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Convergence Rates of Kernel Quadrature Rules
Francis Bach
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Francis Bach (INRIA - Ecole Normale Superieure)
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2021 Poster: Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms »
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2020 : Francis Bach - Where is Machine Learning Going? »
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2020 Poster: Non-parametric Models for Non-negative Functions »
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2020 Spotlight: Non-parametric Models for Non-negative Functions »
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2020 Session: Orals & Spotlights Track 30: Optimization/Theory »
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2019 Poster: An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums »
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2019 Poster: Partially Encrypted Deep Learning using Functional Encryption »
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2019 Poster: Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses »
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2018 Poster: Optimal Algorithms for Non-Smooth Distributed Optimization in Networks »
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2018 Poster: Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes »
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2017 : Concluding remarks »
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2017 : Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance »
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2017 : Overture »
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2017 Workshop: (Almost) 50 shades of Bayesian Learning: PAC-Bayesian trends and insights »
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2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
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2013 Session: Oral Session 2 »
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2012 Workshop: Analysis Operator Learning vs. Dictionary Learning: Fraternal Twins in Sparse Modeling »
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2012 Poster: A Stochastic Gradient Method with an Exponential Convergence
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2012 Oral: A Stochastic Gradient Method with an Exponential Convergence
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2011 Workshop: Sparse Representation and Low-rank Approximation »
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