
Endtoend Learning, with or without Labels
We present an approach for endtoend learning that allows one to jointl...
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Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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Robust Aggregation for Federated Learning
We present a robust aggregation approach to make federated learning robu...
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Coupled Recurrent Models for Polyphonic Music Composition
This work describes a novel recurrent model for music composition, which...
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Catalyst Acceleration for GradientBased NonConvex Optimization
We introduce a generic scheme to solve nonconvex optimization problems u...
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A Generic QuasiNewton Algorithm for Faster GradientBased Optimization
We propose a generic approach to accelerate gradientbased optimization ...
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Fast and Simple Optimization for Poisson Likelihood Models
Poisson likelihood models have been prevalently used in imaging, social ...
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Rademacher Complexity Bounds for a Penalized Multiclass SemiSupervised Algorithm
We propose Rademacher complexity bounds for multiclass classifiers train...
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Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach
Convolutional neural networks (CNNs) have recently received a lot of att...
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DeepMatching: Hierarchical Deformable Dense Matching
We introduce a novel matching algorithm, called DeepMatching, to compute...
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Learning to track for spatiotemporal action localization
We propose an effective approach for spatiotemporal action localization...
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Conditional Gradient Algorithms for NormRegularized Smooth Convex Optimization
Motivated by some applications in signal processing and machine learning...
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LabelEmbedding for Image Classification
Attributes act as intermediate representations that enable parameter sha...
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EpicFlow: EdgePreserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at la...
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Convolutional Kernel Networks
An important goal in visual recognition is to devise image representatio...
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Fast and Robust Archetypal Analysis for Representation Learning
We revisit a pioneer unsupervised learning technique called archetypal a...
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Testing for Homogeneity with Kernel Fisher Discriminant Analysis
We propose to investigate test statistics for testing homogeneity in rep...
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Catalyst Acceleration for Firstorder Convex Optimization: from Theory to Practice
We introduce a generic scheme for accelerating gradientbased optimizati...
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Invariances and Data Augmentation for Supervised Music Transcription
This paper explores a variety of models for framebased music transcript...
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Efficient FirstOrder Algorithms for Adaptive Signal Denoising
We consider the problem of discretetime signal denoising, focusing on a...
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Adaptive Denoising of Signals with ShiftInvariant Structure
We study the problem of discretetime signal denoising, following the li...
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Object Discovery in Videos as Foreground Motion Clustering
We consider the problem of providing dense segmentation masks for object...
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A Smoother Way to Train Structured Prediction Models
We present a framework to train a structured prediction model by perform...
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Kernelbased Translations of Convolutional Networks
Convolutional Neural Networks, as most artificial neural networks, are c...
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A Statistical Investigation of Long Memory in Language and Music
Representation and learning of longrange dependencies is a central chal...
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An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks
We present an approach to obtain convergence guarantees of optimization ...
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Zaid Harchaoui
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Provost’s Initiative in Datadriven Discovery, Assistant professor in the department of statistics at University of Washington, Ph.D. at ParisTech (now in Univ. ParisSaclay), visiting assistant professor at the Courant Institute for Mathematical Sciences at New York University (2015 – 2016), Permanent Researcher on the LEAR team of Inria (2010 – 2015). Postdoctoral Fellow in the Robotics Institute of Carnegie Mellon University in 2009, Inria award for scientific excellence and the NIPS reviewer award, Area Chair for ICML 2015, ICML 2016, NIPS 2016, ICLR 2016. He is currently an associate editor of IEEE Signal Processing Letters.