Z. Harchaoui

Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
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The Rao, Wald, And Likelihood-Ratio Tests under Generalized Self-Concordance
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Iterated Schr\"odinger bridge approximation to Wasserstein Gradient Flows
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Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
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Distributionally Robust Optimization with Bias and Variance Reduction
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Spectral Differential Network Analysis for High-Dimensional Time Series
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Modified Gauss-Newton Algorithms under Noise
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Distributionally Robust Optimization with Bias and Variance Reduction
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Influence Diagnostics under Self-concordance
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Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods
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FiLM: Fill-in Language Models for Any-Order Generation
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Faith and Fate: Limits of Transformers on Compositionality
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On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
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Confidence Sets under Generalized Self-Concordance
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Stochastic Optimization for Spectral Risk Measures
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Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
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Differentially Private Federated Quantiles with the Distributed Discrete Gaussian Mechanism
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MAUVE Scores for Generative Models: Theory and Practice
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Complexity Bounds of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
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A Representation-Focused Training Algorithm for Deep Networks
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Statistical and Computational Guarantees for Influence Diagnostics
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Orthogonal Statistical Learning with Self-Concordant Loss
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Cortical network structure mediates response to stimulation: an optogenetic study in non-human primates
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Reconstruction of visual images from mouse retinal ganglion cell spiking activity using convolutional neural networks
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Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization
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A Bayesian approach to modeling phytoplankton population dynamics from size distribution time series
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Network structure mediates functional reorganization induced by optogenetic stimulation of non-human primate sensorimotor cortex
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Likelihood Score under Generalized Self-Concordance
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A Spectral Analysis of Dot-product Kernels
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Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees
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Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation
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MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation
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Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
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Network-level modeling of stimulation-induced functional connectivity change: An optogenetic study in non-human primate cortex
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Stochastic optimization under time drift: iterate averaging, step decay, and high probability guarantees
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On the Smoothing of Deep Networks
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An Information Divergence Measure Between Neural Text and Human Text
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Score-Based Change Detection For Gradient-Based Learning Machines
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A Superquantile Approach to Federated Learning with Heterogeneous Devices
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Entropy Regularized Optimal Transport Independence Criterion
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Discrete Schrödinger Bridges with Applications to Two-Sample Homogeneity Testing
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Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral
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Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach
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Target Propagation via Regularized Inversion
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Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
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Stochastic Optimization under Distributional Drift
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MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
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A flexible Bayesian approach to estimating size-structured matrix population models
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Asymptotics of Entropy-Regularized Optimal Transport via Chaos Decomposition
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Differentiable Programming A La Moreau
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Asymptotics of discrete Schrödinger bridges via chaos decomposition
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Federated learning with superquantile aggregation for heterogeneous data
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Risk Bounds for Multi-layer Perceptrons through Spectra of Integral Operators
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End-to-End Learning for Retrospective Change-Point Estimation
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First-Order Optimization for Superquantile-Based Supervised Learning
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Harmonic Decompositions of Convolutional Networks
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A Kernel-Based Change Detection Method to Map Shifts in Phytoplankton Communities Measured by Flow Cytometry
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Paper Index
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Faster Policy Learning with Continuous-Time Gradients
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Discriminative clustering with representation learning with any ratio of labeled to unlabeled data
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Point Process Estimation with Mirror Prox Algorithms
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A Statistical Investigation of Long Memory in Language and Music
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Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
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Learning Feature Representations for Discriminative Clustering with Limited Supervision
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Robust Aggregation for Federated Learning
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End-to-end Learning, with or without Labels
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A Simple Adaptive Tracker with Reminiscences
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On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points
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A Smoother Way to Train Structured Prediction Models
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Kernel-based Translations of Convolutional Networks
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An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks
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Advances and Open Problems in Federated Learning
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Catalyst for Gradient-based Nonconvex Optimization
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Object Discovery in Videos as Foreground Motion Clustering
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Coupled Recurrent Models for Polyphonic Music Composition
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Efficient First-Order Algorithms for Adaptive Signal Denoising
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Adaptive Denoising of Signals with Shift-Invariant Structure
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Inferring the Structure of Action Movies
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Invariances and Data Augmentation for Supervised Music Transcription
201766 citationsauthor #2
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
201740 citationsauthor #5
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
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The group k-support norm for learning with structured sparsity
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An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration
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Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm
201635 citationsauthor #3
Convolutional Patch Representations for Image Retrieval: An Unsupervised Approach
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QuickeNing: A Generic Quasi-Newton Algorithm for Faster Gradient-Based Optimization
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Fast and Simple Optimization for Poisson Likelihood Models
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Structure-Blind Signal Recovery
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Learning Features of Music from Scratch
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Filtrage adaptatif par optimisation convexe
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A Universal Catalyst for First-Order Optimization
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DeepMatching: Hierarchical Deformable Dense Matching
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Beat-Event Detection in Action Movie Franchises
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Learning to Track for Spatio-Temporal Action Localization
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Deep Convolutional Matching
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Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization
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Learning to detect Motion Boundaries
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Adaptive Recovery of Signals by Convex Optimization
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EpicFlow: Edge-preserving interpolation of correspondences for optical flow
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Local Convolutional Features with Unsupervised Training for Image Retrieval
2015179 citationsauthor #3

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