Ioannis Mitliagkas

Biographie

Ioannis Mitliagkas (Γιάννης Μητλιάγκας) est professeur associé au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Il est également membre académique principal à Mila – Institut québécois d’intelligence artificielle et titulaire d’une chaire en IA Canada-CIFAR. Il occupe présentement un poste de chercheur scientifique à temps partiel chez Google DeepMind à Montréal.

Auparavant, il était chercheur postdoctoral aux Départements de statistique et d'informatique de l'Université de Stanford. Il a obtenu un doctorat au Département d'ingénierie électrique et informatique de l'Université du Texas à Austin.

Ses recherches portent sur des sujets liés à l'apprentissage automatique, en particulier l'optimisation, la théorie de l'apprentissage profond et l'apprentissage statistique. Ses travaux récents portent notamment sur les méthodes d'optimisation efficace et adaptative, l'étude de l'interaction entre l'optimisation et la dynamique des systèmes d'apprentissage à grande échelle et la dynamique des jeux.

Étudiants actuels

Mehdi Inane Ahmed

Doctorat - UdeM

Github

Hiba Cheikh

UdeM

Doctorat - UdeM

Collaborateur·rice de recherche

Github

Adam Ibrahim

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Irina Rish

Site web

Décodeurs additifs pour l’identification des variables latentes et l’extrapolation du produit cartésien

Zichu Liu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

Youry Macius

Maîtrise professionnelle - UdeM

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Maîtrise recherche - UdeM

Billets de blogue

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

18 mars 2024

par

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Lire l'article

Publications

Learning to Defer for Causal Discovery with Imperfect Experts

Sara Magliacane

Valentina Zantedeschi

Alexandre Drouin

Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not… (voir plus) guaranteed to be correct. Expert recommendations may contradict data-driven results, and their reliability can vary significantly depending on the domain or specific query. Existing methods based on soft constraints or inconsistencies in predicted causal relationships fail to account for these variations in expertise. To remedy this, we propose L2D-CD, a method for gauging the correctness of expert recommendations and optimally combining them with data-driven causal discovery results. By adapting learning-to-defer (L2D) algorithms for pairwise causal discovery (CD), we learn a deferral function that selects whether to rely on classical causal discovery methods using numerical data or expert recommendations based on textual meta-data. We evaluate L2D-CD on the canonical Tübingen pairs dataset and demonstrate its superior performance compared to both the causal discovery method and the expert used in isolation. Moreover, our approach identifies domains where the expert's performance is strong or weak. Finally, we outline a strategy for generalizing this approach to causal discovery on graphs with more than two variables, paving the way for further research in this area.

2025-03-04

ICLR.cc/2025/Workshop/LLM_Reason_and_Plan (publié)

Solving Hidden Monotone Variational Inequalities with Surrogate Losses

Junhyung Lyle Kim

Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minim… (voir plus)izing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) problem. This difference in setting has caused many practical challenges as naive gradient-based approaches from supervised learning tend to diverge and cycle in the VI case. In this work, we propose a principled surrogate-based approach compatible with deep learning to solve VIs. We show that our surrogate-based approach has three main benefits: (1) under assumptions that are realistic in practice (when hidden monotone structure is present, interpolation, and sufficient optimization of the surrogates), it guarantees convergence, (2) it provides a unifying perspective of existing methods, and (3) is amenable to existing deep learning optimizers like ADAM. Experimentally, we demonstrate our surrogate-based approach is effective in min-max optimization and minimizing projected Bellman error. Furthermore, in the deep reinforcement learning case, we propose a novel variant of TD(0) which is more compute and sample efficient.

2025-01-21

ICLR.cc/2025/Conference (poster)

An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration

Hiroki Naganuma

Ryuichiro Hataya

Kotaro Yoshida

2024-12-31

Trans. Mach. Learn. Res. (publié)

Alexia Jolicoeur-Martineau

Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching

Ange-Cl'ement Akazan

2024-10-19

ArXiv (prépublication)

arxiv.org

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

Charles Guille-escuret

Pierre-Andre Noel

David Vázquez

Joao Monteiro

Improving the reliability of deployed machine learning systems often involves developing methods to detect out-of-distribution (OOD) inputs.… (voir plus) 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.

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths

Charles Guille-escuret

Hiroki Naganuma

Kilian Fatras

Understanding the optimization dynamics of neural networks is necessary for closing the gap between theory and practice. Stochastic first-or… (voir plus)der optimization algorithms are known to efficiently locate favorable minima in deep neural networks. This efficiency, however, contrasts with the non-convex and seemingly complex structure of neural loss landscapes. In this study, we delve into the fundamental geometric properties of sampled gradients along optimization paths. We focus on two key quantities, which appear in the restricted secant inequality and error bound. Both hold high significance for first-order optimization. Our analysis reveals that these quantities exhibit predictable, consistent behavior throughout training, despite the stochasticity induced by sampling minibatches. Our findings suggest that not only do optimization trajectories never encounter significant obstacles, but they also maintain stable dynamics during the majority of training. These observed properties are sufficiently expressive to theoretically guarantee linear convergence and prescribe learning rate schedules mirroring empirical practices. We conduct our experiments on image classification, semantic segmentation and language modeling across different batch sizes, network architectures, datasets, optimizers, and initialization seeds. We discuss the impact of each factor. Our work provides novel insights into the properties of neural network loss functions, and opens the door to theoretical frameworks more relevant to prevalent practice.

2024-07-07

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

proceedings.mlr.press

Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones

Mehrnaz Mofakhami

Reza Bayat

Joao Monteiro

Valentina Zantedeschi

2024-06-19

ICML.cc/2024/Workshop/ES-FoMo-II (poster)

Gradient descent induces alignment between weights and the pre-activation tangents for deep non-linear networks

Daniel Beaglehole

Atish Agarwala

Understanding the mechanisms through which neural networks extract statistics from input-label pairs is one of the most important unsolved p… (voir plus)roblems in supervised learning. Prior works have identified that the gram matrices of the weights in trained neural networks of general architectures are proportional to the average gradient outer product of the model, in a statement known as the Neural Feature Ansatz (NFA). However, the reason these quantities become correlated during training is poorly understood. In this work, we clarify the nature of this correlation and explain its emergence at early training times. We identify that the NFA is equivalent to alignment between the left singular structure of the weight matrices and the newly defined pre-activation tangent kernel. We identify a centering of the NFA that isolates this alignment and is robust to initialization scale. We show that, through this centering, the speed of NFA development can be predicted analytically in terms of simple statistics of the inputs and labels.

2024-06-15

ICML.cc/2024/Workshop/HiLD (poster)

Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition

Eleni Triantafillou

Peter Kairouz

Fabian Pedregosa

Jamie Hayes

Meghdad Kurmanji

Kairan Zhao

Vincent Dumoulin

Julio C. S. Jacques Junior

Gintare Karolina Dziugaite

Jun Wan

Lisheng Sun-Hosoya

Sergio Escalera

Peter Triantafillou

Isabelle Guyon

We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and in… (voir plus)itiate discussions on formal and robust evaluation methodologies. The competition was highly successful: nearly 1,200 teams from across the world participated, and a wealth of novel, imaginative solutions with different characteristics were contributed. In this paper, we analyze top solutions and delve into discussions on benchmarking unlearning, which itself is a research problem. The evaluation methodology we developed for the competition measures forgetting quality according to a formal notion of unlearning, while incorporating model utility for a holistic evaluation. We analyze the effectiveness of different instantiations of this evaluation framework vis-a-vis the associated compute cost, and discuss implications for standardizing evaluation. We find that the ranking of leading methods remains stable under several variations of this framework, pointing to avenues for reducing the cost of evaluation. Overall, our findings indicate progress in unlearning, with top-performing competition entries surpassing existing algorithms under our evaluation framework. We analyze trade-offs made by different algorithms and strengths or weaknesses in terms of generalizability to new datasets, paving the way for advancing both benchmarking and algorithm development in this important area.

2024-06-12

ArXiv (prépublication)

arxiv.org

Smoothness-Adaptive Sharpness-Aware Minimization for Finding Flatter Minima

Hiroki Naganuma

Junhyung Lyle Kim

Anastasios Kyrillidis

The sharpness-aware minimization (SAM) procedure recently gained increasing attention due to its favorable generalization ability to unseen … (voir plus)data. SAM aims to find flatter (local) minima, utilizing a minimax objective. An immediate challenge in the application of SAM is the adjustment of two pivotal step sizes, which significantly influence its effectiveness. We introduce a novel, straightforward approach for adjusting step sizes that adapts to the smoothness of the objective function, thereby reducing the necessity for manual tuning. This method, termed Smoothness-Adaptive SAM (SA-SAM), not only simplifies the optimization process but also promotes the method's inherent tendency to converge towards flatter minima, enhancing performance in specific models.

2024-03-04

ICLR.cc/2024/Workshop/PML4LRS (poster)

Feature learning as alignment: a structural property of gradient descent in non-linear neural networks

Daniel Beaglehole

Atish Agarwala

Understanding the mechanisms through which neural networks extract statistics from input-label pairs through feature learning is one of the … (voir plus)most important unsolved problems in supervised learning. Prior works demonstrated that the gram matrices of the weights (the neural feature matrices, NFM) and the average gradient outer products (AGOP) become correlated during training, in a statement known as the neural feature ansatz (NFA). Through the NFA, the authors introduce mapping with the AGOP as a general mechanism for neural feature learning. However, these works do not provide a theoretical explanation for this correlation or its origins. In this work, we further clarify the nature of this correlation, and explain its emergence. We show that this correlation is equivalent to alignment between the left singular structure of the weight matrices and the newly defined pre-activation tangent features at each layer. We further establish that the alignment is driven by the interaction of weight changes induced by SGD with the pre-activation features, and analyze the resulting dynamics analytically at early times in terms of simple statistics of the inputs and labels. We prove the derivative alignment occurs with high probability in specific high dimensional settings. Finally, motivated by the observation that the NFA is driven by this centered correlation, we introduce a simple optimization rule that dramatically increases the NFA correlations at any given layer and improves the quality of features learned.

2024-02-06

ArXiv (prépublication)

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation

Divyat Mahajan

Brady Neal

Vasilis Syrgkanis

2024-01-15

ICLR.cc/2024/Conference (spotlight)