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no code implementations • 30 Apr 2022 • Lang Liu, Carlos Cinelli, Zaid Harchaoui

Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component.

1 code implementation • 31 Dec 2021 • Lang Liu, Soumik Pal, Zaid Harchaoui

We introduce an independence criterion based on entropy regularized optimal transport.

no code implementations • 31 Dec 2021 • Nicholas J. Irons, Meyer Scetbon, Soumik Pal, Zaid Harchaoui

Triangular flows, also known as Kn\"{o}the-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation, including popular autoregressive flow models such as real-valued non-volume preserving transformation models (Real NVP).

no code implementations • 17 Dec 2021 • Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaid Harchaoui

We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data.

1 code implementation • 2 Dec 2021 • Vincent Roulet, Zaid Harchaoui

Target Propagation (TP) algorithms compute targets instead of gradients along neural networks and propagate them backward in a way that is similar yet different than gradient back-propagation (BP).

1 code implementation • NeurIPS 2021 • Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui

We consider the problem of minimizing a convex function that is evolving according to unknown and possibly stochastic dynamics, which may depend jointly on time and on the decision variable itself.

1 code implementation • 27 Jun 2021 • Lang Liu, Joseph Salmon, Zaid Harchaoui

The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time.

1 code implementation • NeurIPS 2021 • Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui

The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance.

no code implementations • NeurIPS 2021 • Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui

We consider the problem of minimizing a convex function that is evolving in time according to unknown and possibly stochastic dynamics.

2 code implementations • NeurIPS 2021 • Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaid Harchaoui

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem.

no code implementations • 31 Dec 2020 • Vincent Roulet, Zaid Harchaoui

The notion of a Moreau envelope is central to the analysis of first-order optimization algorithms for machine learning.

3 code implementations • 12 Dec 2020 • Samuel Ainsworth, Kendall Lowrey, John Thickstun, Zaid Harchaoui, Siddhartha Srinivasa

We study the estimation of policy gradients for continuous-time systems with known dynamics.

no code implementations • 17 Nov 2020 • Zaid Harchaoui, Lang Liu, Soumik Pal

We introduce a modification of the commonly used discrete entropic regularization (Cuturi '13) such that the optimal coupling for the regularized problem can be thought of as the static Schr\"odinger bridge with $N$ particles.

1 code implementation • 30 Sep 2020 • Yassine Laguel, Jérôme Malick, Zaid Harchaoui

Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training distribution.

no code implementations • ICML 2020 • Meyer Scetbon, Zaid Harchaoui

We present a description of the function space and the smoothness class associated with a convolutional network using the machinery of reproducing kernel Hilbert spaces.

no code implementations • 28 Feb 2020 • Meyer Scetbon, Zaid Harchaoui

We present eigenvalue decay estimates of integral operators associated with compositional dot-product kernels.

1 code implementation • arXiv preprint 2020 • Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaid Harchaoui

We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution.

no code implementations • 20 Feb 2020 • Vincent Roulet, Zaid Harchaoui

We present an approach to obtain convergence guarantees of optimization algorithms for deep networks based on elementary arguments and computations.

1 code implementation • arXiv preprint 2019 • Krishna Pillutla, Sham M. Kakade, Zaid Harchaoui

We present a robust aggregation approach to make federated learning robust to settings when a fraction of the devices may be sending corrupted updates to the server.

no code implementations • 30 Dec 2019 • Corinne Jones, Vincent Roulet, Zaid Harchaoui

We present an approach for end-to-end learning that allows one to jointly learn a feature representation from unlabeled data (with or without labeled data) and predict labels for unlabeled data.

7 code implementations • 10 Dec 2019 • Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.

1 code implementation • 8 Apr 2019 • Alexander Greaves-Tunnell, Zaid Harchaoui

Representation and learning of long-range dependencies is a central challenge confronted in modern applications of machine learning to sequence data.

1 code implementation • 19 Mar 2019 • Corinne Jones, Vincent Roulet, Zaid Harchaoui

Convolutional Neural Networks, as most artificial neural networks, are commonly viewed as methods different in essence from kernel-based methods.

1 code implementation • NeurIPS 2018 • Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaid Harchaoui

We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon.

no code implementations • CVPR 2019 • Christopher Xie, Yu Xiang, Zaid Harchaoui, Dieter Fox

We consider the problem of providing dense segmentation masks for object discovery in videos.

no code implementations • 20 Nov 2018 • John Thickstun, Zaid Harchaoui, Dean P. Foster, Sham M. Kakade

This paper introduces a novel recurrent model for music composition that is tailored to the structure of polyphonic music.

no code implementations • 11 Jun 2018 • Zaid Harchaoui, Anatoli Juditsky, Arkadi Nemirovski, Dmitrii Ostrovskii

We discuss the problem of adaptive discrete-time signal denoising in the situation where the signal to be recovered admits a "linear oracle" -- an unknown linear estimate that takes the form of convolution of observations with a time-invariant filter.

1 code implementation • ICML 2018 • Dmitrii Ostrovskii, Zaid Harchaoui

Our second contribution is a computational complexity analysis of the proposed procedures, which takes into account their statistical nature and the related notion of statistical accuracy.

1 code implementation • 15 Dec 2017 • Hongzhou Lin, Julien Mairal, Zaid Harchaoui

One of the keys to achieve acceleration in theory and in practice is to solve these sub-problems with appropriate accuracy by using the right stopping criterion and the right warm-start strategy.

1 code implementation • 13 Nov 2017 • John Thickstun, Zaid Harchaoui, Dean Foster, Sham M. Kakade

This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings.

no code implementations • 31 Mar 2017 • Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaid Harchaoui

We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions.

1 code implementation • 29 Nov 2016 • John Thickstun, Zaid Harchaoui, Sham Kakade

This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research.

Ranked #6 on Music Transcription on MusicNet

1 code implementation • 4 Oct 2016 • Hongzhou Lin, Julien Mairal, Zaid Harchaoui

We propose an inexact variable-metric proximal point algorithm to accelerate gradient-based optimization algorithms.

no code implementations • 3 Aug 2016 • Niao He, Zaid Harchaoui, Yichen Wang, Le Song

Since almost all gradient-based optimization algorithms rely on Lipschitz-continuity, optimizing Poisson likelihood models with a guarantee of convergence can be challenging, especially for large-scale problems.

1 code implementation • NeurIPS 2016 • Dmitry Ostrovsky, Zaid Harchaoui, Anatoli Juditsky, Arkadi Nemirovski

We consider the problem of recovering a signal observed in Gaussian noise.

Statistics Theory Statistics Theory

no code implementations • 2 Jul 2016 • Yury Maximov, Massih-Reza Amini, Zaid Harchaoui

We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model.

no code implementations • 1 Mar 2016 • Mattis Paulin, Julien Mairal, Matthijs Douze, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid

Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision.

no code implementations • ICCV 2015 • Mattis Paulin, Matthijs Douze, Zaid Harchaoui, Julien Mairal, Florent Perronin, Cordelia Schmid

Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval.

no code implementations • 15 Aug 2015 • Danila Potapov, Matthijs Douze, Jerome Revaud, Zaid Harchaoui, Cordelia Schmid

While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantically defined categories such as "pursuit" or "romance" remains challenging. We introduce a new dataset, Action Movie Franchises, consisting of a collection of Hollywood action movie franchises.

no code implementations • NeurIPS 2015 • Niao He, Zaid Harchaoui

We propose a new first-order optimisation algorithm to solve high-dimensional non-smooth composite minimisation problems.

1 code implementation • 25 Jun 2015 • Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid

We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images.

Ranked #4 on Dense Pixel Correspondence Estimation on HPatches

Dense Pixel Correspondence Estimation Optical Flow Estimation

no code implementations • ICCV 2015 • Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid

We present experimental results for spatio-temporal localization on the UCF-Sports, J-HMDB and UCF-101 action localization datasets, where our approach outperforms the state of the art with a margin of 15%, 7% and 12% respectively in mAP.

no code implementations • CVPR 2015 • Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, Cordelia Schmid

We compare the results obtained with several state-of-the-art optical flow approaches and study the impact of the different cues used in the random forest. Furthermore, we introduce a new dataset, the YouTube Motion Boundaries dataset (YMB), that comprises 60 sequences taken from real-world videos with manually annotated motion boundaries.

2 code implementations • 30 Mar 2015 • Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid

Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce.

Ranked #5 on Zero-Shot Action Recognition on Kinetics

no code implementations • CVPR 2015 • Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid

We propose a novel approach for optical flow estimation , targeted at large displacements with significant oc-clusions.

no code implementations • NeurIPS 2014 • Julien Mairal, Piotr Koniusz, Zaid Harchaoui, Cordelia Schmid

An important goal in visual recognition is to devise image representations that are invariant to particular transformations.

Ranked #24 on Image Classification on MNIST

no code implementations • CVPR 2014 • Mattis Paulin, Jerome Revaud, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid

We propose a principled algorithm Image Transformation Pursuit (ITP) for the automatic selection of a compact set of transformations.

1 code implementation • CVPR 2014 • Yuansi Chen, Julien Mairal, Zaid Harchaoui

We revisit a pioneer unsupervised learning technique called archetypal analysis, which is related to successful data analysis methods such as sparse coding and non-negative matrix factorization.

no code implementations • 5 Mar 2014 • Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell

Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain.

Ranked #30 on Weakly Supervised Object Detection on PASCAL VOC 2007

no code implementations • CVPR 2013 • Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid

The label embedding framework offers other advantages such as the ability to leverage alternative sources of information in addition to attributes (e. g. class hierarchies) or to transition smoothly from zero-shot learning to learning with large quantities of data.

no code implementations • 10 Feb 2013 • Zaid Harchaoui, Anatoli Juditsky, Arkadi Nemirovski

Motivated by some applications in signal processing and machine learning, we consider two convex optimization problems where, given a cone $K$, a norm $\|\cdot\|$ and a smooth convex function $f$, we want either 1) to minimize the norm over the intersection of the cone and a level set of $f$, or 2) to minimize over the cone the sum of $f$ and a multiple of the norm.

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