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ORCIDFrancis R. Bach
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- affiliation: École Normale Supérieure, Computer Science Department
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2020 – today
- 2024
[j74]Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
:
Second Order Conditions to Decompose Smooth Functions as Sums of Squares. SIAM J. Optim. 34(1): 616-641 (2024)- [j73]Silvère Bonnabel, Marc Lambert, Francis R. Bach:
Low-Rank Plus Diagonal Approximations for Riccati-Like Matrix Differential Equations. SIAM J. Matrix Anal. Appl. 45(3): 1669-1688 (2024) - [j72]Adrien Vacher, Boris Muzellec, Francis R. Bach, François-Xavier Vialard, Alessandro Rudi:
Optimal Estimation of Smooth Transport Maps with Kernel SoS. SIAM J. Math. Data Sci. 6(2): 311-342 (2024) - [c195]Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. AISTATS 2024: 1972-1980 - [c194]Saeed Saremi, Ji Won Park, Francis R. Bach:
Chain of Log-Concave Markov Chains. ICLR 2024 - [i186]Silvère Bonnabel, Marc Lambert, Francis R. Bach:
Low-rank plus diagonal approximations for Riccati-like matrix differential equations. CoRR abs/2407.03373 (2024) - 2023
- [j71]Belinda Tzen, Anant Raj, Maxim Raginsky, Francis R. Bach:
Variational Principles for Mirror Descent and Mirror Langevin Dynamics. IEEE Control. Syst. Lett. 7: 1542-1547 (2023) - [j70]Mathieu Barré, Adrien B. Taylor, Francis R. Bach:
Principled analyses and design of first-order methods with inexact proximal operators. Math. Program. 201(1): 185-230 (2023) - [j69]Marc Lambert, Silvère Bonnabel, Francis R. Bach:
The limited-memory recursive variational Gaussian approximation (L-RVGA). Stat. Comput. 33(3): 70 (2023) - [j68]Céline Moucer, Adrien B. Taylor, Francis R. Bach:
A Systematic Approach to Lyapunov Analyses of Continuous-Time Models in Convex Optimization. SIAM J. Optim. 33(3): 1558-1586 (2023) - [j67]Francis R. Bach, Alessandro Rudi:
Exponential Convergence of Sum-of-Squares Hierarchies for Trigonometric Polynomials. SIAM J. Optim. 33(3): 2137-2159 (2023) - [j66]Francis R. Bach:
Information Theory With Kernel Methods. IEEE Trans. Inf. Theory 69(2): 752-775 (2023) - [c193]Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach:
Explicit Regularization in Overparametrized Models via Noise Injection. AISTATS 2023: 7265-7287 - [c192]Lawrence Stewart, Francis R. Bach, Quentin Berthet, Jean-Philippe Vert:
Regression as Classification: Influence of Task Formulation on Neural Network Features. AISTATS 2023: 11563-11582 - [c191]Loucas Pillaud-Vivien, Francis R. Bach:
Kernelized Diffusion Maps. COLT 2023: 5236-5259 - [c190]Marc Lambert, Silvère Bonnabel, Francis R. Bach:
Variational Gaussian Approximation of the Kushner Optimal Filter. GSI (1) 2023: 395-404 - [c189]Amir Joudaki, Hadi Daneshmand, Francis R. Bach:
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization. ICML 2023: 15388-15400 - [c188]Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach:
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. ICML 2023: 37273-37292 - [c187]Amir Joudaki, Hadi Daneshmand, Francis R. Bach:
On the impact of activation and normalization in obtaining isometric embeddings at initialization. NeurIPS 2023 - [c186]Lawrence Stewart, Francis R. Bach, Felipe Llinares-López, Quentin Berthet:
Differentiable Clustering with Perturbed Spanning Forests. NeurIPS 2023 - [i185]Francis R. Bach:
On the relationship between multivariate splines and infinitely-wide neural networks. CoRR abs/2302.03459 (2023) - [i184]Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach:
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. CoRR abs/2302.03542 (2023) - [i183]Loucas Pillaud-Vivien, Francis R. Bach:
Kernelized Diffusion maps. CoRR abs/2302.06757 (2023) - [i182]Francis R. Bach:
High-dimensional analysis of double descent for linear regression with random projections. CoRR abs/2303.01372 (2023) - [i181]David Holzmüller, Francis R. Bach:
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation. CoRR abs/2303.03237 (2023) - [i180]Belinda Tzen, Anant Raj, Maxim Raginsky, Francis R. Bach:
Variational Principles for Mirror Descent and Mirror Langevin Dynamics. CoRR abs/2303.09532 (2023) - [i179]Saeed Saremi, Rupesh Kumar Srivastava, Francis R. Bach:
Universal Smoothed Score Functions for Generative Modeling. CoRR abs/2303.11669 (2023) - [i178]Marc Lambert, Silvère Bonnabel, Francis R. Bach:
The limited-memory recursive variational Gaussian approximation (L-RVGA). CoRR abs/2303.14195 (2023) - [i177]Lawrence Stewart, Francis R. Bach, Felipe Llinares-López, Quentin Berthet:
Differentiable Clustering with Perturbed Spanning Forests. CoRR abs/2305.16358 (2023) - [i176]Amir Joudaki, Hadi Daneshmand, Francis R. Bach:
On the impact of activation and normalization in obtaining isometric embeddings at initialization. CoRR abs/2305.18399 (2023) - [i175]Saeed Saremi, Ji Won Park, Francis R. Bach:
Chain of Log-Concave Markov Chains. CoRR abs/2305.19473 (2023) - [i174]Francis R. Bach, Elisabetta Cornacchia, Luca Pesce, Giovanni Piccioli:
Theory and applications of the Sum-Of-Squares technique. CoRR abs/2306.16255 (2023) - [i173]Marc Lambert, Silvère Bonnabel, Francis R. Bach:
Variational Gaussian approximation of the Kushner optimal filter. CoRR abs/2310.01859 (2023) - [i172]Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. CoRR abs/2311.12436 (2023) - 2022
- [j65]Mathieu Barré, Adrien B. Taylor, Francis R. Bach:
A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly convex objectives. Open J. Math. Optim. 3: 1-15 (2022) - [j64]Yifan Sun, Francis R. Bach:
Screening for a Reweighted Penalized Conditional Gradient Method. Open J. Math. Optim. 3: 1-35 (2022) - [j63]Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis R. Bach:
AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. Proc. Priv. Enhancing Technol. 2022(1): 291-316 (2022) - [j62]Marc Lambert, Silvère Bonnabel, Francis R. Bach:
The recursive variational Gaussian approximation (R-VGA). Stat. Comput. 32(1): 10 (2022) - [j61]Alexandre Défossez, Léon Bottou, Francis R. Bach, Nicolas Usunier:
A Simple Convergence Proof of Adam and Adagrad. Trans. Mach. Learn. Res. 2022 (2022) - [c185]Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Sampling from Arbitrary Functions via PSD Models. AISTATS 2022: 2823-2861 - [c184]Alex Nowak, Alessandro Rudi, Francis R. Bach:
On the Consistency of Max-Margin Losses. AISTATS 2022: 4612-4633 - [c183]Eloïse Berthier, Justin Carpentier, Alessandro Rudi, Francis R. Bach:
Infinite-Dimensional Sums-of-Squares for Optimal Control. CDC 2022: 577-582 - [c182]Marc Lambert, Silvère Bonnabel, Francis R. Bach:
The continuous-discrete variational Kalman filter (CD-VKF). CDC 2022: 6632-6639 - [c181]Blake E. Woodworth, Francis R. Bach, Alessandro Rudi:
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. COLT 2022: 4620-4642 - [c180]Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi:
Anticorrelated Noise Injection for Improved Generalization. ICML 2022: 17094-17116 - [c179]Anant Raj, Francis R. Bach:
Convergence of Uncertainty Sampling for Active Learning. ICML 2022: 18310-18331 - [c178]Eloïse Berthier, Ziad Kobeissi, Francis R. Bach:
A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning. NeurIPS 2022 - [c177]Vivien Cabannes, Francis R. Bach, Vianney Perchet, Alessandro Rudi:
Active Labeling: Streaming Stochastic Gradients. NeurIPS 2022 - [c176]Benjamin Dubois-Taine, Francis R. Bach, Quentin Berthet, Adrien B. Taylor:
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization. NeurIPS 2022 - [c175]Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet:
Variational inference via Wasserstein gradient flows. NeurIPS 2022 - [c174]Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting:
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. NeurIPS 2022 - [c173]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. NeurIPS 2022 - [i171]Théo Ryffel, Francis R. Bach, David Pointcheval:
Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics. CoRR abs/2201.11980 (2022) - [i170]Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi:
Anticorrelated Noise Injection for Improved Generalization. CoRR abs/2202.02831 (2022) - [i169]Ziad Kobeissi, Francis R. Bach:
On a Variance Reduction Correction of the Temporal Difference for Policy Evaluation in the Stochastic Continuous Setting. CoRR abs/2202.07960 (2022) - [i168]Francis R. Bach:
Information Theory with Kernel Methods. CoRR abs/2202.08545 (2022) - [i167]Blake E. Woodworth, Francis R. Bach, Alessandro Rudi:
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. CoRR abs/2204.04970 (2022) - [i166]Hadi Daneshmand, Francis R. Bach:
Polynomial-time sparse measure recovery. CoRR abs/2204.07879 (2022) - [i165]Benjamin Dubois-Taine, Francis R. Bach, Quentin Berthet, Adrien B. Taylor:
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization. CoRR abs/2205.12751 (2022) - [i164]Céline Moucer, Adrien B. Taylor, Francis R. Bach:
A systematic approach to Lyapunov analyses of continuous-time models in convex optimization. CoRR abs/2205.12772 (2022) - [i163]Amir Joudaki, Hadi Daneshmand, Francis R. Bach:
Entropy Maximization with Depth: A Variational Principle for Random Neural Networks. CoRR abs/2205.13076 (2022) - [i162]Vivien Cabannes, Francis R. Bach, Vianney Perchet, Alessandro Rudi:
Active Labeling: Streaming Stochastic Gradients. CoRR abs/2205.13255 (2022) - [i161]Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet:
Variational inference via Wasserstein gradient flows. CoRR abs/2205.15902 (2022) - [i160]Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach:
Explicit Regularization in Overparametrized Models via Noise Injection. CoRR abs/2206.04613 (2022) - [i159]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. CoRR abs/2206.07638 (2022) - [i158]Francis R. Bach:
Sum-of-Squares Relaxations for Information Theory and Variational Inference. CoRR abs/2206.13285 (2022) - [i157]Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting:
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. CoRR abs/2209.09162 (2022) - [i156]Lawrence Stewart, Francis R. Bach, Quentin Berthet, Jean-Philippe Vert:
Regression as Classification: Influence of Task Formulation on Neural Network Features. CoRR abs/2211.05641 (2022) - 2021
- [j60]Robert M. Gower, Peter Richtárik, Francis R. Bach:
Stochastic quasi-gradient methods: variance reduction via Jacobian sketching. Math. Program. 188(1): 135-192 (2021) - [j59]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
An Optimal Algorithm for Decentralized Finite-Sum Optimization. SIAM J. Optim. 31(4): 2753-2783 (2021) - [j58]Francis R. Bach:
On the Effectiveness of Richardson Extrapolation in Data Science. SIAM J. Math. Data Sci. 3(4): 1251-1277 (2021) - [c172]Anant Raj, Francis R. Bach:
Explicit Regularization of Stochastic Gradient Methods through Duality. AISTATS 2021: 1882-1890 - [c171]Vivien A. Cabannes, Francis R. Bach, Alessandro Rudi:
Fast Rates for Structured Prediction. COLT 2021: 823-865 - [c170]Adrien Vacher, Boris Muzellec, Alessandro Rudi, Francis R. Bach, François-Xavier Vialard:
A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation. COLT 2021: 4143-4173 - [c169]Eloïse Berthier, Justin Carpentier, Francis R. Bach:
Fast and Robust Stability Region Estimation for Nonlinear Dynamical Systems. ECC 2021: 1412-1419 - [c168]Alberto Bietti, Francis R. Bach:
Deep Equals Shallow for ReLU Networks in Kernel Regimes. ICLR 2021 - [c167]Vivien A. Cabannes, Francis R. Bach, Alessandro Rudi:
Disambiguation of Weak Supervision leading to Exponential Convergence rates. ICML 2021: 1147-1157 - [c166]Hadi Daneshmand, Amir Joudaki, Francis R. Bach:
Batch Normalization Orthogonalizes Representations in Deep Random Networks. NeurIPS 2021: 4896-4906 - [c165]Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien B. Taylor:
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms. NeurIPS 2021: 28054-28066 - [c164]Vivien Cabannes, Loucas Pillaud-Vivien, Francis R. Bach, Alessandro Rudi:
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. NeurIPS 2021: 30439-30451 - [i155]Vivien Cabannes, Alessandro Rudi, Francis R. Bach:
Fast rates in structured prediction. CoRR abs/2102.00760 (2021) - [i154]Vivien Cabannes, Francis R. Bach, Alessandro Rudi:
Disambiguation of weak supervision with exponential convergence rates. CoRR abs/2102.02789 (2021) - [i153]Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Adrien B. Taylor:
A Continuized View on Nesterov Acceleration. CoRR abs/2102.06035 (2021) - [i152]Alex Nowak-Vila, Alessandro Rudi, Francis R. Bach:
Max-Margin is Dead, Long Live Max-Margin! CoRR abs/2105.15069 (2021) - [i151]Hadi Daneshmand, Amir Joudaki, Francis R. Bach:
Batch Normalization Orthogonalizes Representations in Deep Random Networks. CoRR abs/2106.03970 (2021) - [i150]Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Hadrien Hendrikx, Laurent Massoulié, Adrien B. Taylor:
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. CoRR abs/2106.07644 (2021) - [i149]Boris Muzellec, Francis R. Bach, Alessandro Rudi:
A Note on Optimizing Distributions using Kernel Mean Embeddings. CoRR abs/2106.09994 (2021) - [i148]Yifan Sun, Francis R. Bach:
Screening for a Reweighted Penalized Conditional Gradient Method. CoRR abs/2107.01106 (2021) - [i147]Francis R. Bach, Lenaïc Chizat:
Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization. CoRR abs/2110.08084 (2021) - [i146]Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Sampling from Arbitrary Functions via PSD Models. CoRR abs/2110.10527 (2021) - [i145]Anant Raj, Francis R. Bach:
Convergence of Uncertainty Sampling for Active Learning. CoRR abs/2110.15784 (2021) - [i144]Boris Muzellec, Francis R. Bach, Alessandro Rudi:
Learning PSD-valued functions using kernel sums-of-squares. CoRR abs/2111.11306 (2021) - [i143]Boris Muzellec, Adrien Vacher, Francis R. Bach, François-Xavier Vialard, Alessandro Rudi:
Near-optimal estimation of smooth transport maps with kernel sums-of-squares. CoRR abs/2112.01907 (2021) - 2020
- [j57]Eloïse Berthier, Francis R. Bach:
Max-Plus Linear Approximations for Deterministic Continuous-State Markov Decision Processes. IEEE Control. Syst. Lett. 4(3): 767-772 (2020) - [j56]Damien Scieur, Alexandre d'Aspremont, Francis R. Bach:
Regularized nonlinear acceleration. Math. Program. 179(1): 47-83 (2020) - [j55]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. Proc. IEEE 108(11): 1968-1983 (2020) - [j54]Raphaël Berthier, Francis R. Bach, Pierre Gaillard:
Accelerated Gossip in Networks of Given Dimension Using Jacobi Polynomial Iterations. SIAM J. Math. Data Sci. 2(1): 24-47 (2020) - [c163]Loucas Pillaud-Vivien, Francis R. Bach, Tony Lelièvre, Alessandro Rudi, Gabriel Stoltz:
Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions. AISTATS 2020: 2753-2763 - [c162]Lénaïc Chizat, Francis R. Bach:
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss. COLT 2020: 1305-1338 - [c161]Marin Ballu, Quentin Berthet, Francis R. Bach:
Stochastic Optimization for Regularized Wasserstein Estimators. ICML 2020: 602-612 - [c160]Vivien Cabannes, Alessandro Rudi, Francis R. Bach:
Structured Prediction with Partial Labelling through the Infimum Loss. ICML 2020: 1230-1239 - [c159]Hadrien Hendrikx, Lin Xiao, Sébastien Bubeck, Francis R. Bach, Laurent Massoulié:
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization. ICML 2020: 4203-4227 - [c158]Alex Nowak, Francis R. Bach, Alessandro Rudi:
Consistent Structured Prediction with Max-Min Margin Markov Networks. ICML 2020: 7381-7391 - [c157]Raman Sankaran, Francis R. Bach, Chiranjib Bhattacharyya:
Learning With Subquadratic Regularization : A Primal-Dual Approach. IJCAI 2020: 1963-1969 - [c156]Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach:
Learning with Differentiable Pertubed Optimizers. NeurIPS 2020 - [c155]Raphaël Berthier, Francis R. Bach, Pierre Gaillard:
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model. NeurIPS 2020 - [c154]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Batch normalization provably avoids ranks collapse for randomly initialised deep networks. NeurIPS 2020 - [c153]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
Dual-Free Stochastic Decentralized Optimization with Variance Reduction. NeurIPS 2020 - [c152]Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Non-parametric Models for Non-negative Functions. NeurIPS 2020 - [i142]Francis R. Bach:
On the Effectiveness of Richardson Extrapolation in Machine Learning. CoRR abs/2002.02835 (2020) - [i141]Lénaïc Chizat, Francis R. Bach:
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss. CoRR abs/2002.04486 (2020) - [i140]Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach:
Learning with Differentiable Perturbed Optimizers. CoRR abs/2002.08676 (2020) - [i139]Marin Ballu, Quentin Berthet, Francis R. Bach:
Stochastic Optimization for Regularized Wasserstein Estimators. CoRR abs/2002.08695 (2020) - [i138]Yifan Sun, Francis R. Bach:
Safe Screening for the Generalized Conditional Gradient Method. CoRR abs/2002.09718 (2020) - [i137]Hadrien Hendrikx, Lin Xiao, Sébastien Bubeck, Francis R. Bach, Laurent Massoulié:
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization. CoRR abs/2002.10726 (2020) - [i136]Vivien Cabannes, Alessandro Rudi, Francis R. Bach:
Structured Prediction with Partial Labelling through the Infimum Loss. CoRR abs/2003.00920 (2020) - [i135]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. CoRR abs/2003.01652 (2020) - [i134]Alexandre Défossez, Léon Bottou, Francis R. Bach, Nicolas Usunier:
On the Convergence of Adam and Adagrad. CoRR abs/2003.02395 (2020) - [i133]Anant Raj, Francis R. Bach:
Explicit Regularization of Stochastic Gradient Methods through Duality. CoRR abs/2003.13807 (2020) - [i132]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
An Optimal Algorithm for Decentralized Finite Sum Optimization. CoRR abs/2005.10675 (2020) - [i131]Théo Ryffel, David Pointcheval, Francis R. Bach:
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. CoRR abs/2006.04593 (2020) - [i130]Mathieu Barré, Adrien B. Taylor, Francis R. Bach:
Principled Analyses and Design of First-Order Methods with Inexact Proximal Operators. CoRR abs/2006.06041 (2020) - [i129]Raphaël Berthier, Francis R. Bach, Pierre Gaillard:
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model. CoRR abs/2006.08212 (2020) - [i128]Thomas Eboli, Alex Nowak-Vila, Jian Sun, Francis R. Bach, Jean Ponce, Alessandro Rudi:
Structured and Localized Image Restoration. CoRR abs/2006.09261 (2020) - [i127]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
Dual-Free Stochastic Decentralized Optimization with Variance Reduction. CoRR abs/2006.14384 (2020) - [i126]Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi:
Consistent Structured Prediction with Max-Min Margin Markov Networks. CoRR abs/2007.01012 (2020) - [i125]Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Non-parametric Models for Non-negative Functions. CoRR abs/2007.03926 (2020) - [i124]Alberto Bietti, Francis R. Bach:
Deep Equals Shallow for ReLU Networks in Kernel Regimes. CoRR abs/2009.14397 (2020) - [i123]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. CoRR abs/2010.00892 (2020) - [i122]Alessandro Rudi, Ulysse Marteau-Ferey, Francis R. Bach:
Finding Global Minima via Kernel Approximations. CoRR abs/2012.11978 (2020)
2010 – 2019
- 2019
- [j53]Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié:
Optimal Convergence Rates for Convex Distributed Optimization in Networks. J. Mach. Learn. Res. 20: 159:1-159:31 (2019) - [j52]Francis R. Bach:
Submodular functions: from discrete to continuous domains. Math. Program. 175(1-2): 419-459 (2019) - [j51]Lucas Rencker, Francis R. Bach, Wenwu Wang, Mark D. Plumbley:
Sparse Recovery and Dictionary Learning From Nonlinear Compressive Measurements. IEEE Trans. Signal Process. 67(21): 5659-5670 (2019) - [c151]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives. AISTATS 2019: 897-906 - [c150]Sharan Vaswani, Francis R. Bach, Mark Schmidt:
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. AISTATS 2019: 1195-1204 - [c149]Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach:
Stochastic algorithms with descent guarantees for ICA. AISTATS 2019: 1564-1573 - [c148]Aude Genevay, Lénaïc Chizat, Francis R. Bach, Marco Cuturi, Gabriel Peyré:
Sample Complexity of Sinkhorn Divergences. AISTATS 2019: 1574-1583 - [c147]Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi:
Sharp Analysis of Learning with Discrete Losses. AISTATS 2019: 1920-1929 - [c146]Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag:
Overcomplete Independent Component Analysis via SDP. AISTATS 2019: 2583-2592 - [c145]Francis R. Bach, Kfir Y. Levy:
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. COLT 2019: 164-194 - [c144]Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis R. Bach, Alessandro Rudi:
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance. COLT 2019: 2294-2340 - [c143]Adrien B. Taylor, Francis R. Bach:
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions. COLT 2019: 2934-2992 - [c142]Huy V. Vo, Francis R. Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Pérez, Jean Ponce:
Unsupervised Image Matching and Object Discovery as Optimization. CVPR 2019: 8287-8296 - [c141]Tatiana Shpakova, Francis R. Bach, Mike E. Davies:
Hyper-parameter Learning for Sparse Structured Probabilistic Models. ICASSP 2019: 3347-3351 - [c140]Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower:
Towards closing the gap between the theory and practice of SVRG. NeurIPS 2019: 646-656 - [c139]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums. NeurIPS 2019: 952-962 - [c138]Lénaïc Chizat, Edouard Oyallon, Francis R. Bach:
On Lazy Training in Differentiable Programming. NeurIPS 2019: 2933-2943 - [c137]Gauthier Gidel, Francis R. Bach, Simon Lacoste-Julien:
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks. NeurIPS 2019: 3196-3206 - [c136]Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed:
Massively scalable Sinkhorn distances via the Nyström method. NeurIPS 2019: 4429-4439 - [c135]Théo Ryffel, David Pointcheval, Francis R. Bach, Edouard Dufour-Sans, Romain Gay:
Partially Encrypted Deep Learning using Functional Encryption. NeurIPS 2019: 4519-4530 - [c134]Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher:
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. NeurIPS 2019: 6257-6266 - [c133]Carlo Ciliberto, Francis R. Bach, Alessandro Rudi:
Localized Structured Prediction. NeurIPS 2019: 7299-7309 - [c132]Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses. NeurIPS 2019: 7634-7644 - [c131]Senanayak Sesh Kumar Karri, Francis R. Bach, Thomas Pock:
Fast Decomposable Submodular Function Minimization using Constrained Total Variation. NeurIPS 2019: 8183-8193 - [i121]Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag:
Overcomplete Independent Component Analysis via SDP. CoRR abs/1901.08334 (2019) - [i120]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums. CoRR abs/1901.09865 (2019) - [i119]Adrien B. Taylor, Francis R. Bach:
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions. CoRR abs/1902.00947 (2019) - [i118]Francis R. Bach, Kfir Y. Levy:
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. CoRR abs/1902.01637 (2019) - [i117]Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi:
A General Theory for Structured Prediction with Smooth Convex Surrogates. CoRR abs/1902.01958 (2019) - [i116]Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis R. Bach, Alessandro Rudi:
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance. CoRR abs/1902.03046 (2019) - [i115]Dmitry Babichev, Dmitrii Ostrovskii, Francis R. Bach:
Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification. CoRR abs/1902.03755 (2019) - [i114]Huy V. Vo, Francis R. Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Pérez, Jean Ponce:
Unsupervised Image Matching and Object Discovery as Optimization. CoRR abs/1904.03148 (2019) - [i113]Gauthier Gidel, Francis R. Bach, Simon Lacoste-Julien:
Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks. CoRR abs/1904.13262 (2019) - [i112]Théo Ryffel, Edouard Dufour Sans, Romain Gay, Francis R. Bach, David Pointcheval:
Partially Encrypted Machine Learning using Functional Encryption. CoRR abs/1905.10214 (2019) - [i111]Senanayak Sesh Kumar Karri, Francis R. Bach, Thomas Pock:
Fast Decomposable Submodular Function Minimization using Constrained Total Variation. CoRR abs/1905.11327 (2019) - [i110]Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums. CoRR abs/1905.11394 (2019) - [i109]Francis R. Bach:
Max-Plus Matching Pursuit for Deterministic Markov Decision Processes. CoRR abs/1906.08524 (2019) - [i108]Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses. CoRR abs/1907.01771 (2019) - [i107]Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower:
Towards closing the gap between the theory and practice of SVRG. CoRR abs/1908.02725 (2019) - [i106]Alexandre Défossez, Nicolas Usunier, Léon Bottou, Francis R. Bach:
Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed. CoRR abs/1909.01174 (2019) - [i105]Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher:
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. CoRR abs/1910.13857 (2019) - [i104]Alexandre Défossez, Nicolas Usunier, Léon Bottou, Francis R. Bach:
Music Source Separation in the Waveform Domain. CoRR abs/1911.13254 (2019) - 2018
- [c130]Anaël Beaugnon, Pierre Chifflier, Francis R. Bach:
End-to-End Active Learning for Computer Security Experts. AAAI Workshops 2018: 217-224 - [c129]Christophe Dupuy, Francis R. Bach:
Learning Determinantal Point Processes in Sublinear Time. AISTATS 2018: 244-257 - [c128]Robert M. Gower, Nicolas Le Roux, Francis R. Bach:
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. AISTATS 2018: 707-715 - [c127]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. AISTATS 2018: 1233-1242 - [c126]Marwa El Halabi, Francis R. Bach, Volkan Cevher:
Combinatorial Penalties: Which structures are preserved by convex relaxations? AISTATS 2018: 1551-1560 - [c125]Achintya Kundu, Francis R. Bach, Chiranjib Bhattacharyya:
Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach. AISTATS 2018: i - [c124]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Exponential Convergence of Testing Error for Stochastic Gradient Methods. COLT 2018: 250-296 - [c123]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT 2018: 650-687 - [c122]Lucas Rencker, Francis R. Bach, Wenwu Wang, Mark D. Plumbley:
Consistent Dictionary Learning for Signal Declipping. LVA/ICA 2018: 446-455 - [c121]Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach:
Nonlinear Acceleration of CNNs. ICLR (Workshop) 2018 - [c120]Francis R. Bach:
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization. NeurIPS 2018: 1-10 - [c119]Junqi Tang, Mohammad Golbabaee, Francis R. Bach, Mike E. Davies:
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes. NeurIPS 2018: 427-438 - [c118]Edouard Pauwels, Francis R. Bach, Jean-Philippe Vert:
Relating Leverage Scores and Density using Regularized Christoffel Functions. NeurIPS 2018: 1670-1679 - [c117]Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee:
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. NeurIPS 2018: 2745-2754 - [c116]Lénaïc Chizat, Francis R. Bach:
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport. NeurIPS 2018: 3040-3050 - [c115]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. NeurIPS 2018: 8125-8135 - [c114]Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Léon Bottou, Francis R. Bach:
SING: Symbol-to-Instrument Neural Generator. NeurIPS 2018: 9055-9065 - [c113]Dmitry Babichev, Francis R. Bach:
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling. UAI 2018: 219-228 - [c112]Tatiana Shpakova, Francis R. Bach, Anton Osokin:
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models. UAI 2018: 279-289 - [i103]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. CoRR abs/1802.09128 (2018) - [i102]Dmitry Babichev, Francis R. Bach:
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling. CoRR abs/1804.05567 (2018) - [i101]Edouard Pauwels, Francis R. Bach, Jean-Philippe Vert:
Relating Leverage Scores and Density using Regularized Christoffel Functions. CoRR abs/1805.07943 (2018) - [i100]Raphaël Berthier, Francis R. Bach, Pierre Gaillard:
Gossip of Statistical Observations using Orthogonal Polynomials. CoRR abs/1805.08531 (2018) - [i99]Lenaïc Chizat, Francis R. Bach:
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport. CoRR abs/1805.09545 (2018) - [i98]Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach:
Nonlinear Acceleration of Deep Neural Networks. CoRR abs/1805.09639 (2018) - [i97]Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach:
EM algorithms for ICA. CoRR abs/1805.10054 (2018) - [i96]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. CoRR abs/1805.10074 (2018) - [i95]Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach:
Nonlinear Acceleration of CNNs. CoRR abs/1806.00370 (2018) - [i94]Carlo Ciliberto, Francis R. Bach, Alessandro Rudi:
Localized Structured Prediction. CoRR abs/1806.02402 (2018) - [i93]Hadrien Hendrikx, Laurent Massoulié, Francis R. Bach:
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives. CoRR abs/1810.02660 (2018) - [i92]Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi:
Sharp Analysis of Learning with Discrete Losses. CoRR abs/1810.06839 (2018) - [i91]Sharan Vaswani, Francis R. Bach, Mark Schmidt:
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. CoRR abs/1810.07288 (2018) - [i90]Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Léon Bottou, Francis R. Bach:
SING: Symbol-to-Instrument Neural Generator. CoRR abs/1810.09785 (2018) - [i89]Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Weed:
Approximating the Quadratic Transportation Metric in Near-Linear Time. CoRR abs/1810.10046 (2018) - [i88]Tatiana Shpakova, Francis R. Bach, Anton Osokin:
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models. CoRR abs/1811.08725 (2018) - [i87]Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Weed:
Massively scalable Sinkhorn distances via the Nyström method. CoRR abs/1812.05189 (2018) - [i86]Lénaïc Chizat, Francis R. Bach:
A Note on Lazy Training in Supervised Differentiable Programming. CoRR abs/1812.07956 (2018) - 2017
- [j50]Francis R. Bach:
Breaking the Curse of Dimensionality with Convex Neural Networks. J. Mach. Learn. Res. 18: 19:1-19:53 (2017) - [j49]Francis R. Bach:
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions. J. Mach. Learn. Res. 18: 21:1-21:38 (2017) - [j48]Fabian Pedregosa, Francis R. Bach, Alexandre Gramfort:
On the Consistency of Ordinal Regression Methods. J. Mach. Learn. Res. 18: 55:1-55:35 (2017) - [j47]Nicolas Flammarion, Balamurugan Palaniappan, Francis R. Bach:
Robust Discriminative Clustering with Sparse Regularizers. J. Mach. Learn. Res. 18: 80:1-80:50 (2017) - [j46]Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach:
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. J. Mach. Learn. Res. 18: 101:1-101:51 (2017) - [j45]Christophe Dupuy, Francis R. Bach:
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling. J. Mach. Learn. Res. 18: 126:1-126:45 (2017) - [j44]K. S. Sesh Kumar, Francis R. Bach:
Active-set Methods for Submodular Minimization Problems. J. Mach. Learn. Res. 18: 132:1-132:31 (2017) - [j43]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1-2): 83-112 (2017) - [j42]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Erratum to: Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1-2): 113 (2017) - [c111]Raman Sankaran, Francis R. Bach, Chiranjib Bhattacharyya:
Identifying Groups of Strongly Correlated Variables through Smoothed Ordered Weighted L1-norms. AISTATS 2017: 1123-1131 - [c110]Thomas Schatz, Francis R. Bach, Emmanuel Dupoux:
ASR Systems as Models of Phonetic Category Perception in Adults. CogSci 2017 - [c109]Nicolas Flammarion, Francis R. Bach:
Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$. COLT 2017: 831-875 - [c108]Rafael S. Rezende, Joaquin Zepeda, Jean Ponce, Francis R. Bach, Patrick Pérez:
Kernel Square-Loss Exemplar Machines for Image Retrieval. CVPR 2017: 7263-7271 - [c107]Felipe Yanez, Francis R. Bach:
Primal-dual algorithms for non-negative matrix factorization with the Kullback-Leibler divergence. ICASSP 2017: 2257-2261 - [c106]Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié:
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. ICML 2017: 3027-3036 - [c105]Thomas Schatz, Rory Turnbull, Francis R. Bach, Emmanuel Dupoux:
A Quantitative Measure of the Impact of Coarticulation on Phone Discriminability. INTERSPEECH 2017: 3033-3037 - [c104]Christophe Dupuy, Francis R. Bach, Christophe Diot:
Qualitative and Descriptive Topic Extraction from Movie Reviews Using LDA. MLDM 2017: 91-106 - [c103]Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. NIPS 2017: 302-313 - [c102]Damien Scieur, Vincent Roulet, Francis R. Bach, Alexandre d'Aspremont:
Integration Methods and Optimization Algorithms. NIPS 2017: 1109-1118 - [c101]Damien Scieur, Francis R. Bach, Alexandre d'Aspremont:
Nonlinear Acceleration of Stochastic Algorithms. NIPS 2017: 3982-3991 - [c100]Anaël Beaugnon, Pierre Chifflier, Francis R. Bach:
ILAB: An Interactive Labelling Strategy for Intrusion Detection. RAID 2017: 120-140 - [i85]Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. CoRR abs/1703.02403 (2017) - [i84]Francis R. Bach:
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization. CoRR abs/1707.09157 (2017) - [i83]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. CoRR abs/1709.01434 (2017) - [i82]Marwa El Halabi, Francis R. Bach, Volkan Cevher:
Combinatorial Penalties: Which structures are preserved by convex relaxations? CoRR abs/1710.06273 (2017) - [i81]Robert M. Gower, Nicolas Le Roux, Francis R. Bach:
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. CoRR abs/1710.07462 (2017) - [i80]Alexandre Défossez, Francis R. Bach:
AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods. CoRR abs/1711.01761 (2017) - [i79]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Exponential convergence of testing error for stochastic gradient methods. CoRR abs/1712.04755 (2017) - 2016
- [c99]Francis R. Bach, Vianney Perchet:
Highly-Smooth Zero-th Order Online Optimization. COLT 2016: 257-283 - [c98]Rémi Lajugie, Piotr Bojanowski, Philippe Cuvillier, Sylvain Arlot, Francis R. Bach:
A weakly-supervised discriminative model for audio-to-score alignment. ICASSP 2016: 2484-2488 - [c97]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Beyond CCA: Moment Matching for Multi-View Models. ICML 2016: 458-467 - [c96]Sara El Aouad, Christophe Dupuy, Renata Teixeira, Francis R. Bach, Christophe Diot:
Exploiting Crowd Sourced Reviews to Explain Movie Recommendation. NETYS 2016: 193-201 - [c95]Damien Scieur, Alexandre d'Aspremont, Francis R. Bach:
Regularized Nonlinear Acceleration. NIPS 2016: 712-720 - [c94]Balamurugan Palaniappan, Francis R. Bach:
Stochastic Variance Reduction Methods for Saddle-Point Problems. NIPS 2016: 1408-1416 - [c93]Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien:
PAC-Bayesian Theory Meets Bayesian Inference. NIPS 2016: 1876-1884 - [c92]Tatiana Shpakova, Francis R. Bach:
Parameter Learning for Log-supermodular Distributions. NIPS 2016: 3234-3242 - [c91]Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis R. Bach:
Stochastic Optimization for Large-scale Optimal Transport. NIPS 2016: 3432-3440 - [i78]Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach:
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. CoRR abs/1602.05419 (2016) - [i77]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Beyond CCA: Moment Matching for Multi-View Models. CoRR abs/1602.09013 (2016) - [i76]Christophe Dupuy, Francis R. Bach:
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling. CoRR abs/1603.02644 (2016) - [i75]Balamurugan Palaniappan, Francis R. Bach:
Stochastic Variance Reduction Methods for Saddle-Point Problems. CoRR abs/1605.06398 (2016) - [i74]Francis R. Bach, Vianney Perchet:
Highly-Smooth Zero-th Order Online Optimization Vianney Perchet. CoRR abs/1605.08165 (2016) - [i73]Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis R. Bach:
Stochastic Optimization for Large-scale Optimal Transport. CoRR abs/1605.08527 (2016) - [i72]Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien:
PAC-Bayesian Theory Meets Bayesian Inference. CoRR abs/1605.08636 (2016) - [i71]Tatiana Shpakova, Francis R. Bach:
Parameter Learning for Log-supermodular Distributions. CoRR abs/1608.05258 (2016) - [i70]Nicolas Flammarion, Balamurugan Palaniappan, Francis R. Bach:
Robust Discriminative Clustering with Sparse Regularizers. CoRR abs/1608.08052 (2016) - [i69]Christophe Dupuy, Francis R. Bach:
Learning Determinantal Point Processes in Sublinear Time. CoRR abs/1610.05925 (2016) - 2015
- [j41]Julien Mairal, Michael Elad, Francis R. Bach:
Guest Editorial: Sparse Coding. Int. J. Comput. Vis. 114(2-3): 89-90 (2015) - [j40]Francis R. Bach:
Duality Between Subgradient and Conditional Gradient Methods. SIAM J. Optim. 25(1): 115-129 (2015) - [j39]Fajwel Fogel, Rodolphe Jenatton, Francis R. Bach, Alexandre d'Aspremont:
Convex Relaxations for Permutation Problems. SIAM J. Matrix Anal. Appl. 36(4): 1465-1488 (2015) - [j38]Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach, Martin Kleinsteuber, Matthias Seibert:
Sample Complexity of Dictionary Learning and Other Matrix Factorizations. IEEE Trans. Inf. Theory 61(6): 3469-3486 (2015) - [j37]Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach:
Sparse and Spurious: Dictionary Learning With Noise and Outliers. IEEE Trans. Inf. Theory 61(11): 6298-6319 (2015) - [j36]Nino Shervashidze, Francis R. Bach:
Learning the Structure for Structured Sparsity. IEEE Trans. Signal Process. 63(18): 4894-4902 (2015) - [c90]Alexandre Défossez, Francis R. Bach:
Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions. AISTATS 2015 - [c89]Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach:
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. AISTATS 2015 - [c88]Nicolas Flammarion, Francis R. Bach:
From Averaging to Acceleration, There is Only a Step-size. COLT 2015: 658-695 - [c87]Alberto Bietti, Francis R. Bach, Arshia Cont:
An online EM algorithm in hidden (semi-)Markov models for audio segmentation and clustering. ICASSP 2015: 1881-1885 - [c86]Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis R. Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid:
Weakly-Supervised Alignment of Video with Text. ICCV 2015: 4462-4470 - [c85]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Rethinking LDA: Moment Matching for Discrete ICA. NIPS 2015: 514-522 - [c84]Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis R. Bach:
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction. NIPS 2015: 2215-2223 - [e1]Francis R. Bach, David M. Blei:
Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015. JMLR Workshop and Conference Proceedings 37, JMLR.org 2015 [contents] - [i68]Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach:
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. CoRR abs/1501.02056 (2015) - [i67]Francis R. Bach:
On the Equivalence between Quadrature Rules and Random Features. CoRR abs/1502.06800 (2015) - [i66]K. S. Sesh Kumar, Álvaro Barbero Jiménez, Stefanie Jegelka, Suvrit Sra, Francis R. Bach:
Convex Optimization for Parallel Energy Minimization. CoRR abs/1503.01563 (2015) - [i65]Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis R. Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid:
Weakly-Supervised Alignment of Video With Text. CoRR abs/1505.06027 (2015) - [i64]Rémi Lajugie, Piotr Bojanowski, Sylvain Arlot, Francis R. Bach:
Semidefinite and Spectral Relaxations for Multi-Label Classification. CoRR abs/1506.01829 (2015) - [i63]Vincent Roulet, Fajwel Fogel, Alexandre d'Aspremont, Francis R. Bach:
Supervised Clustering in the Data Cube. CoRR abs/1506.04908 (2015) - [i62]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Rethinking LDA: moment matching for discrete ICA. CoRR abs/1507.01784 (2015) - [i61]Francis R. Bach:
Submodular Functions: from Discrete to Continous Domains. CoRR abs/1511.00394 (2015) - 2014
- [j35]Toby Dylan Hocking, Valentina Boeva, Guillem Rigaill, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey, Olivier Delattre, Wilfrid Richer, Franck Bourdeaut, Miyuki Suguro, Masao Seto, Francis R. Bach, Jean-Philippe Vert:
SegAnnDB: interactive Web-based genomic segmentation. Bioinform. 30(11): 1539-1546 (2014) - [j34]Julien Mairal, Francis R. Bach, Jean Ponce:
Sparse Modeling for Image and Vision Processing. Found. Trends Comput. Graph. Vis. 8(2-3): 85-283 (2014) - [j33]Francis R. Bach:
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression. J. Mach. Learn. Res. 15(1): 595-627 (2014) - [j32]Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui:
Approximation bounds for sparse principal component analysis. Math. Program. 148(1-2): 89-110 (2014) - [j31]Georgios B. Giannakis, Francis R. Bach, Raphael Cendrillon, Michael W. Mahoney, Jennifer Neville:
Signal Processing for Big Data [From the Guest Editors]. IEEE Signal Process. Mag. 31(5): 15-16 (2014) - [c83]Edouard Grave, Guillaume Obozinski, Francis R. Bach:
A Markovian approach to distributional semantics with application to semantic compositionality. COLING 2014: 1447-1456 - [c82]Piotr Bojanowski, Rémi Lajugie, Francis R. Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, Josef Sivic:
Weakly Supervised Action Labeling in Videos under Ordering Constraints. ECCV (5) 2014: 628-643 - [c81]Rémi Lajugie, Francis R. Bach, Sylvain Arlot:
Large-Margin Metric Learning for Constrained Partitioning Problems. ICML 2014: 297-305 - [c80]Thomas Schatz, Vijayaditya Peddinti, Xuan-Nga Cao, Francis R. Bach, Hynek Hermansky, Emmanuel Dupoux:
Evaluating speech features with the minimal-pair ABX task (II): resistance to noise. INTERSPEECH 2014: 915-919 - [c79]Aaron Defazio, Francis R. Bach, Simon Lacoste-Julien:
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. NIPS 2014: 1646-1654 - [c78]Rémi Lajugie, Damien Garreau, Francis R. Bach, Sylvain Arlot:
Metric Learning for Temporal Sequence Alignment. NIPS 2014: 1817-1825 - [c77]Matthias Seibert, Martin Kleinsteuber, Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach:
On the sample complexity of sparse dictionary learning. SSP 2014: 244-247 - [i60]Aaron Defazio, Francis R. Bach, Simon Lacoste-Julien:
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. CoRR abs/1407.0202 (2014) - [i59]Piotr Bojanowski, Rémi Lajugie, Francis R. Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, Josef Sivic:
Weakly Supervised Action Labeling in Videos Under Ordering Constraints. CoRR abs/1407.1208 (2014) - [i58]Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach:
Sparse and spurious: dictionary learning with noise and outliers. CoRR abs/1407.5155 (2014) - [i57]Fabian Pedregosa, Francis R. Bach, Alexandre Gramfort:
On the Consistency of Ordinal Regression Methods. CoRR abs/1408.2327 (2014) - [i56]Damien Garreau, Rémi Lajugie, Sylvain Arlot, Francis R. Bach:
Metric Learning for Temporal Sequence Alignment. CoRR abs/1409.3136 (2014) - [i55]Julien Mairal, Francis R. Bach, Jean Ponce:
Sparse Modeling for Image and Vision Processing. CoRR abs/1411.3230 (2014) - [i54]Alexandre Défossez, Francis R. Bach:
Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and Optimal Sampling Distributions. CoRR abs/1412.0156 (2014) - [i53]Felipe Yanez, Francis R. Bach:
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence. CoRR abs/1412.1788 (2014) - [i52]Francis R. Bach:
Breaking the Curse of Dimensionality with Convex Neural Networks. CoRR abs/1412.8690 (2014) - 2013
- [j30]Toby Dylan Hocking, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey, Valentina Boeva, Julie Cappo, Olivier Delattre, Francis R. Bach, Jean-Philippe Vert:
Learning smoothing models of copy number profiles using breakpoint annotations. BMC Bioinform. 14: 164 (2013) - [j29]Francis R. Bach:
Learning with Submodular Functions: A Convex Optimization Perspective. Found. Trends Mach. Learn. 6(2-3): 145-373 (2013) - [j28]Bamdev Mishra, Gilles Meyer, Francis R. Bach, Rodolphe Sepulchre:
Low-Rank Optimization with Trace Norm Penalty. SIAM J. Optim. 23(4): 2124-2149 (2013) - [j27]Zaïd Harchaoui, Francis R. Bach, Olivier Cappé, Eric Moulines:
Kernel-Based Methods for Hypothesis Testing: A Unified View. IEEE Signal Process. Mag. 30(4): 87-97 (2013) - [c76]Francis R. Bach:
Sharp analysis of low-rank kernel matrix approximations. COLT 2013: 185-209 - [c75]Edouard Grave, Guillaume Obozinski, Francis R. Bach:
Hidden Markov tree models for semantic class induction. CoNLL 2013: 94-103 - [c74]Anil Kumar Nelakanti, Cédric Archambeau, Julien Mairal, Francis R. Bach, Guillaume Bouchard:
Structured Penalties for Log-Linear Language Models. EMNLP 2013: 233-243 - [c73]Piotr Bojanowski, Francis R. Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, Josef Sivic:
Finding Actors and Actions in Movies. ICCV 2013: 2280-2287 - [c72]Toby Hocking, Guillem Rigaill, Jean-Philippe Vert, Francis R. Bach:
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression. ICML (3) 2013: 172-180 - [c71]K. S. Sesh Kumar, Francis R. Bach:
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs. ICML (1) 2013: 525-533 - [c70]Emile Richard, Francis R. Bach, Jean-Philippe Vert:
Intersecting singularities for multi-structured estimation. ICML (3) 2013: 1157-1165 - [c69]Thomas Schatz, Vijayaditya Peddinti, Francis R. Bach, Aren Jansen, Hynek Hermansky, Emmanuel Dupoux:
Evaluating speech features with the minimal-pair ABX task: analysis of the classical MFC/PLP pipeline. INTERSPEECH 2013: 1781-1785 - [c68]Francis R. Bach, Eric Moulines:
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n). NIPS 2013: 773-781 - [c67]Fajwel Fogel, Rodolphe Jenatton, Francis R. Bach, Alexandre d'Aspremont:
Convex Relaxations for Permutation Problems. NIPS 2013: 1016-1024 - [c66]Stefanie Jegelka, Francis R. Bach, Suvrit Sra:
Reflection methods for user-friendly submodular optimization. NIPS 2013: 1313-1321 - [c65]Nicolas Le Roux, Francis R. Bach:
Local Component Analysis. ICLR (Poster) 2013 - [i51]Francis R. Bach, Michael I. Jordan:
Tree-dependent Component Analysis. CoRR abs/1301.0554 (2013) - [i50]Rémi Lajugie, Sylvain Arlot, Francis R. Bach:
Large-Margin Metric Learning for Partitioning Problems. CoRR abs/1303.1280 (2013) - [i49]Francis R. Bach:
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression. CoRR abs/1303.6149 (2013) - [i48]Francis R. Bach, Eric Moulines:
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n). CoRR abs/1306.2119 (2013) - [i47]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Minimizing Finite Sums with the Stochastic Average Gradient. CoRR abs/1309.2388 (2013) - [i46]K. S. Sesh Kumar, Francis R. Bach:
Maximizing submodular functions using probabilistic graphical models. CoRR abs/1309.2593 (2013) - [i45]Francis R. Bach:
Convex relaxations of structured matrix factorizations. CoRR abs/1309.3117 (2013) - [i44]Stefanie Jegelka, Francis R. Bach, Suvrit Sra:
Reflection methods for user-friendly submodular optimization. CoRR abs/1311.4296 (2013) - [i43]Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach, Martin Kleinsteuber, Matthias Seibert:
Sample Complexity of Dictionary Learning and other Matrix Factorizations. CoRR abs/1312.3790 (2013) - [i42]Edouard Grave, Guillaume Obozinski, Francis R. Bach:
Domain adaptation for sequence labeling using hidden Markov models. CoRR abs/1312.4092 (2013) - 2012
- [j26]Francis R. Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski:
Optimization with Sparsity-Inducing Penalties. Found. Trends Mach. Learn. 4(1): 1-106 (2012) - [j25]Matthieu Solnon, Sylvain Arlot, Francis R. Bach:
Multi-task regression using minimal penalties. J. Mach. Learn. Res. 13: 2773-2812 (2012) - [j24]Julien Mairal, Francis R. Bach, Jean Ponce:
Task-Driven Dictionary Learning. IEEE Trans. Pattern Anal. Mach. Intell. 34(4): 791-804 (2012) - [j23]Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel, Guillaume Obozinski, Evelyn Eger, Francis R. Bach, Bertrand Thirion:
Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity. SIAM J. Imaging Sci. 5(3): 835-856 (2012) - [c64]Armand Joulin, Francis R. Bach, Jean Ponce:
Multi-class cosegmentation. CVPR 2012: 542-549 - [c63]Francis R. Bach, Simon Lacoste-Julien, Guillaume Obozinski:
On the Equivalence between Herding and Conditional Gradient Algorithms. ICML 2012 - [c62]Armand Joulin, Francis R. Bach:
A convex relaxation for weakly supervised classifiers. ICML 2012 - [c61]Francis R. Bach:
Structured Sparsity and Convex Optimization. ICPRAM (1) 2012 - [c60]Augustin Lefèvre, Francis R. Bach, Cédric Févotte:
Semi-supervised NMF with Time-frequency Annotations for Single-channel Source Separation. ISMIR 2012: 115-120 - [c59]Hachem Kadri, Alain Rakotomamonjy, Francis R. Bach, Philippe Preux:
Multiple Operator-valued Kernel Learning. NIPS 2012: 2438-2446 - [c58]Nicolas Le Roux, Mark Schmidt, Francis R. Bach:
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. NIPS 2012: 2672-2680 - [i41]Nicolas Le Roux, Mark Schmidt, Francis R. Bach:
A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets. CoRR abs/1202.6258 (2012) - [i40]Hachem Kadri, Alain Rakotomamonjy, Francis R. Bach, Philippe Preux:
Multiple Operator-valued Kernel Learning. CoRR abs/1203.1596 (2012) - [i39]Francis R. Bach, Simon Lacoste-Julien, Guillaume Obozinski:
On the Equivalence between Herding and Conditional Gradient Algorithms. CoRR abs/1203.4523 (2012) - [i38]Guillaume Obozinski, Francis R. Bach:
Convex Relaxation for Combinatorial Penalties. CoRR abs/1205.1240 (2012) - [i37]Francis R. Bach:
Sharp analysis of low-rank kernel matrix approximations. CoRR abs/1208.2015 (2012) - [i36]Rodolphe Jenatton, Rémi Gribonval, Francis R. Bach:
Local stability and robustness of sparse dictionary learning in the presence of noise. CoRR abs/1210.0685 (2012) - [i35]Francis R. Bach:
Duality between subgradient and conditional gradient methods. CoRR abs/1211.6302 (2012) - [i34]Simon Lacoste-Julien, Mark Schmidt, Francis R. Bach:
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method. CoRR abs/1212.2002 (2012) - [i33]K. S. Sesh Kumar, Francis R. Bach:
Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs. CoRR abs/1212.2573 (2012) - 2011
- [j22]Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis R. Bach:
Proximal Methods for Hierarchical Sparse Coding. J. Mach. Learn. Res. 12: 2297-2334 (2011) - [j21]Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach:
Convex and Network Flow Optimization for Structured Sparsity. J. Mach. Learn. Res. 12: 2681-2720 (2011) - [j20]Rodolphe Jenatton, Jean-Yves Audibert, Francis R. Bach:
Structured Variable Selection with Sparsity-Inducing Norms. J. Mach. Learn. Res. 12: 2777-2824 (2011) - [j19]Alexandre d'Aspremont, Francis R. Bach, Inderjit S. Dhillon, Bin Yu:
Preface. Math. Program. 127(1): 1-2 (2011) - [j18]Olivier Duchenne, Francis R. Bach, In-So Kweon, Jean Ponce:
A Tensor-Based Algorithm for High-Order Graph Matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(12): 2383-2395 (2011) - [c57]Louise Benoît, Julien Mairal, Francis R. Bach, Jean Ponce:
Sparse image representation with epitomes. CVPR 2011: 2913-2920 - [c56]Augustin Lefèvre, Francis R. Bach, Cédric Févotte:
Itakura-Saito nonnegative matrix factorization with group sparsity. ICASSP 2011: 21-24 - [c55]Y-Lan Boureau, Nicolas Le Roux, Francis R. Bach, Jean Ponce, Yann LeCun:
Ask the locals: Multi-way local pooling for image recognition. ICCV 2011: 2651-2658 - [c54]Toby Hocking, Jean-Philippe Vert, Francis R. Bach, Armand Joulin:
Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties. ICML 2011: 745-752 - [c53]Francis R. Bach:
Shaping Level Sets with Submodular Functions. NIPS 2011: 10-18 - [c52]Francis R. Bach, Eric Moulines:
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning. NIPS 2011: 451-459 - [c51]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. NIPS 2011: 1458-1466 - [c50]Edouard Grave, Guillaume Obozinski, Francis R. Bach:
Trace Lasso: a trace norm regularization for correlated designs. NIPS 2011: 2187-2195 - [c49]Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel, Guillaume Obozinski, Francis R. Bach, Bertrand Thirion:
Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity. PRNI 2011: 69-72 - [c48]Augustin Lefèvre, Francis R. Bach, Cédric Févotte:
Online algorithms for nonnegative matrix factorization with the Itakura-Saito divergence. WASPAA 2011: 313-316 - [i32]Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach:
Convex and Network Flow Optimization for Structured Sparsity. CoRR abs/1104.1872 (2011) - [i31]Francis R. Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski:
Optimization with Sparsity-Inducing Penalties. CoRR abs/1108.0775 (2011) - [i30]Edouard Grave, Guillaume Obozinski, Francis R. Bach:
Trace Lasso: a trace norm regularization for correlated designs. CoRR abs/1109.1990 (2011) - [i29]Francis R. Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski:
Structured sparsity through convex optimization. CoRR abs/1109.2397 (2011) - [i28]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. CoRR abs/1109.2415 (2011) - [i27]Florent Couzinie-Devy, Julien Mairal, Francis R. Bach, Jean Ponce:
Dictionary Learning for Deblurring and Digital Zoom. CoRR abs/1110.0957 (2011) - [i26]Louise Benoît, Julien Mairal, Francis R. Bach, Jean Ponce:
Sparse Image Representation with Epitomes. CoRR abs/1110.2855 (2011) - [i25]Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach:
Learning Hierarchical and Topographic Dictionaries with Structured Sparsity. CoRR abs/1110.4481 (2011) - [i24]Francis R. Bach:
Learning with Submodular Functions: A Convex Optimization Perspective. CoRR abs/1111.6453 (2011) - [i23]Bamdev Mishra, Gilles Meyer, Francis R. Bach, Rodolphe Sepulchre:
Low-rank optimization with trace norm penalty. CoRR abs/1112.2318 (2011) - 2010
- [j17]Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro:
Online Learning for Matrix Factorization and Sparse Coding. J. Mach. Learn. Res. 11: 19-60 (2010) - [j16]Michel Journée, Francis R. Bach, Pierre-Antoine Absil, Rodolphe Sepulchre:
Low-Rank Optimization on the Cone of Positive Semidefinite Matrices. SIAM J. Optim. 20(5): 2327-2351 (2010) - [c47]Armand Joulin, Francis R. Bach, Jean Ponce:
Discriminative clustering for image co-segmentation. CVPR 2010: 1943-1950 - [c46]Y-Lan Boureau, Francis R. Bach, Yann LeCun, Jean Ponce:
Learning mid-level features for recognition. CVPR 2010: 2559-2566 - [c45]Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis R. Bach:
Proximal Methods for Sparse Hierarchical Dictionary Learning. ICML 2010: 487-494 - [c44]Francis R. Bach:
Structured sparsity-inducing norms through submodular functions. NIPS 2010: 118-126 - [c43]Matthew D. Hoffman, David M. Blei, Francis R. Bach:
Online Learning for Latent Dirichlet Allocation. NIPS 2010: 856-864 - [c42]Armand Joulin, Francis R. Bach, Jean Ponce:
Efficient Optimization for Discriminative Latent Class Models. NIPS 2010: 1045-1053 - [c41]Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach:
Network Flow Algorithms for Structured Sparsity. NIPS 2010: 1558-1566 - [c40]Mikhail Zaslavskiy, Francis R. Bach, Jean-Philippe Vert:
Many-to-Many Graph Matching: A Continuous Relaxation Approach. ECML/PKDD (3) 2010: 515-530 - [c39]Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach:
Structured Sparse Principal Component Analysis. AISTATS 2010: 366-373 - [i22]Mikhail Zaslavskiy, Francis R. Bach, Jean-Philippe Vert:
Many-to-Many Graph Matching: a Continuous Relaxation Approach. CoRR abs/1004.4965 (2010) - [i21]Francis R. Bach, Selin Damla Ahipasaoglu, Alexandre d'Aspremont:
Convex Relaxations for Subset Selection. CoRR abs/1006.3601 (2010) - [i20]Francis R. Bach:
Structured sparsity-inducing norms through submodular functions. CoRR abs/1008.4220 (2010) - [i19]Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach:
Network Flow Algorithms for Structured Sparsity. CoRR abs/1008.5209 (2010) - [i18]Francis R. Bach:
Convex Analysis and Optimization with Submodular Functions: a Tutorial. CoRR abs/1010.4207 (2010) - [i17]Francis R. Bach:
Shaping Level Sets with Submodular Functions. CoRR abs/1012.1501 (2010)
2000 – 2009
- 2009
- [j15]Mikhail Zaslavskiy, Francis R. Bach, Jean-Philippe Vert:
Global alignment of protein-protein interaction networks by graph matching methods. Bioinform. 25(12) (2009) - [j14]Jacob D. Abernethy, Francis R. Bach, Theodoros Evgeniou, Jean-Philippe Vert:
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. J. Mach. Learn. Res. 10: 803-826 (2009) - [j13]Mikhail Zaslavskiy, Francis R. Bach, Jean-Philippe Vert:
A Path Following Algorithm for the Graph Matching Problem. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2227-2242 (2009) - [c38]Olivier Duchenne, Francis R. Bach, In-So Kweon, Jean Ponce:
A tensor-based algorithm for high-order graph matching. CVPR 2009: 1980-1987 - [c37]Olivier Duchenne, Ivan Laptev, Josef Sivic, Francis R. Bach, Jean Ponce:
Automatic annotation of human actions in video. ICCV 2009: 1491-1498 - [c36]Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman:
Non-local sparse models for image restoration. ICCV 2009: 2272-2279 - [c35]Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro:
Online dictionary learning for sparse coding. ICML 2009: 689-696 - [c34]Sylvain Arlot, Francis R. Bach:
Data-driven calibration of linear estimators with minimal penalties. NIPS 2009: 46-54 - [c33]Percy Liang, Francis R. Bach, Guillaume Bouchard, Michael I. Jordan:
Asymptotically Optimal Regularization in Smooth Parametric Models. NIPS 2009: 1132-1140 - [i16]Francis R. Bach:
Model-Consistent Sparse Estimation through the Bootstrap. CoRR abs/0901.3202 (2009) - [i15]Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro:
Online Learning for Matrix Factorization and Sparse Coding. CoRR abs/0908.0050 (2009) - [i14]Francis R. Bach:
High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel Learning. CoRR abs/0909.0844 (2009) - [i13]Francis R. Bach:
Self-concordant analysis for logistic regression. CoRR abs/0910.4627 (2009) - 2008
- [j12]Francis R. Bach:
Consistency of Trace Norm Minimization. J. Mach. Learn. Res. 9: 1019-1048 (2008) - [j11]Francis R. Bach:
Consistency of the Group Lasso and Multiple Kernel Learning. J. Mach. Learn. Res. 9: 1179-1225 (2008) - [j10]Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui:
Optimal Solutions for Sparse Principal Component Analysis. J. Mach. Learn. Res. 9: 1269-1294 (2008) - [c32]Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman:
Discriminative learned dictionaries for local image analysis. CVPR 2008 - [c31]Julien Mairal, Marius Leordeanu, Francis R. Bach, Martial Hebert, Jean Ponce:
Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation. ECCV (3) 2008: 43-56 - [c30]Mikhail Zaslavskiy, Francis R. Bach, Jean-Philippe Vert:
A Path Following Algorithm for Graph Matching. ICISP 2008: 329-337 - [c29]Francis R. Bach:
Graph kernels between point clouds. ICML 2008: 25-32 - [c28]Francis R. Bach:
Bolasso: model consistent Lasso estimation through the bootstrap. ICML 2008: 33-40 - [c27]Cédric Archambeau, Francis R. Bach:
Sparse probabilistic projections. NIPS 2008: 73-80 - [c26]Francis R. Bach:
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning. NIPS 2008: 105-112 - [c25]Zaïd Harchaoui, Francis R. Bach, Eric Moulines:
Kernel Change-point Analysis. NIPS 2008: 609-616 - [c24]Laurent Jacob, Francis R. Bach, Jean-Philippe Vert:
Clustered Multi-Task Learning: A Convex Formulation. NIPS 2008: 745-752 - [c23]Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman:
Supervised Dictionary Learning. NIPS 2008: 1033-1040 - [i12]Mikhail Zaslavskiy, Francis R. Bach, Jean-Philippe Vert:
Path following algorithm for the graph matching problem. CoRR abs/0801.3654 (2008) - [i11]Francis R. Bach, Jacob D. Abernethy, Jean-Philippe Vert, Theodoros Evgeniou:
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. CoRR abs/0802.1430 (2008) - [i10]Francis R. Bach:
Bolasso: model consistent Lasso estimation through the bootstrap. CoRR abs/0804.1302 (2008) - [i9]Francis R. Bach:
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning. CoRR abs/0809.1493 (2008) - [i8]Laurent Jacob, Francis R. Bach, Jean-Philippe Vert:
Clustered Multi-Task Learning: A Convex Formulation. CoRR abs/0809.2085 (2008) - [i7]Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman:
Supervised Dictionary Learning. CoRR abs/0809.3083 (2008) - [i6]Francis R. Bach, Julien Mairal, Jean Ponce:
Convex Sparse Matrix Factorizations. CoRR abs/0812.1869 (2008) - 2007
- [j9]Yoshihiro Yamanishi, Francis R. Bach, Jean-Philippe Vert:
Glycan classification with tree kernels. Bioinform. 23(10): 1211-1216 (2007) - [j8]Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Consistency of Kernel Canonical Correlation Analysis. J. Mach. Learn. Res. 8: 361-383 (2007) - [j7]Jérôme Louradour, Khalid Daoudi, Francis R. Bach:
Feature Space Mahalanobis Sequence Kernels: Application to SVM Speaker Verification. IEEE Trans. Speech Audio Process. 15(8): 2465-2475 (2007) - [c22]Zaïd Harchaoui, Francis R. Bach:
Image Classification with Segmentation Graph Kernels. CVPR 2007 - [c21]Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui:
Full regularization path for sparse principal component analysis. ICML 2007: 177-184 - [c20]Alain Rakotomamonjy, Francis R. Bach, Stéphane Canu, Yves Grandvalet:
More efficiency in multiple kernel learning. ICML 2007: 775-782 - [c19]Aurélien Cord, Dominique Jeulin, Francis R. Bach:
Segmentation of random textures by morphological and linear operators. ISMM (1) 2007: 387-398 - [c18]Francis R. Bach, Zaïd Harchaoui:
DIFFRAC: a discriminative and flexible framework for clustering. NIPS 2007: 49-56 - [c17]Zaïd Harchaoui, Francis R. Bach, Eric Moulines:
Testing for Homogeneity with Kernel Fisher Discriminant Analysis. NIPS 2007: 609-616 - [i5]Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui:
Optimal Solutions for Sparse Principal Component Analysis. CoRR abs/0707.0705 (2007) - [i4]Francis R. Bach:
Consistency of the group Lasso and multiple kernel learning. CoRR abs/0707.3390 (2007) - [i3]Francis R. Bach:
Consistency of trace norm minimization. CoRR abs/0710.2848 (2007) - [i2]Francis R. Bach:
Graph kernels between point clouds. CoRR abs/0712.3402 (2007) - 2006
- [j6]Francis R. Bach, David Heckerman, Eric Horvitz:
Considering Cost Asymmetry in Learning Classifiers. J. Mach. Learn. Res. 7: 1713-1741 (2006) - [j5]Francis R. Bach, Michael I. Jordan:
Learning Spectral Clustering, With Application To Speech Separation. J. Mach. Learn. Res. 7: 1963-2001 (2006) - [c16]Francis R. Bach:
Active learning for misspecified generalized linear models. NIPS 2006: 65-72 - [c15]Jérôme Louradour, Khalid Daoudi, Francis R. Bach:
SVM Speaker Verification using an Incomplete Cholesky Decomposition Sequence Kernel. Odyssey 2006: 1-5 - [i1]Jacob D. Abernethy, Francis R. Bach, Theodoros Evgeniou, Jean-Philippe Vert:
Low-rank matrix factorization with attributes. CoRR abs/cs/0611124 (2006) - 2005
- [c14]Francis R. Bach, David Heckerman, Eric Horvitz:
On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers. AISTATS 2005: 9-16 - [c13]Michael I. Jordan, Francis R. Bach:
Modèles de Markov cachés pour l'estimation de plusieurs fréquences fondamentales. EGC (Ateliers) 2005: 49-52 - [c12]Francis R. Bach, Michael I. Jordan:
Discriminative training of hidden Markov models for multiple pitch tracking [speech processing examples]. ICASSP (5) 2005: 489-492 - [c11]Francis R. Bach, Michael I. Jordan:
Predictive low-rank decomposition for kernel methods. ICML 2005: 33-40 - [c10]Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Convergence of Kernel CCA. NIPS 2005: 387-394 - 2004
- [j4]Kenji Fukumizu, Francis R. Bach, Michael I. Jordan:
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. J. Mach. Learn. Res. 5: 73-99 (2004) - [j3]Francis R. Bach, Michael I. Jordan:
Learning graphical models for stationary time series. IEEE Trans. Signal Process. 52(8): 2189-2199 (2004) - [c9]Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan:
Multiple kernel learning, conic duality, and the SMO algorithm. ICML 2004 - [c8]Francis R. Bach, Michael I. Jordan:
Blind One-microphone Speech Separation: A Spectral Learning Approach. NIPS 2004: 65-72 - [c7]Francis R. Bach, Romain Thibaux, Michael I. Jordan:
Computing regularization paths for learning multiple kernels. NIPS 2004: 73-80 - 2003
- [j2]Francis R. Bach, Michael I. Jordan:
Beyond Independent Components: Trees and Clusters. J. Mach. Learn. Res. 4: 1205-1233 (2003) - [c6]Francis R. Bach, Michael I. Jordan:
Kernel independent component analysis. ICASSP (4) 2003: 876-879 - [c5]Kenji Fukumizu, Francis R. Bach, Michael I. Jordan:
Kernel Dimensionality Reduction for Supervised Learning. NIPS 2003: 81-88 - [c4]Francis R. Bach, Michael I. Jordan:
Learning Spectral Clustering. NIPS 2003: 305-312 - 2002
- [j1]Francis R. Bach, Michael I. Jordan:
Kernel Independent Component Analysis. J. Mach. Learn. Res. 3: 1-48 (2002) - [c3]Francis R. Bach, Michael I. Jordan:
Learning Graphical Models with Mercer Kernels. NIPS 2002: 1009-1016 - [c2]Francis R. Bach, Michael I. Jordan:
Tree-dependent Component Analysis. UAI 2002: 36-44 - 2001
- [c1]Francis R. Bach, Michael I. Jordan:
Thin Junction Trees. NIPS 2001: 569-576
Coauthor Index
Vivien Cabannes
aka: Vivien A. Cabannes
aka: Vivien A. Cabannes
[j74] [j72] [j67] [c185] [c184] [c183] [c181] [c177] [i167] [i162] [c171] [c170] [c167] [c164] [i155] [i154] [i152] [i149] [i146] [i144] [i143] [c163] [c160] [c158] [c152] [i136] [i128] [i126] [i125] [i122] [c147] [c144] [c136] [c133] [c132] [i117] [i116] [i108] [c124] [c115] [i96] [i94] [i92] [i89] [i87] [i79]
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
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last updated on 2024-11-07 20:33 CET by the dblp team
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