Search Results for author: Ioannis Mitliagkas

Found 52 papers, 30 papers with code

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

no code implementations7 Feb 2024 Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala

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).

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

1 code implementation22 Aug 2023 Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vazquez, Joao Monteiro

Our findings reveal that while these methods excel in detecting unknown classes, their performance is inconsistent when encountering other types of distribution shifts.

Benchmarking Out-of-Distribution Detection

Performative Prediction with Neural Networks

1 code implementation14 Apr 2023 Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel

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.

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation

1 code implementation3 Nov 2022 Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis

We study the problem of model selection in causal inference, specifically for the case of conditional average treatment effect (CATE) estimation under binary treatments.

AutoML Causal Inference +2

Towards Out-of-Distribution Adversarial Robustness

1 code implementation6 Oct 2022 Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan

Compared to existing methods, we obtain similar or superior worst-case adversarial robustness on attacks seen during training.

Adversarial Robustness

A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods

no code implementations3 Oct 2022 Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam Oberman

In this work, we consider the Partial Domain Adaptation (PDA) variant, where we have extra source classes not present in the target domain.

Model Selection Partial Domain Adaptation +1

Neural Networks Efficiently Learn Low-Dimensional Representations with SGD

no code implementations29 Sep 2022 Alireza Mousavi-Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu

We further demonstrate that, SGD-trained ReLU NNs can learn a single-index target of the form $y=f(\langle\boldsymbol{u},\boldsymbol{x}\rangle) + \epsilon$ by recovering the principal direction, with a sample complexity linear in $d$ (up to log factors), where $f$ is a monotonic function with at most polynomial growth, and $\epsilon$ is the noise.

Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation

no code implementations22 Jun 2022 Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas

It is common in computer vision to be confronted with domain shift: images which have the same class but different acquisition conditions.

Domain Adaptation

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

3 code implementations12 Jun 2022 Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, 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 gradient algorithm.

MuJoCo Games reinforcement-learning +1

Towards efficient representation identification in supervised learning

1 code implementation10 Apr 2022 Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas

In this work, we depart from these assumptions and ask: a) How can we get disentanglement when the auxiliary information does not provide conditional independence over the factors of variation?

Disentanglement

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

no code implementations28 Oct 2021 Ryan D'Orazio, Nicolas Loizou, Issam Laradji, Ioannis Mitliagkas

We investigate the convergence of stochastic mirror descent (SMD) under interpolation in relatively smooth and smooth convex optimization.

Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks

no code implementations20 Oct 2021 Manuela Girotti, Ioannis Mitliagkas, Gauthier Gidel

We theoretically analyze the Feedback Alignment (FA) algorithm, an efficient alternative to backpropagation for training neural networks.

Incremental Learning

Gotta Go Fast with Score-Based Generative Models

no code implementations NeurIPS Workshop DLDE 2021 Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas

Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data.

Denoising

Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity

1 code implementation NeurIPS 2021 Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien

Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) [Mescheder et al., 2017].

Gotta Go Fast When Generating Data with Score-Based Models

1 code implementation28 May 2021 Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas

For high-resolution images, our method leads to significantly higher quality samples than all other methods tested.

Ranked #8 on Image Generation on CIFAR-10 (Inception score metric)

Image Generation

A Study of Condition Numbers for First-Order Optimization

no code implementations10 Dec 2020 Charles Guille-Escuret, Baptiste Goujaud, Manuela Girotti, Ioannis Mitliagkas

Since smoothness and strong convexity are not continuous, we propose a comprehensive study of existing alternative metrics which we prove to be continuous.

LEAD: Min-Max Optimization from a Physical Perspective

1 code implementation26 Oct 2020 Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

A central obstacle in the optimization of such games is the rotational dynamics that hinder their convergence.

Image Generation

In Search of Robust Measures of Generalization

1 code implementation NeurIPS 2020 Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy

A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.

Generalization Bounds

Generalizing to unseen domains via distribution matching

2 code implementations3 Nov 2019 Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, Ioannis Mitliagkas

In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).

Domain Generalization LEMMA +5

Gradient penalty from a maximum margin perspective

2 code implementations15 Oct 2019 Alexia Jolicoeur-Martineau, Ioannis Mitliagkas

We present a unifying framework of expected margin maximization and show that a wide range of gradient-penalized GANs (e. g., Wasserstein, Standard, Least-Squares, and Hinge GANs) can be derived from this framework.

Image Generation

Linear Lower Bounds and Conditioning of Differentiable Games

no code implementations ICML 2020 Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas

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.

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games

no code implementations13 Jun 2019 Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

We provide new analyses of the EG's local and global convergence properties and use is to get a tighter global convergence rate for OG and CO. Our analysis covers the whole range of settings between bilinear and strongly monotone games.

Reducing the variance in online optimization by transporting past gradients

1 code implementation NeurIPS 2019 Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting.

Stochastic Optimization

In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study

no code implementations ICML Workshop Deep_Phenomen 2019 Brady Neal, Ioannis Mitliagkas

There is significant recent evidence in supervised learning that, in the over-parametrized setting, wider networks achieve better test error.

OpenAI Gym reinforcement-learning +1

Manifold Mixup: Learning Better Representations by Interpolating Hidden States

1 code implementation ICLR 2019 Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Aaron Courville, Ioannis Mitliagkas, Yoshua Bengio

Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes.

Multi-objective training of Generative Adversarial Networks with multiple discriminators

1 code implementation ICLR 2019 Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas

Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.

A Modern Take on the Bias-Variance Tradeoff in Neural Networks

no code implementations19 Oct 2018 Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas

The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve.

Test

h-detach: Modifying the LSTM Gradient Towards Better Optimization

1 code implementation ICLR 2019 Devansh Arpit, Bhargav Kanuparthi, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio

This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately over a large number of steps.

Negative Momentum for Improved Game Dynamics

1 code implementation12 Jul 2018 Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Remi Lepriol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas

Games generalize the single-objective optimization paradigm by introducing different objective functions for different players.

Manifold Mixup: Better Representations by Interpolating Hidden States

12 code implementations ICLR 2019 Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz, Yoshua Bengio

Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples.

Image Classification Test

Learning Generative Models with Locally Disentangled Latent Factors

no code implementations ICLR 2018 Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio, Ioannis Mitliagkas

One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.

Improving Gibbs Sampler Scan Quality with DoGS

no code implementations ICML 2017 Ioannis Mitliagkas, Lester Mackey

The pairwise influence matrix of Dobrushin has long been used as an analytical tool to bound the rate of convergence of Gibbs sampling.

Image Segmentation Object Recognition +2

Accelerated Stochastic Power Iteration

2 code implementations10 Jul 2017 Christopher De Sa, Bryan He, Ioannis Mitliagkas, Christopher Ré, Peng Xu

We propose a simple variant of the power iteration with an added momentum term, that achieves both the optimal sample and iteration complexity.

Dimensionality Reduction

Learning Representations and Generative Models for 3D Point Clouds

3 code implementations ICML 2018 Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas

Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling.

Representation Learning

YellowFin and the Art of Momentum Tuning

2 code implementations ICLR 2018 Jian Zhang, Ioannis Mitliagkas

We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam.

Constituency Parsing Language Modelling +1

Parallel SGD: When does averaging help?

no code implementations23 Jun 2016 Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

Consider a number of workers running SGD independently on the same pool of data and averaging the models every once in a while -- a common but not well understood practice.

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

1 code implementation14 Jun 2016 Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Dan Iter, Christopher Ré

Given a specification of a convolutional neural network, our goal is to minimize the time to train this model on a cluster of commodity CPUs and GPUs.

Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much

no code implementations NeurIPS 2016 Bryan He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions.

Asynchrony begets Momentum, with an Application to Deep Learning

3 code implementations31 May 2016 Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré

Since asynchronous methods have better hardware efficiency, this result may shed light on when asynchronous execution is more efficient for deep learning systems.

Memory Limited, Streaming PCA

no code implementations NeurIPS 2013 Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain

Standard algorithms require $O(p^2)$ memory; meanwhile no algorithm can do better than $O(kp)$ memory, since this is what the output itself requires.

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