnetic mirror descent as both an equilibrium solver and as an approach to reinforcement learning in two-player zero-sum games. These virtues include: 1) Being the first quantal response equilibria solver to achieve linear convergence for extensive-form games with first order feedback; 2) Being the first standard reinforcement learning algorithm to achieve empirically competitive results with CFR in tabular settings; 3) Achieving favorable performance in 3x3 Dark Hex and Phantom Tic-Tac-Toe as a self-play deep reinforcement learning algorithm.

2023-02-01

ICLR.cc/2023/Conference (poster)

LEAD: Min-Max Optimization from a Physical Perspective

Reyhane Askari Hemmat

Amartya Mitra

Guillaume Lajoie

Adversarial formulations have rekindled interest in two-player min-max games. A central obstacle in the optimization of such games is the ro… (see more)tational dynamics that hinder their convergence. In this paper, we show that game optimization shares dynamic properties with particle systems subject to multiple forces, and one can leverage tools from physics to improve optimization dynamics. Inspired by the physical framework, we propose LEAD, an optimizer for min-max games. Next, using Lyapunov stability theory from dynamical systems as well as spectral analysis, we study LEAD’s convergence properties in continuous and discrete time settings for a class of quadratic min-max games to demonstrate linear convergence to the Nash equilibrium. Finally, we empirically evaluate our method on synthetic setups and CIFAR-10 image generation to demonstrate improvements in GAN training.

2023-01-01

Trans. Mach. Learn. Res. (published)

Performative Prediction with Neural Networks

Mehrnaz Mofakhami

Gauthier Gidel

2023-01-01

AISTATS (published)

Performative Prediction with Neural Networks

Mehrnaz Mofakhami

Gauthier Gidel

Performative prediction is a framework for learning models that influence the data they intend to predict. We focus on finding classifiers t… (see more)hat are performatively stable, i.e. optimal for the data distribution they induce. Standard convergence results for finding a performatively stable classifier with the method of repeated risk minimization assume that the data distribution is Lipschitz continuous to the model's parameters. Under this assumption, the loss must be strongly convex and smooth in these parameters; otherwise, the method will diverge for some problems. In this work, we instead assume that the data distribution is Lipschitz continuous with respect to the model's predictions, a more natural assumption for performative systems. As a result, we are able to significantly relax the assumptions on the loss function. In particular, we do not need to assume convexity with respect to the model's parameters. As an illustration, we introduce a resampling procedure that models realistic distribution shifts and show that it satisfies our assumptions. We support our theory by showing that one can learn performatively stable classifiers with neural networks making predictions about real data that shift according to our proposed procedure.

2023-01-01

AISTATS (published)

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

Sébastien Lachapelle

Tristan Deleu

Divyat Mahajan

Yoshua Bengio

Simon Lacoste-Julien

Quentin Bertrand

Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding … (see more)is limited. In this work, we provide evidence that disentangled representations coupled with sparse task-specific predictors improve generalization. In the context of multi-task learning, we prove a new identifiability result that provides conditions under which maximally sparse predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem. Finally, we explore a meta-learning version of this algorithm based on group Lasso multiclass SVM predictors, for which we derive a tractable dual formulation. It obtains competitive results on standard few-shot classification benchmarks, while each task is using only a fraction of the learned representations.

2023-01-01

ICML (published)

proceedings.mlr.press