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

Hiba Cheikh

Université de Montréal

Ryan D'Orazio

PhD - Université de Montréal

Website

Mahdi Ghaznavi

Collaborating researcher

Github

Adam Ibrahim

Collaborating Alumni - Université de Montréal

Co-supervisor :

Irina Rish

Website

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

Zichu Liu

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

Youry Macius

Professional Master's - Université de Montréal

PhD - Université de Montréal

Yukti Makhija

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Master's Research - Université de Montréal

Blog Posts

March 18, 2024

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Read the article

Publications

Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation

Divyat Mahajan

Brady Neal

Vasilis Syrgkanis

We study the problem of model selection in causal inference, specifically for the case of conditional average treatment effect (CATE) estima… (see more)tion under binary treatments. Unlike model selection in machine learning, there is no perfect analogue of cross-validation as we do not observe the counterfactual potential outcome for any data point. Towards this, there have been a variety of proxy metrics proposed in the literature, that depend on auxiliary nuisance models estimated from the observed data (propensity score model, outcome regression model). However, the effectiveness of these metrics has only been studied on synthetic datasets as we can access the counterfactual data for them. We conduct an extensive empirical analysis to judge the performance of these metrics introduced in the literature, and novel ones introduced in this work, where we utilize the latest advances in generative modeling to incorporate multiple realistic datasets. Our analysis suggests novel model selection strategies based on careful hyperparameter tuning of CATE estimators and causal ensembling.

2023-12-31

ICLR (published)

arxiv.org

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

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.

2023-12-11

Neural Information Processing Systems (Accept (oral))

Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize

Ryan D'Orazio

Nicolas Loizou

Issam Hadj Laradji

We investigate the convergence of stochastic mirror descent (SMD) under interpolation in relatively smooth and smooth convex optimization. I… (see more)n relatively smooth convex optimization we provide new convergence guarantees for SMD with a constant stepsize. For smooth convex optimization we propose a new adaptive stepsize scheme --- the mirror stochastic Polyak stepsize (mSPS). Notably, our convergence results in both settings do not make bounded gradient assumptions or bounded variance assumptions, and we show convergence to a neighborhood that vanishes under interpolation. Consequently, these results correspond to the first convergence guarantees under interpolation for the exponentiated gradient algorithm for fixed or adaptive stepsizes. mSPS generalizes the recently proposed stochastic Polyak stepsize (SPS) (Loizou et al. 2021) to mirror descent and remains both practical and efficient for modern machine learning applications while inheriting the benefits of mirror descent. We complement our results with experiments across various supervised learning tasks and different instances of SMD, demonstrating the effectiveness of mSPS.

2023-11-07

TMLR (accepted)

CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning

Charles Guille-escuret

Pau Rodríguez

David Vázquez

Joao Monteiro

Handling out-of-distribution (OOD) samples has become a major stake in the real-world deployment of machine learning systems. This work expl… (see more)ores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples: unseen classes and adversarial perturbations. First, we pair self-supervised contrastive learning with the maximum mean discrepancy (MMD) two-sample test. This approach enables us to robustly test whether two independent sets of samples originate from the same distribution, and we demonstrate its effectiveness by discriminating between CIFAR-10 and CIFAR-10.1 with higher confidence than previous work. Motivated by this success, we introduce CADet (Contrastive Anomaly Detection), a novel method for OOD detection of single samples. CADet draws inspiration from MMD, but leverages the similarity between contrastive transformations of a same sample. CADet outperforms existing adversarial detection methods in identifying adversarially perturbed samples on ImageNet and achieves comparable performance to unseen label detection methods on two challenging benchmarks: ImageNet-O and iNaturalist. Significantly, CADet is fully self-supervised and requires neither labels for in-distribution samples nor access to OOD examples.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

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

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 base-predictors improve generalization. In the context of multi-task learning, we prove a new identifiability result that provides conditions under which maximally sparse base-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 base-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.

2023-07-24

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

Towards Out-of-Distribution Adversarial Robustness

Adam Ibrahim

Charles Guille-escuret

Irina Rish

David 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… (see more)s to transfer to other attacks. While prior work establishes a theoretical trade-off in robustness against different

2023-06-19

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.

2023-06-15

TMLR (accepted)

A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods

Tiago Salvador

Kilian Fatras

Adam 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

Transactions on Machine Learning Research (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 …

2023-01-31

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

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.

2023-01-31

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.

2022-12-31

Trans. Mach. Learn. Res. (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.

2022-12-31

AISTATS (published)