Zaïd Harchaoui

Spectral Differential Network Analysis for High-Dimensional Time Series AISTATS 2025 On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control JMLR 2025 A Generalization Theory for Zero-Shot Prediction ICML 2025 The Benefits of Balance: From Information Projections to Variance Reduction NIPS 2024 Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization NIPS 2024 StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements EMNLP 2024 JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models NAACL 2024 Distributionally Robust Optimization with Bias and Variance Reduction ICLR 2024 Faith and Fate: Limits of Transformers on Compositionality NIPS 2023 MAUVE Scores for Generative Models: Theory and Practice JMLR 2023 Stochastic Optimization under Distributional Drift JMLR 2023 Influence Diagnostics under Self-concordance AISTATS 2023 Stochastic Optimization for Spectral Risk Measures AISTATS 2023 Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates AISTATS 2022 Orthogonal Statistical Learning with Self-Concordant Loss COLT 2022 Entropy Regularized Optimal Transport Independence Criterion AISTATS 2022 Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals NIPS 2021 Faster Policy Learning with Continuous-Time Gradients L4DC 2021 A Spectral Analysis of Dot-product Kernels AISTATS 2021 Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees NIPS 2021 MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers NIPS 2021 Harmonic Decompositions of Convolutional Networks ICML 2020 A Statistical Investigation of Long Memory in Language and Music ICML 2019 Object Discovery in Videos as Foreground Motion Clustering CVPR 2019 Iterative Linearized Control: Stable Algorithms and Complexity Guarantees ICML 2019 A Kernel Multiple Change-point Algorithm via Model Selection JMLR 2019 Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice JMLR 2018 A Smoother Way to Train Structured Prediction Models NIPS 2018 Catalyst for Gradient-based Nonconvex Optimization AISTATS 2018 Efficient First-Order Algorithms for Adaptive Signal Denoising ICML 2018 Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm (Extended Abstract) IJCAI 2018 Structure-Blind Signal Recovery NIPS 2016 Learning to Detect Motion Boundaries CVPR 2015 Local Convolutional Features With Unsupervised Training for Image Retrieval ICCV 2015 Learning to Track for Spatio-Temporal Action Localization ICCV 2015 Adaptive Recovery of Signals by Convex Optimization COLT 2015 A Universal Catalyst for First-Order Optimization NIPS 2015 Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization NIPS 2015 EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow CVPR 2015 On learning to localize objects with minimal supervision ICML 2014 Fast and Robust Archetypal Analysis for Representation Learning CVPR 2014 Transformation Pursuit for Image Classification CVPR 2014 Convolutional Kernel Networks NIPS 2014 Label-Embedding for Attribute-Based Classification CVPR 2013 DeepFlow: Large Displacement Optical Flow with Deep Matching ICCV 2013 Lifted coordinate descent for learning with trace-norm regularization AISTATS 2012 A Fast, Consistent Kernel Two-Sample Test NIPS 2009 Kernel Change-point Analysis NIPS 2008 DIFFRAC: a discriminative and flexible framework for clustering NIPS 2007 Testing for Homogeneity with Kernel Fisher Discriminant Analysis NIPS 2007 Catching Change-points with Lasso NIPS 2007