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2020 – today
- 2020
[c57]Fabian Pedregosa, Geoffrey Négiar, Armin Askari, Martin Jaggi:
Linearly Convergent Frank-Wolfe without Line-Search. AISTATS 2020: 1-10- [c56]Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations. AISTATS 2020: 3437-3449 - [c55]Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, Hinrich Schütze:
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models. EMNLP (1) 2020: 2226-2241 - [c54]Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi:
On the Relationship between Self-Attention and Convolutional Layers. ICLR 2020 - [c53]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. ICLR 2020 - [c52]Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi:
Dynamic Model Pruning with Feedback. ICLR 2020 - [c51]Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi:
Don't Use Large Mini-batches, Use Local SGD. ICLR 2020 - [c50]Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, Mathieu Salzmann:
Evaluating The Search Phase of Neural Architecture Search. ICLR 2020 - [c49]Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. ICML 2020: 5381-5393 - [c48]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. ICML 2020: 6094-6104 - [c47]Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. ICML 2020: 9036-9045 - [c46]Felix Grimberg
, Mary-Anne Hartley, Martin Jaggi, Sai Praneeth Karimireddy:
Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning. DART/DCL@MICCAI 2020: 160-169 - [c45]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. NeurIPS 2020 - [c44]Sidak Pal Singh, Martin Jaggi:
Model Fusion via Optimal Transport. NeurIPS 2020 - [c43]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
Practical Low-Rank Communication Compression in Decentralized Deep Learning. NeurIPS 2020 - [i70]Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR abs/2003.10422 (2020) - [i69]Namhoon Lee, Philip H. S. Torr, Martin Jaggi:
Data Parallelism in Training Sparse Neural Networks. CoRR abs/2003.11316 (2020) - [i68]Mengjie Zhao, Tao Lin, Martin Jaggi, Hinrich Schütze:
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models. CoRR abs/2004.12406 (2020) - [i67]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Secure Byzantine-Robust Machine Learning. CoRR abs/2006.04747 (2020) - [i66]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. CoRR abs/2006.05720 (2020) - [i65]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. CoRR abs/2006.07242 (2020) - [i64]Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi:
Dynamic Model Pruning with Feedback. CoRR abs/2006.07253 (2020) - [i63]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. CoRR abs/2006.09365 (2020) - [i62]Tatjana Chavdarova, Matteo Pagliardini, Martin Jaggi, François Fleuret:
Taming GANs with Lookahead. CoRR abs/2006.14567 (2020) - [i61]Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi:
Multi-Head Attention: Collaborate Instead of Concatenate. CoRR abs/2006.16362 (2020) - [i60]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning. CoRR abs/2008.01425 (2020) - [i59]Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR abs/2008.03606 (2020) - [i58]Negar Foroutan Eghlidi, Martin Jaggi:
Sparse Communication for Training Deep Networks. CoRR abs/2009.09271 (2020) - [i57]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! CoRR abs/2011.01697 (2020) - [i56]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Learning from History for Byzantine Robust Optimization. CoRR abs/2012.10333 (2020)
2010 – 2019
- 2019
- [j7]Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi:
Unsupervised robust nonparametric learning of hidden community properties. Math. Found. Comput. 2(2): 127-147 (2019) - [c42]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. AISTATS 2019: 2887-2896 - [c41]Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel:
On Linear Learning with Manycore Processors. HiPC 2019: 184-194 - [c40]Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations. DGS@ICLR 2019 - [c39]Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat:
Overcoming Multi-model Forgetting. ICML 2019: 594-603 - [c38]Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. ICML 2019: 3252-3261 - [c37]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. ICML 2019: 3478-3487 - [c36]Niccolò Sacchi, Alexandre Nanchen, Martin Jaggi, Milos Cernak:
Open-Vocabulary Keyword Spotting with Audio and Text Embeddings. INTERSPEECH 2019: 3362-3366 - [c35]Prakhar Gupta, Matteo Pagliardini, Martin Jaggi:
Better Word Embeddings by Disentangling Contextual n-Gram Information. NAACL-HLT (1) 2019: 933-939 - [c34]Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi:
Unsupervised Scalable Representation Learning for Multivariate Time Series. NeurIPS 2019: 4652-4663 - [c33]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. NeurIPS 2019: 14236-14245 - [c32]Martin Josifoski, Ivan S. Paskov, Hristo S. Paskov, Martin Jaggi, Robert West:
Crosslingual Document Embedding as Reduced-Rank Ridge Regression. WSDM 2019: 744-752 - [i55]Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. CoRR abs/1901.09847 (2019) - [i54]Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi:
Unsupervised Scalable Representation Learning for Multivariate Time Series. CoRR abs/1901.10738 (2019) - [i53]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. CoRR abs/1902.00340 (2019) - [i52]Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, Mathieu Salzmann:
Evaluating the Search Phase of Neural Architecture Search. CoRR abs/1902.08142 (2019) - [i51]Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat:
Overcoming Multi-Model Forgetting. CoRR abs/1902.08232 (2019) - [i50]Matthias Hüser, Adrian Kündig, Walter Karlen, Valeria De Luca, Martin Jaggi:
Forecasting intracranial hypertension using multi-scale waveform metrics. CoRR abs/1902.09499 (2019) - [i49]Khalil Mrini, Claudiu Musat, Michael Baeriswyl, Martin Jaggi:
Structure Tree-LSTM: Structure-aware Attentional Document Encoders. CoRR abs/1902.09713 (2019) - [i48]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i47]Martin Josifoski, Ivan S. Paskov, Hristo S. Paskov, Martin Jaggi, Robert West:
Crosslingual Document Embedding as Reduced-Rank Ridge Regression. CoRR abs/1904.03922 (2019) - [i46]Prakhar Gupta, Matteo Pagliardini, Martin Jaggi:
Better Word Embeddings by Disentangling Contextual n-Gram Information. CoRR abs/1904.05033 (2019) - [i45]Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel:
On Linear Learning with Manycore Processors. CoRR abs/1905.00626 (2019) - [i44]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. CoRR abs/1905.13727 (2019) - [i43]Arno Schneuwly, Ralf Grubenmann, Séverine Rion Logean, Mark Cieliebak, Martin Jaggi:
Correlating Twitter Language with Community-Level Health Outcomes. CoRR abs/1906.06465 (2019) - [i42]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. CoRR abs/1907.09356 (2019) - [i41]Sidak Pal Singh, Martin Jaggi:
Model Fusion via Optimal Transport. CoRR abs/1910.05653 (2019) - [i40]Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
On the Tunability of Optimizers in Deep Learning. CoRR abs/1910.11758 (2019) - [i39]Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi:
On the Relationship between Self-Attention and Convolutional Layers. CoRR abs/1911.03584 (2019) - [i38]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - [i37]Ali Sabet, Prakhar Gupta, Jean-Baptiste Cordonnier, Robert West, Martin Jaggi:
Robust Cross-lingual Embeddings from Parallel Sentences. CoRR abs/1912.12481 (2019) - 2018
- [j6]Alexandre d'Aspremont, Cristóbal Guzmán, Martin Jaggi:
Optimal Affine-Invariant Smooth Minimization Algorithms. SIAM J. Optim. 28(3): 2384-2405 (2018) - [c31]Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi:
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. AISTATS 2018: 1204-1213 - [c30]Kamil Bennani-Smires, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl, Martin Jaggi:
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings. CoNLL 2018: 221-229 - [c29]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. ICML 2018: 1357-1365 - [c28]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
On Matching Pursuit and Coordinate Descent. ICML 2018: 3204-3213 - [c27]Matteo Pagliardini, Prakhar Gupta, Martin Jaggi:
Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features. NAACL-HLT 2018: 528-540 - [c26]Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi:
Training DNNs with Hybrid Block Floating Point. NeurIPS 2018: 451-461 - [c25]Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi:
Sparsified SGD with Memory. NeurIPS 2018: 4452-4463 - [c24]Lie He, An Bian, Martin Jaggi:
COLA: Decentralized Linear Learning. NeurIPS 2018: 4541-4551 - [i36]Kamil Bennani-Smires, Claudiu Musat, Martin Jaggi, Andreea Hossmann, Michael Baeriswyl:
EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings. CoRR abs/1801.04470 (2018) - [i35]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR abs/1803.09539 (2018) - [i34]Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi:
End-to-End DNN Training with Block Floating Point Arithmetic. CoRR abs/1804.01526 (2018) - [i33]Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients. CoRR abs/1806.00413 (2018) - [i32]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. CoRR abs/1806.07569 (2018) - [i31]Lie He, An Bian, Martin Jaggi:
COLA: Communication-Efficient Decentralized Linear Learning. CoRR abs/1808.04883 (2018) - [i30]Tao Lin, Sebastian U. Stich, Martin Jaggi:
Don't Use Large Mini-Batches, Use Local SGD. CoRR abs/1808.07217 (2018) - [i29]Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Wasserstein is all you need. CoRR abs/1808.09663 (2018) - [i28]Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi:
Sparsified SGD with Memory. CoRR abs/1809.07599 (2018) - [i27]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. CoRR abs/1810.06999 (2018) - 2017
- [j5]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. 18: 230:1-230:49 (2017) - [j4]Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác:
Distributed optimization with arbitrary local solvers. Optim. Methods Softw. 32(4): 813-848 (2017) - [j3]Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler:
Learning Aerial Image Segmentation From Online Maps. IEEE Trans. Geosci. Remote. Sens. 55(11): 6054-6068 (2017) - [c23]Tina Fang, Martin Jaggi, Katerina J. Argyraki:
Generating Steganographic Text with LSTMs. ACL (Student Research Workshop) 2017: 100-106 - [c22]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. AISTATS 2017: 860-868 - [c21]Dmytro Perekrestenko, Volkan Cevher, Martin Jaggi:
Faster Coordinate Descent via Adaptive Importance Sampling. AISTATS 2017: 869-877 - [c20]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Approximate Steepest Coordinate Descent. ICML 2017: 3251-3259 - [c19]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. NIPS 2017: 773-784 - [c18]Celestine Dünner, Thomas P. Parnell, Martin Jaggi:
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems. NIPS 2017: 4258-4267 - [c17]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Safe Adaptive Importance Sampling. NIPS 2017: 4381-4391 - [c16]Jan Deriu, Aurélien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi:
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. WWW 2017: 1045-1052 - [i26]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. CoRR abs/1702.06457 (2017) - [i25]Jan Deriu, Aurélien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi:
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. CoRR abs/1703.02504 (2017) - [i24]Matteo Pagliardini, Prakhar Gupta, Martin Jaggi:
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features. CoRR abs/1703.02507 (2017) - [i23]Dmytro Perekrestenko, Volkan Cevher, Martin Jaggi:
Faster Coordinate Descent via Adaptive Importance Sampling. CoRR abs/1703.02518 (2017) - [i22]Tina Fang, Martin Jaggi, Katerina J. Argyraki:
Generating Steganographic Text with LSTMs. CoRR abs/1705.10742 (2017) - [i21]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. CoRR abs/1705.11041 (2017) - [i20]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Approximate Steepest Coordinate Descent. CoRR abs/1706.08427 (2017) - [i19]Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi, Suvrit Sra:
Unsupervised robust nonparametric learning of hidden community properties. CoRR abs/1707.03494 (2017) - [i18]Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler:
Learning Aerial Image Segmentation from Online Maps. CoRR abs/1707.06879 (2017) - [i17]Celestine Dünner, Thomas P. Parnell, Martin Jaggi:
Efficient Use of Limited-Memory Resources to Accelerate Linear Learning. CoRR abs/1708.05357 (2017) - [i16]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Safe Adaptive Importance Sampling. CoRR abs/1711.02637 (2017) - [i15]Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác:
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. CoRR abs/1711.05305 (2017) - 2016
- [c15]Elias Sprengel, Martin Jaggi, Yannic Kilcher, Thomas Hofmann:
Audio Based Bird Species Identification using Deep Learning Techniques. CLEF (Working Notes) 2016: 547-559 - [c14]Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi:
Primal-Dual Rates and Certificates. ICML 2016: 783-792 - [c13]Jan Deriu, Maurice Gonzenbach, Fatih Uzdilli, Aurélien Lucchi, Valeria De Luca, Martin Jaggi:
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision. SemEval@NAACL-HLT 2016: 1124-1128 - [i14]Rajiv Khanna, Michael Tschannen, Martin Jaggi:
Pursuits in Structured Non-Convex Matrix Factorizations. CoRR abs/1602.04208 (2016) - [i13]Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi:
Primal-Dual Rates and Certificates. CoRR abs/1602.05205 (2016) - [i12]Anant Raj, Jakob Olbrich, Bernd Gärtner, Bernhard Schölkopf, Martin Jaggi:
Screening Rules for Convex Problems. CoRR abs/1609.07478 (2016) - [i11]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR abs/1611.02189 (2016) - 2015
- [c12]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML 2015: 1973-1982 - [c11]Simon Lacoste-Julien, Martin Jaggi:
On the Global Linear Convergence of Frank-Wolfe Optimization Variants. NIPS 2015: 496-504 - [c10]Fatih Uzdilli, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, Mark Cieliebak:
Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment. SemEval@NAACL-HLT 2015: 608-612 - [i10]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR abs/1502.03508 (2015) - [i9]Simon Lacoste-Julien, Martin Jaggi:
On the Global Linear Convergence of Frank-Wolfe Optimization Variants. CoRR abs/1511.05932 (2015) - [i8]Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi:
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. CoRR abs/1512.04011 (2015) - [i7]Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác:
Distributed Optimization with Arbitrary Local Solvers. CoRR abs/1512.04039 (2015) - 2014
- [c9]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. NIPS 2014: 3068-3076 - [c8]Martin Jaggi, Fatih Uzdilli, Mark Cieliebak:
Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams. SemEval@COLING 2014: 601-604 - [i6]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. CoRR abs/1409.1458 (2014) - 2013
- [c7]Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. ICML (1) 2013: 53-61 - [c6]Martin Jaggi:
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization. ICML (1) 2013: 427-435 - [i5]Martin Jaggi:
An Equivalence between the Lasso and Support Vector Machines. CoRR abs/1303.1152 (2013) - 2012
- [j2]Bernd Gärtner, Martin Jaggi, Clément Maria:
An Exponential Lower Bound on the Complexity of Regularization Paths. J. Comput. Geom. 3(1): 168-195 (2012) - [j1]Joachim Giesen, Martin Jaggi