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

reyhane.askari.hemmat@mila.quebec

Ange-Clément Akazan

Research Intern - Université de Montréal

Reyhane Askari Hemmat

PhD - Université de Montréal

Co-supervisor :

Nicolas Le Roux

Oscar Clivio

Research Intern - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Kilian Fatras

Postdoctorate - McGill University

Principal supervisor :

Charles Guille-escuret

PhD - Université de Montréal

PhD - Université de Montréal

Github

Adam Ibrahim

PhD - Université de Montréal

Co-supervisor :

Irina Rish

Zichu Liu

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

Divyat Mahajan

PhD - Université de Montréal

Hiroki Naganuma

PhD - Université de Montréal

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

Vincent Quirion

Master's Research - Université de Montréal

Github

Blog Posts

March 18, 2024

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Read the article

Publications

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 … (see more)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.

2024-11-11

TMLR (accepted)

Solving Hidden Monotone Variational Inequalities with Surrogate Losses

Ryan D'Orazio

Danilo Vucetic

Zichu Liu

Junhyung Lyle Kim

Gauthier Gidel

Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minim… (see more)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.

2024-11-07

ArXiv (preprint)

Understanding Adam Requires Better Rotation Dependent Assumptions

Lucas Maes

Tianyue H. Zhang

Alexia Jolicoeur-Martineau

Damien Scieur

Simon Lacoste-Julien

Charles Guille-Escuret

Despite its widespread adoption, Adam's advantage over Stochastic Gradient Descent (SGD) lacks a comprehensive theoretical explanation. This… (see more) paper investigates Adam's sensitivity to rotations of the parameter space. We demonstrate that Adam's performance in training transformers degrades under random rotations of the parameter space, indicating a crucial sensitivity to the choice of basis. This reveals that conventional rotation-invariant assumptions are insufficient to capture Adam's advantages theoretically. To better understand the rotation-dependent properties that benefit Adam, we also identify structured rotations that preserve or even enhance its empirical performance. We then examine the rotation-dependent assumptions in the literature, evaluating their adequacy in explaining Adam's behavior across various rotation types. This work highlights the need for new, rotation-dependent theoretical frameworks to fully understand Adam's empirical success in modern machine learning tasks.

2024-10-25

ArXiv (preprint)

Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching

Ange-Cl'ement Akazan

Alexia Jolicoeur-Martineau

2024-10-20

ArXiv (preprint)

Compositional Risk Minimization

Divyat Mahajan

Mohammad Pezeshki

Kartik Ahuja

Pascal Vincent

2024-10-08

ArXiv (preprint)

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

Charles Guille-Escuret

Pierre-Andre Noel

David Vazquez

Joao Monteiro

2024-09-26

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… (see more)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.

2024-07-08

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

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

Mehrnaz Mofakhami

Reza Bayat

Joao Monteiro

Valentina Zantedeschi

2024-06-20

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… (see more)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.

2024-06-16

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… (see more)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.

2024-06-13

ArXiv (preprint)

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 … (see more)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.

2024-03-05

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

Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks

Daniel Beaglehole

Atish Agarwala

2024-02-07

ArXiv (preprint)