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ORCIDMartin Jaggi
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- affiliation: EPFL, School of Computer and Communication Sciences, Lausanne, Switzerland
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2020 – today
- 2024
[j13]Klavdiia Naumova
, Arnout Devos, Sai Praneeth Karimireddy, Martin Jaggi, Mary-Anne Hartley:
MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images. npj Digit. Medicine 7(1) (2024)- [c101]Atli Kosson, Dongyang Fan, Martin Jaggi:
Ghost Noise for Regularizing Deep Neural Networks. AAAI 2024: 13274-13282 - [c100]Linara Adilova, Maksym Andriushchenko, Michael Kamp, Asja Fischer, Martin Jaggi:
Layer-wise linear mode connectivity. ICLR 2024 - [c99]Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui:
The Privacy Power of Correlated Noise in Decentralized Learning. ICML 2024 - [c98]Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions. ICML 2024 - [c97]Simin Fan, Matteo Pagliardini, Martin Jaggi:
DOGE: Domain Reweighting with Generalization Estimation. ICML 2024 - [c96]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
On Convergence of Incremental Gradient for Non-convex Smooth Functions. ICML 2024 - [c95]Atli Kosson, Bettina Messmer, Martin Jaggi:
Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks. ICML 2024 - [c94]Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael Gastpar:
LASER: Linear Compression in Wireless Distributed Optimization. ICML 2024 - [i139]Matteo Pagliardini, Amirkeivan Mohtashami, François Fleuret, Martin Jaggi:
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging. CoRR abs/2402.02622 (2024) - [i138]Vinitra Swamy, Julian Blackwell, Jibril Frej, Martin Jaggi, Tanja Käser:
InterpretCC: Conditional Computation for Inherently Interpretable Neural Networks. CoRR abs/2402.02933 (2024) - [i137]Ashok Vardhan Makkuva, Marco Bondaschi, Adway Girish, Alliot Nagle, Martin Jaggi, Hyeji Kim, Michael Gastpar:
Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains. CoRR abs/2402.04161 (2024) - [i136]Dongyang Fan, Bettina Messmer, Martin Jaggi:
Towards an empirical understanding of MoE design choices. CoRR abs/2402.13089 (2024) - [i135]Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman:
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. CoRR abs/2404.00456 (2024) - [i134]Nicolas Wagner, Dongyang Fan, Martin Jaggi:
Personalized Collaborative Fine-Tuning for On-Device Large Language Models. CoRR abs/2404.09753 (2024) - [i133]Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui:
The Privacy Power of Correlated Noise in Decentralized Learning. CoRR abs/2405.01031 (2024) - [i132]Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Ben Allal, Leandro von Werra, Martin Jaggi:
Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations. CoRR abs/2405.18392 (2024) - [i131]Simin Fan, Razvan Pascanu, Martin Jaggi:
Deep Grokking: Would Deep Neural Networks Generalize Better? CoRR abs/2405.19454 (2024) - [i130]Simla Burcu Harma, Ayan Chakraborty, Elizaveta Kostenok, Danila Mishin, Dongho Ha, Babak Falsafi, Martin Jaggi, Ming Liu, Yunho Oh, Suvinay Subramanian, Amir Yazdanbakhsh:
Effective Interplay between Sparsity and Quantization: From Theory to Practice. CoRR abs/2405.20935 (2024) - [i129]Beatriz Borges, Negar Foroutan, Deniz Bayazit, Anna Sotnikova, Syrielle Montariol, Tanya Nazaretzky, Mohammadreza Banaei, Alireza Sakhaeirad, Philippe Servant, Seyed Parsa Neshaei, Jibril Frej, Angelika Romanou, Gail Weiss, Sepideh Mamooler, Zeming Chen, Simin Fan, Silin Gao, Mete Ismayilzada, Debjit Paul, Alexandre Schöpfer, Andrej Janchevski, Anja Tiede, Clarence Linden, Emanuele Troiani, Francesco Salvi, Freya Behrens, Giacomo Orsi, Giovanni Piccioli, Hadrien Sevel, Louis Coulon, Manuela Pineros-Rodriguez, Marin Bonnassies, Pierre Hellich, Puck van Gerwen, Sankalp Gambhir, Solal Pirelli, Thomas Blanchard, Timothée Callens, Toni Abi Aoun, Yannick Calvino Alonso, Yuri Cho, Alberto Silvio Chiappa, Antonio Sclocchi, Étienne Bruno, Florian Hofhammer, Gabriel Pescia, Geovani Rizk, Leello Dadi, Lucas Stoffl, Manoel Horta Ribeiro, Matthieu Bovel, Yueyang Pan, Aleksandra Radenovic, Alexandre Alahi, Alexander Mathis, Anne-Florence Bitbol, Boi Faltings, Cécile Hébert, Devis Tuia, François Maréchal, George Candea, Giuseppe Carleo, Jean-Cédric Chappelier, Nicolas Flammarion, Jean-Marie Fürbringer, Jean-Philippe Pellet, Karl Aberer, Lenka Zdeborová, Marcel Salathé, Martin Jaggi, Martin Rajman, Mathias Payer, Matthieu Wyart, Michael Gastpar, Michele Ceriotti, Ola Svensson, Olivier Lévêque, Paolo Ienne, Rachid Guerraoui, Robert West, Sanidhya Kashyap, Valerio Piazza, Viesturs Simanis, Viktor Kuncak, Volkan Cevher, Philippe Schwaller, Sacha Friedli, Patrick Jermann, Tanja Käser, Antoine Bosselut:
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. CoRR abs/2408.11841 (2024) - [i128]El Mahdi Chayti, Martin Jaggi:
A New First-Order Meta-Learning Algorithm with Convergence Guarantees. CoRR abs/2409.03682 (2024) - [i127]Diba Hashemi, Lie He, Martin Jaggi:
CoBo: Collaborative Learning via Bilevel Optimization. CoRR abs/2409.05539 (2024) - [i126]Dongyang Fan, Bettina Messmer, Martin Jaggi:
On-device Collaborative Language Modeling via a Mixture of Generalists and Specialists. CoRR abs/2409.13931 (2024) - [i125]Xinyu Zhou, Simin Fan, Martin Jaggi:
HyperINF: Unleashing the HyperPower of the Schulz's Method for Data Influence Estimation. CoRR abs/2410.05090 (2024) - [i124]El Mahdi Chayti, Nikita Doikov, Martin Jaggi:
Improving Stochastic Cubic Newton with Momentum. CoRR abs/2410.19644 (2024) - [i123]Atli Kosson, Bettina Messmer, Martin Jaggi:
Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training. CoRR abs/2410.23922 (2024) - 2023
- [j12]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond Spectral Gap: The Role of the Topology in Decentralized Learning. J. Mach. Learn. Res. 24: 355:1-355:31 (2023) - [j11]Julien Heitmann, Alban Glangetas, Jonathan Doenz, Juliane Dervaux, Deeksha M. Shama, Daniel Hinjos Garcia, Mohamed Rida Benissa, Aymeric Cantais, Alexandre Perez, Daniel Müller, Tatjana Chavdarova, Isabelle Ruchonnet-Metrailler, Johan N. Siebert, Laurence Lacroix, Martin Jaggi, Alain Gervaix, Mary-Anne Hartley, Florence Hugon, Derrick Fassbind, Makura Barro, Georges Bediang, N. E. L. Hafidi, M. Bouskraoui, Idrissa Ba:
DeepBreath - automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries. npj Digit. Medicine 6 (2023) - [j10]Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy:
Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c93]Sofia Blinova, Xinyu Zhou, Martin Jaggi, Carsten Eickhoff, Seyed Ali Bahrainian:
SIMSUM: Document-level Text Simplification via Simultaneous Summarization. ACL (1) 2023: 9927-9944 - [c92]Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion:
Linearization Algorithms for Fully Composite Optimization. COLT 2023: 3669-3695 - [c91]Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy:
Agree to Disagree: Diversity through Disagreement for Better Transferability. ICLR 2023 - [c90]Nikita Doikov, El Mahdi Chayti, Martin Jaggi:
Second-Order Optimization with Lazy Hessians. ICML 2023: 8138-8161 - [c89]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Special Properties of Gradient Descent with Large Learning Rates. ICML 2023: 25082-25104 - [c88]Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi:
Collaborative Learning via Prediction Consensus. NeurIPS 2023 - [c87]Atli Kosson, Martin Jaggi:
Multiplication-Free Transformer Training via Piecewise Affine Operations. NeurIPS 2023 - [c86]Amirkeivan Mohtashami, Martin Jaggi:
Random-Access Infinite Context Length for Transformers. NeurIPS 2023 - [c85]Matteo Pagliardini, Daniele Paliotta, Martin Jaggi, François Fleuret:
Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention. NeurIPS 2023 - [c84]Vinitra Swamy, Malika Satayeva, Jibril Frej, Thierry Bossy, Thijs Vogels, Martin Jaggi, Tanja Käser, Mary-Anne Hartley:
MultiMoDN - Multimodal, Multi-Task, Interpretable Modular Networks. NeurIPS 2023 - [i122]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond spectral gap (extended): The role of the topology in decentralized learning. CoRR abs/2301.02151 (2023) - [i121]El Mahdi Chayti, Nikita Doikov, Martin Jaggi:
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods. CoRR abs/2302.11962 (2023) - [i120]Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion:
Linearization Algorithms for Fully Composite Optimization. CoRR abs/2302.12808 (2023) - [i119]Amirkeivan Mohtashami, Martin Jaggi:
Landmark Attention: Random-Access Infinite Context Length for Transformers. CoRR abs/2305.16300 (2023) - [i118]Atli Kosson, Martin Jaggi:
Hardware-Efficient Transformer Training via Piecewise Affine Operations. CoRR abs/2305.17190 (2023) - [i117]Atli Kosson, Dongyang Fan, Martin Jaggi:
Ghost Noise for Regularizing Deep Neural Networks. CoRR abs/2305.17205 (2023) - [i116]Atli Kosson, Bettina Messmer, Martin Jaggi:
Rotational Optimizers: Simple & Robust DNN Training. CoRR abs/2305.17212 (2023) - [i115]Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi:
Collaborative Learning via Prediction Consensus. CoRR abs/2305.18497 (2023) - [i114]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders. CoRR abs/2305.19259 (2023) - [i113]Matteo Pagliardini, Daniele Paliotta, Martin Jaggi, François Fleuret:
Faster Causal Attention Over Large Sequences Through Sparse Flash Attention. CoRR abs/2306.01160 (2023) - [i112]Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi:
Provably Personalized and Robust Federated Learning. CoRR abs/2306.08393 (2023) - [i111]Linara Adilova, Asja Fischer, Martin Jaggi:
Layerwise Linear Mode Connectivity. CoRR abs/2307.06966 (2023) - [i110]Vinitra Swamy, Malika Satayeva, Jibril Frej, Thierry Bossy, Thijs Vogels, Martin Jaggi, Tanja Käser, Mary-Anne Hartley:
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks. CoRR abs/2309.14118 (2023) - [i109]Amirkeivan Mohtashami, Matteo Pagliardini, Martin Jaggi:
CoTFormer: More Tokens With Attention Make Up For Less Depth. CoRR abs/2310.10845 (2023) - [i108]Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael C. Gastpar:
LASER: Linear Compression in Wireless Distributed Optimization. CoRR abs/2310.13033 (2023) - [i107]Simin Fan, Martin Jaggi:
Irreducible Curriculum for Language Model Pretraining. CoRR abs/2310.15389 (2023) - [i106]Simin Fan, Matteo Pagliardini, Martin Jaggi:
DoGE: Domain Reweighting with Generalization Estimation. CoRR abs/2310.15393 (2023) - [i105]Seyed Ali Bahrainian, Martin Jaggi, Carsten Eickhoff:
Controllable Topic-Focused Abstractive Summarization. CoRR abs/2311.06724 (2023) - [i104]Zeming Chen, Alejandro Hernández-Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne Hartley, Martin Jaggi, Antoine Bosselut:
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models. CoRR abs/2311.16079 (2023) - 2022
- [c83]Yatin Dandi, Luis Barba, Martin Jaggi:
Implicit Gradient Alignment in Distributed and Federated Learning. AAAI 2022: 6454-6462 - [c82]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Masked Training of Neural Networks with Partial Gradients. AISTATS 2022: 5876-5890 - [c81]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. ICLR 2022 - [c80]Fedor Moiseev, Zhe Dong, Enrique Alfonseca, Martin Jaggi:
SKILL: Structured Knowledge Infusion for Large Language Models. NAACL-HLT 2022: 1581-1588 - [c79]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. NeurIPS 2022 - [c78]Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. NeurIPS 2022 - [c77]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond spectral gap: the role of the topology in decentralized learning. NeurIPS 2022 - [i103]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Byzantine-Robust Decentralized Learning via Self-Centered Clipping. CoRR abs/2202.01545 (2022) - [i102]Amirkeivan Mohtashami, Sebastian U. Stich, Martin Jaggi:
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods. CoRR abs/2202.01838 (2022) - [i101]Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy:
Agree to Disagree: Diversity through Disagreement for Better Transferability. CoRR abs/2202.04414 (2022) - [i100]Matteo Pagliardini, Gilberto Manunza, Martin Jaggi, Michael I. Jordan, Tatjana Chavdarova:
Improving Generalization via Uncertainty Driven Perturbations. CoRR abs/2202.05737 (2022) - [i99]Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Data-heterogeneity-aware Mixing for Decentralized Learning. CoRR abs/2204.06477 (2022) - [i98]Fedor Moiseev, Zhe Dong, Enrique Alfonseca, Martin Jaggi:
SKILL: Structured Knowledge Infusion for Large Language Models. CoRR abs/2205.08184 (2022) - [i97]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
On Avoiding Local Minima Using Gradient Descent With Large Learning Rates. CoRR abs/2205.15142 (2022) - [i96]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond spectral gap: The role of the topology in decentralized learning. CoRR abs/2206.03093 (2022) - [i95]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. CoRR abs/2206.08307 (2022) - [i94]Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. CoRR abs/2210.04620 (2022) - [i93]Cécile Trottet, Thijs Vogels, Martin Jaggi, Mary-Anne Hartley:
Modular Clinical Decision Support Networks (MoDN) - Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments. CoRR abs/2211.06637 (2022) - [i92]Simla Burcu Harma, Canberk Sönmez, Babak Falsafi, Martin Jaggi, Yunho Oh:
Accuracy Boosters: Epoch-Driven Mixed-Mantissa Block Floating-Point for DNN Training. CoRR abs/2211.10737 (2022) - [i91]Frédéric Berdoz, Abhishek Singh, Martin Jaggi, Ramesh Raskar:
Scalable Collaborative Learning via Representation Sharing. CoRR abs/2211.10943 (2022) - [i90]Nikita Doikov, El Mahdi Chayti, Martin Jaggi:
Second-order optimization with lazy Hessians. CoRR abs/2212.00781 (2022) - 2021
- [j9]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, 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, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, 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. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [j8]Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác:
An accelerated communication-efficient primal-dual optimization framework for structured machine learning. Optim. Methods Softw. 36(1): 20-44 (2021) - [c76]Zhuoyuan Mao, Prakhar Gupta, Chenhui Chu, Martin Jaggi, Sadao Kurohashi:
Lightweight Cross-Lingual Sentence Representation Learning. ACL/IJCNLP (1) 2021: 2902-2913 - [c75]Prakhar Gupta, Martin Jaggi:
Obtaining Better Static Word Embeddings Using Contextual Embedding Models. ACL/IJCNLP (1) 2021: 5241-5253 - [c74]Hossein Shokri Ghadikolaei, Sebastian U. Stich, Martin Jaggi:
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. AISTATS 2021: 3943-3951 - [c73]Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi:
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. AISTATS 2021: 4042-4050 - [c72]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS 2021: 4087-4095 - [c71]Seyed Ali Bahrainian, Martin Jaggi, Carsten Eickhoff:
Self-Supervised Neural Topic Modeling. EMNLP (Findings) 2021: 3341-3350 - [c70]Eliza Wszola, Martin Jaggi, Markus Püschel:
Faster Parallel Training of Word Embeddings. HiPC 2021: 31-41 - [c69]Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova:
Semantic Perturbations with Normalizing Flows for Improved Generalization. ICCV 2021: 6599-6609 - [c68]Tatjana Chavdarova, Matteo Pagliardini, Sebastian U. Stich, François Fleuret, Martin Jaggi:
Taming GANs with Lookahead-Minmax. ICLR 2021 - [c67]Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr, Martin Jaggi:
Understanding the effects of data parallelism and sparsity on neural network training. ICLR 2021 - [c66]Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi:
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning. ICML 2021: 1836-1845 - [c65]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Learning from History for Byzantine Robust Optimization. ICML 2021: 5311-5319 - [c64]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. ICML 2021: 5686-5696 - [c63]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML 2021: 6654-6665 - [c62]Mario Drumond, Louis Coulon, Arash Pourhabibi Zarandi, Ahmet Caner Yüzügüler, Babak Falsafi, Martin Jaggi:
Equinox: Training (for Free) on a Custom Inference Accelerator. MICRO 2021: 421-433 - [c61]Mariko Makhmutova, Raghu Kainkaryam, Marta Ferreira, Jae Min, Martin Jaggi, Ieuan Clay:
Prediction of self-reported depression scores using person-generated health data from a virtual 1-year mental health observational study. DigiBiom@MobiSys 2021: 4-11 - [c60]Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS 2021: 28004-28015 - [c59]Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
Breaking the centralized barrier for cross-device federated learning. NeurIPS 2021: 28663-28676 - [d1]Mariko Makhmutova, Raghu Kainkaryam, Marta Ferreira, Jae Min, Martin Jaggi, Ieuan Clay:
PSYCHE-D: predicting change in depression severity using person-generated health data (DATASET). Zenodo, 2021
- [i89]Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi:
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning. CoRR abs/2102.03236 (2021) - [i88]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2102.04761 (2021) - [i87]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. CoRR abs/2102.04828 (2021) - [i86]Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi:
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. CoRR abs/2103.02351 (2021) - [i85]Valerian Rey, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet, Martin Jaggi:
Federated Learning for Malware Detection in IoT Devices. CoRR abs/2104.09994 (2021) - [i84]Zhuoyuan Mao, Prakhar Gupta, Chenhui Chu, Martin Jaggi, Sadao Kurohashi:
Lightweight Cross-Lingual Sentence Representation Learning. CoRR abs/2105.13856 (2021) - [i83]Prakhar Gupta, Martin Jaggi:
Obtaining Better Static Word Embeddings Using Contextual Embedding Models. CoRR abs/2106.04302 (2021) - [i82]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Simultaneous Training of Partially Masked Neural Networks. CoRR abs/2106.08895 (2021) - [i81]Yatin Dandi, Luis Barba, Martin Jaggi:
Implicit Gradient Alignment in Distributed and Federated Learning. CoRR abs/2106.13897 (2021) - [i80]David Roschewitz, Mary-Anne Hartley, Luca Corinzia, Martin Jaggi:
IFedAvg: Interpretable Data-Interoperability for Federated Learning. CoRR abs/2107.06580 (2021) - [i79]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i78]Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova:
Semantic Perturbations with Normalizing Flows for Improved Generalization. CoRR abs/2108.07958 (2021) - [i77]Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich:
On Second-order Optimization Methods for Federated Learning. CoRR abs/2109.02388 (2021) - [i76]Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2110.04175 (2021) - [i75]Martin Beaussart, Felix Grimberg, Mary-Anne Hartley, Martin Jaggi:
WAFFLE: Weighted Averaging for Personalized Federated Learning. CoRR abs/2110.06978 (2021) - [i74]Felix Grimberg, Mary-Anne Hartley, Sai Praneeth Karimireddy, Martin Jaggi:
Optimal Model Averaging: Towards Personalized Collaborative Learning. CoRR abs/2110.12946 (2021) - [i73]El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi:
Linear Speedup in Personalized Collaborative Learning. CoRR abs/2111.05968 (2021) - [i72]Vinitra Swamy, Angelika Romanou, Martin Jaggi:
Interpreting Language Models Through Knowledge Graph Extraction. CoRR abs/2111.08546 (2021) - [i71]Futong Liu, Tao Lin, Martin Jaggi:
Understanding Memorization from the Perspective of Optimization via Efficient Influence Estimation. CoRR abs/2112.08798 (2021) - 2020
- [c58]Fabian Pedregosa, Geoffrey Négiar, Armin Askari, Martin Jaggi:
Linearly Convergent Frank-Wolfe without Line-Search. AISTATS 2020: 1-10 - [c57]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 - [c56]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 - [c55]Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi:
On the Relationship between Self-Attention and Convolutional Layers. ICLR 2020 - [c54]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. ICLR 2020 - [c53]Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi:
Dynamic Model Pruning with Feedback. ICLR 2020 - [c52]Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi:
Don't Use Large Mini-batches, Use Local SGD. ICLR 2020 - [c51]Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, Mathieu Salzmann:
Evaluating The Search Phase of Neural Architecture Search. ICLR 2020 - [c50]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 - [c49]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. ICML 2020: 6094-6104 - [c48]Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. ICML 2020: 9036-9045 - [c47]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 - [c46]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. NeurIPS 2020 - [c45]Sidak Pal Singh, Martin Jaggi:
Model Fusion via Optimal Transport. NeurIPS 2020 - [c44]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) - [c43]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. AISTATS 2019: 2887-2896 - [c42]Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel:
On Linear Learning with Manycore Processors. HiPC 2019: 184-194 - [c41]Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations. DGS@ICLR 2019 - [c40]Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat:
Overcoming Multi-model Forgetting. ICML 2019: 594-603 - [c39]Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. ICML 2019: 3252-3261 - [c38]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. ICML 2019: 3478-3487 - [c37]Niccolò Sacchi, Alexandre Nanchen, Martin Jaggi, Milos Cernak:
Open-Vocabulary Keyword Spotting with Audio and Text Embeddings. INTERSPEECH 2019: 3362-3366 - [c36]Prakhar Gupta, Matteo Pagliardini, Martin Jaggi:
Better Word Embeddings by Disentangling Contextual n-Gram Information. NAACL-HLT (1) 2019: 933-939 - [c35]Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi:
Unsupervised Scalable Representation Learning for Multivariate Time Series. NeurIPS 2019: 4652-4663 - [c34]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. NeurIPS 2019: 14236-14245 - [c33]Arno Schneuwly, Ralf Grubenmann, Séverine Rion Logean, Mark Cieliebak, Martin Jaggi:
Correlating Twitter Language with Community-Level Health Outcomes. SMM4H@ACL 2019: 71-78 - [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 S. 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 Randall 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, Kallista A. 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, Sören Laue:
Approximating parameterized convex optimization problems. ACM Trans. Algorithms 9(1): 10:1-10:17 (2012) - [c5]Joachim Giesen, Martin Jaggi, Sören Laue:
Optimizing over the Growing Spectrahedron. ESA 2012: 503-514 - [c4]Joachim Giesen, Martin Jaggi, Sören Laue:
Regularization Paths with Guarantees for Convex Semidefinite Optimization. AISTATS 2012: 432-439 - [i4]Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. CoRR abs/1207.4747 (2012) - 2011
- [b1]Martin Jaggi:
Sparse Convex Optimization Methods for Machine Learning. ETH Zurich, Zürich, Switzerland, 2011 - [i3]Martin Jaggi:
Convex Optimization without Projection Steps. CoRR abs/1108.1170 (2011) - 2010
- [c3]Joachim Giesen, Martin Jaggi, Sören Laue:
Approximating Parameterized Convex Optimization Problems. ESA (1) 2010: 524-535 - [c2]Martin Jaggi, Marek Sulovský:
A Simple Algorithm for Nuclear Norm Regularized Problems. ICML 2010: 471-478
2000 – 2009
- 2009
- [c1]Bernd Gärtner, Martin Jaggi:
Coresets for polytope distance. SCG 2009: 33-42 - [i2]Bernd Gärtner, Joachim Giesen, Martin Jaggi:
An Exponential Lower Bound on the Complexity of Regularization Paths. CoRR abs/0903.4817 (2009) - [i1]Bernd Gärtner, Joachim Giesen, Martin Jaggi, Torsten Welsch:
A Combinatorial Algorithm to Compute Regularization Paths. CoRR abs/0903.4856 (2009)
Coauthor Index
Celestine Dünner
aka: Celestine Mendler-Dünner
aka: Celestine Mendler-Dünner
Sai Praneeth Karimireddy
aka: Sai Praneeth Reddy Karimireddy
aka: Sai Praneeth Reddy Karimireddy
[c98] [c96] [c89] [i114] [c82] [c79] [i102] [i99] [i97] [i95] [j9] [c74] [c73] [c72] [c69] [c68] [c64] [c63] [c60] [c59] [i88] [i87] [i86] [i82] [i79] [i78] [i77] [i76] [i73] [c54] [c53] [c52] [c50] [c49] [c46] [i70] [i66] [i65] [i64] [i59] [i57] [c43] [c39] [c38] [i55] [i53] [i42] [i38] [c31] [c28] [c25] [i35] [i33] [i30] [i28] [i27] [c20] [c17] [i20] [i16]
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