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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]