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Session
Oral Session 2
Francis Bach
Fri Dec 06 10:55 AM -- 11:40 AM (PST) @ Harvey's Convention Center Floor, CC
Author Information
Francis Bach (INRIA - Ecole Normale Superieure)
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2022 Poster: A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning »
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2022 Spotlight: Lightning Talks 1A-4 »
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2022 Spotlight: A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning »
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2022 Poster: Variational inference via Wasserstein gradient flows »
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2022 Poster: Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays »
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2022 Poster: On the Theoretical Properties of Noise Correlation in Stochastic Optimization »
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2022 Poster: Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization »
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2022 Poster: Active Labeling: Streaming Stochastic Gradients »
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2021 Test Of Time: Online Learning for Latent Dirichlet Allocation »
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2021 Poster: Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning »
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2021 Oral: Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms »
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2021 Poster: Batch Normalization Orthogonalizes Representations in Deep Random Networks »
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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: Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model »
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2020 Poster: Learning with Differentiable Pertubed Optimizers »
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2020 Poster: Batch normalization provably avoids ranks collapse for randomly initialised deep networks »
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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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2020 Poster: Dual-Free Stochastic Decentralized Optimization with Variance Reduction »
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2019 Poster: Fast Decomposable Submodular Function Minimization using Constrained Total Variation »
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2019 Poster: Towards closing the gap between the theory and practice of SVRG »
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2019 Poster: An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums »
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2019 Poster: On Lazy Training in Differentiable Programming »
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2019 Poster: Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks »
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2019 Poster: Massively scalable Sinkhorn distances via the Nyström method »
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2019 Poster: Localized Structured Prediction »
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2019 Poster: UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization »
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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 »
Kevin Scaman · Francis Bach · Sebastien Bubeck · Laurent Massoulié · Yin Tat Lee -
2018 Poster: Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes »
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2018 Oral: Optimal Algorithms for Non-Smooth Distributed Optimization in Networks »
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2018 Poster: Relating Leverage Scores and Density using Regularized Christoffel Functions »
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2018 Poster: Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization »
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2018 Poster: Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes »
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2018 Poster: SING: Symbol-to-Instrument Neural Generator »
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2018 Poster: On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport »
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2017 : Concluding remarks »
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2017 : Neil Lawrence, Francis Bach and François Laviolette »
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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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2017 Oral: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
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2017 Poster: Nonlinear Acceleration of Stochastic Algorithms »
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2017 Poster: Integration Methods and Optimization Algorithms »
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2016 : Francis Bach. Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. »
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2016 Workshop: OPT 2016: Optimization for Machine Learning »
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2016 : Submodular Functions: from Discrete to Continuous Domains »
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2016 Workshop: Learning in High Dimensions with Structure »
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2016 Poster: Parameter Learning for Log-supermodular Distributions »
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2016 Poster: Regularized Nonlinear Acceleration »
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2016 Oral: Regularized Nonlinear Acceleration »
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2016 Poster: Stochastic Variance Reduction Methods for Saddle-Point Problems »
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2016 Poster: PAC-Bayesian Theory Meets Bayesian Inference »
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2016 Poster: Stochastic Optimization for Large-scale Optimal Transport »
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2016 Tutorial: Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity »
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2015 : Structured Sparsity and convex optimization »
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2015 : Sharp Analysis of Random Feature Expansions »
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2015 : Convergence Rates of Kernel Quadrature Rules »
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2015 Poster: Rethinking LDA: Moment Matching for Discrete ICA »
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2015 Poster: Spectral Norm Regularization of Orthonormal Representations for Graph Transduction »
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2014 Poster: Metric Learning for Temporal Sequence Alignment »
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2014 Poster: SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives »
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2013 Poster: Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) »
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2013 Spotlight: Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) »
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2013 Poster: Convex Relaxations for Permutation Problems »
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2013 Poster: Reflection methods for user-friendly submodular optimization »
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2013 Session: Tutorial Session B »
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2012 Workshop: Analysis Operator Learning vs. Dictionary Learning: Fraternal Twins in Sparse Modeling »
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2012 Poster: Multiple Operator-valued Kernel Learning »
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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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2011 Poster: Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization »
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2011 Oral: Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization »
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2011 Poster: Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning »
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2011 Poster: Trace Lasso: a trace norm regularization for correlated designs »
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2011 Spotlight: Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning »
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2011 Poster: Shaping Level Sets with Submodular Functions »
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2010 Workshop: New Directions in Multiple Kernel Learning »
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2010 Oral: Structured sparsity-inducing norms through submodular functions »
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2010 Poster: Structured sparsity-inducing norms through submodular functions »
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2010 Poster: Network Flow Algorithms for Structured Sparsity »
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2009 Workshop: Understanding Multiple Kernel Learning Methods »
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2009 Poster: Data-driven calibration of linear estimators with minimal penalties »
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2009 Poster: Asymptotically Optimal Regularization in Smooth Parametric Models »
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2009 Tutorial: Sparse Methods for Machine Learning: Theory and Algorithms »
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2008 Poster: Clustered Multi-Task Learning: A Convex Formulation »
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2008 Poster: Sparse probabilistic projections »
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2008 Spotlight: Sparse probabilistic projections »
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2008 Spotlight: Clustered Multi-Task Learning: A Convex Formulation »
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2008 Poster: Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning »
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2008 Poster: Kernel Change-point Analysis »
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2008 Poster: SDL: Supervised Dictionary Learning »
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2007 Poster: Testing for Homogeneity with Kernel Fisher Discriminant Analysis »
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2007 Poster: DIFFRAC: a discriminative and flexible framework for clustering »
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2007 Session: Session 2: Probabilistic Optimization »
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2006 Poster: Active learning for misspecified generalized linear models »
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