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ORCIDFrancis R. Bach
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- affiliation: École Normale Supérieure, Computer Science Department
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2020 – today
- 2021
[i145]Vivien Cabannes, Alessandro Rudi, Francis R. Bach:
Fast rates in structured prediction. CoRR abs/2102.00760 (2021)- [i144]Vivien Cabannes, Francis R. Bach, Alessandro Rudi:
Disambiguation of weak supervision with exponential convergence rates. CoRR abs/2102.02789 (2021) - [i143]Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Adrien Taylor:
A Continuized View on Nesterov Acceleration. CoRR abs/2102.06035 (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) - [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]Theo 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 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 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 Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed:
Massively scalable Sinkhorn distances via the Nyström method. NeurIPS 2019: 4429-4439 - [c135]Theo 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 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]Theo 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 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 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]