Research

We’re interested in machine learning, optimization algorithms and text understanding, as well as several application domains.

The list below is NOT up to date. Please refer to the Google Scholar pages of our team members instead.

2021

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Y-A. Ma; N. S. Chatterji; X. Cheng; N. Flammarion; P. L. Bartlett et al. 

Bernoulli. 2021-08-01. Vol. 27, num. 3, p. 1942-1992. DOI : 10.3150/20-BEJ1297.

Measuring the Impact of Model and Input Heterogeneity in Personalized Federated Learning

F. Behrens 

2021-02-19.

HyperAggregate: A sublinear secure aggregation protocol

M. Vujasinovic 

2021.

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

S. U. Stich; A. Mohtashami; M. Jaggi 

2021. AISTATS 2021 24th International Conference on Artificial Intelligence and Statistics, Virtual, April 13-15, 2021.

2020

Design Patterns for Resource-Constrained Automated Deep-Learning Methods

L. Tuggener; M. Amirian; F. Benites; P. von Däniken; P. Gupta et al. 

AI. 2020-10-06. Vol. 1, num. 4, p. 510-538. DOI : 10.3390/ai1040031.

The Internet of Audio Things: State of the Art, Vision, and Challenges

L. Turchet; G. Fazekas; M. Lagrange; H. S. Ghadikolaei; C. Fischione 

Ieee Internet Of Things Journal. 2020-10-01. Vol. 7, num. 10, p. 10233-10249. DOI : 10.1109/JIOT.2020.2997047.

Catalytic Enantioselective Functionalizations of C-H Bonds by Chiral Iridium Complexes

L. Wozniak; J-F. Tan; Qui-Hien Nguyen; A. M. du Vigne; V. Smal et al. 

Chemical Reviews. 2020-09-23. Vol. 120, num. 18, p. 10516-10543. DOI : 10.1021/acs.chemrev.0c00559.

Clinical relevance of low-densityPlasmodium falciparumparasitemia in untreated febrile children: A cohort study

M-A. Hartley; N. Hofmann; K. Keitel; F. Kagoro; C. A. Moniz et al. 

Plos Medicine. 2020-09-01. Vol. 17, num. 9, p. e1003318. DOI : 10.1371/journal.pmed.1003318.

Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

T. Bossy 

2020-08-30.

Speech Style Transfer

O. Barbany Mayor 

2020-08-14.

Self-supervised learning for time series classification

A-S. Van de Velde 

2020-08-09.

LaMBERT: Light and Multigranular BERT

L. Milosheski 

2020-07-01.

CUMULATOR — a tool to quantify and report the carbon footprint of machine learning computations and communication in academia and healthcare

T. Trébaol 

2020-06-19.

Privacy-preserving and Personalized Federated Machine Learning for Medical Data

F. Grimberg 

2020-06-18.

MLBench – Optional Semester Project

M. Milenkoski 

2020-06-15.

Efficient second-order methods for model compression

S. P. Singh 

2020-04-06.

Automated Essay Scoring in Foreign Language Students Based on Deep Contextualised Word Representations

B. Ranković; S. Smirnow; M. Jaggi; M. J. Tomasik 

2020-03-23. LAK20 – 10th International Conference on Learning Analytics & Knowledge, Mars 23, 2020.

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!

D. Kovalev; A. Koloskova; M. Jaggi; P. Richtarik; U. S. Sitch 

2020. AISTATS 2021 24th International Conference on Artificial Intelligence and Statistics, Virtual, April 13-15, 2021.

Practical Low-Rank Communication Compression in Decentralized Deep Learning

T. Vogels; S. P. R. Karimireddy; M. Jaggi 

2020. NeurIPS 2020 – Advances in Neural Information Processing Systems, Virtual, December 6-12, 2020.

Ensemble Distillation for Robust Model Fusion in Federated Learning

T. Lin; L. Kong; S. U. Stich; M. Jaggi 

2020. NeurIPS 2020 – Advances in Neural Information Processing Systems, Virtual, April 13-15, 2021.

Model Fusion via Optimal Transport

S. P. Singh; M. Jaggi 

2020. NeurIPS 2020 – Advances in Neural Information Processing Systems, Virtual, April 13-15, 2021.

Extrapolation for Large-batch Training in Deep Learning

T. Lin; L. Kong; S. U. Stich; M. Jaggi 

2020. ICML 2020 37th International Conference on Machine Learning, Virtual, July 13-18, 2020.

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

A. Koloskova; N. Loizou; S. Boreiri; M. Jaggi; S. U. Stich 

2020. ICML 2020 37th International Conference on Machine Learning, Virtual, July 13-18, 2020.

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

S. U. Stich; S. P. Karimireddy 

Journal Of Machine Learning Research. 2020-01-01. Vol. 21, p. 237.

Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning

F. Grimberg; M-A. Hartley; M. Jaggi; S. P. Karimireddy 

2020. MICCAI Workshop on Distributed and Collaborative Learning, Lima, Peru, Octobre 4-8, 2020. p. 160-169. DOI : 10.1007/978-3-030-60548-3_16.

Accelerating Gradient Boosting Machines

H. Lu; S. P. Karimireddy; N. Ponomareva; V. Mirrokni 

2020-01-01. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), ELECTR NETWORK, Aug 26-28, 2020. p. 516-525.

Context Mover’s Distance & Barycenters: Optimal Transport of Contexts for Building Representations

S. P. Singh; A. Hug; A. Dieuleveut; M. Jaggi 

2020-01-01. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), ELECTR NETWORK, Aug 26-28, 2020.

GA-Par: Dependable Microservice Orchestration Framework for Geo-Distributed Clouds

Z. Wen; T. Lin; R. Yang; S. Ji; R. Ranjan et al. 

Ieee Transactions On Parallel And Distributed Systems. 2020-01-01. Vol. 31, num. 1, p. 129-143. DOI : 10.1109/TPDS.2019.2929389.

Dynamic Model Pruning with Feedback

T. Lin; S. U. Stich; L. F. Barba Flores; D. Dmitriev; M. Jaggi 

2020. 8th International Conference on Learning Representations (ICLR), Virtual Conference, Formerly Addis Ababa, Ethiopia, April 26-30, 2020.

Forecasting intracranial hypertension using multi-scale waveform metrics

M. Hueser; A. Kuendig; W. Karlen; V. De Luca; M. Jaggi 

Physiological Measurement. 2020-01-01. Vol. 41, num. 1, p. 014001. DOI : 10.1088/1361-6579/ab6360.

Network coded multipath and related techniques

M. Medard; D. Malak; A. Schneuwly; E. Telatar 

US2020328858; WO2020028494.

2020.

An Explicit Construction of Optimal Streaming Codes for Channels With Burst and Arbitrary Erasures

D. Dudzicz; S. L. Fong; A. Khisti 

Ieee Transactions On Communications. 2020-01-01. Vol. 68, num. 1, p. 12-25. DOI : 10.1109/TCOMM.2019.2944372.

On the Relationship between Self-Attention and Convolutional Layers

J-B. Cordonnier; A. Loukas; M. Jaggi 

2020. Eighth International Conference on Learning Representations – ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020.

Evaluating the search phase of neural architecture search

K. Yu; C. Suito; M. Jaggi; C-C. Musat; M. Salzmann 

2020. ICRL 2020 Eighth International Conference on Learning Representations, Millennium Hall, Addis Ababa, ETHIOPIA, April 26-30, 2020.

2019

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

C. Ma; M. Jaggi; F. E. Curtis; N. Srebro; M. Takac 

Optimization Methods & Software. 2019-08-06. DOI : 10.1080/10556788.2019.1650361.

Stochastic Zeroth-Order Optimisation Algorithms with Variance Reduction

A. Ajalloeian 

2019-06-21.

A comparison of model-parallel training methods for deep learning

P. Kang 

2019-06-18.

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

A. Koloskova; S. U. Stich; M. Jaggi 

2019-06-09. ICML 2019 – International Conference on Machine Learning, Long Beach, California, USA, 9-15 June 2019.

Local SGD Converges Fast and Communicates Little

S. U. Stich 

2019-05-06. ICLR 2019 – International Conference on Learning Representations, New Orleans, USA, May 6-9, 2019.

Unsupervised Robust Nonparametric Learning Of Hidden Community Properties

M. Langovoy; A. Gotmare; M. Jaggi 

Mathematical Foundations Of Computing. 2019-05-01. Vol. 2, num. 2, p. 127-147. DOI : 10.3934/mfc.2019010.

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

M. Josifoski; I. S. Paskov; H. S. Paskov; M. Jaggi; R. West 

2019-02-13. WSDM ’19 – ACM International Conference on Web Search and Data Mining, Melbourne, Australia, February 11 – 15, 2019 . p. 744-752. DOI : 10.1145/3289600.3291023.

Decentralized deep learning with arbitrary communication compression

A. Koloskova; T. Lin; S. U. Stich; M. Jaggi 

2019. ICLR 2020 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, April 26-30, 2020.

Don’t Use Large Mini-Batches, Use Local SGD

T. Lin; S. U. Stich; K. K. Patel; M. Jaggi 

2019. ICLR 2020 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, April 26-30, 2020.

On Linear Learning with Manycore Processors

E. Wszola; C. Mendler-Duenner; M. Jaggi; M. Pueschel 

2019-01-01. 26th International Conference on High Performance Computing, Data and Analytics (HiPCW), Hyderabad, INDIA, Dec 17-20, 2019. p. 184-194. DOI : 10.1109/HiPC.2019.00032.

Communication trade-offs for Local-SGD with large step size

K. K. Patel; A. Dieuleveut 

2019-01-01. 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, CANADA, Dec 08-14, 2019.

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

T. Vogels; S. P. Karinireddy; M. Jaggi 

2019-01-01. 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec 08-14, 2019.

Correlating Twitter Language with Community-Level Health Outcomes

A. Schneuwly; R. Grubenmann; S. R. Logean; M. Cieliebak; M. Jaggi 

2019-01-01. 4th Social Media Mining for Health Applications Workshop and Shared Task (SMM4H), Florence, ITALY, Aug 02, 2019. p. 71-78.

Unsupervised Scalable Representation Learning for Multivariate Time Series

J-Y. Franceschi; A. Dieuleveut; M. Jaggi 

2019-01-01. 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, CANADA, Dec 08-14, 2019.

Transformer-Based Multi-lingual Sentence Embeddings

W. Pei 

2019.

Efficient Greedy Coordinate Descent for Composite Problems

S. P. R. Karimireddy; A. Koloskova; S. U. Stich; M. Jaggi 

2019. The 22nd International Conference on Artificial Intelligence and Statistics – AISTATS 2019, Naha, Okinawa, Japan, April 16-18, 2019. p. 2887-2896.

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

S. P. R. Karimireddy; Q. Rebjock; S. U. Stich; M. Jaggi 

2019. 36th International Conference on Machine Learning (ICML) 2019, Long Beach, USA, June 9-15, 2019. p. 3252-3261.

Overcoming Multi-model Forgetting

Y. Benyahia; K. Yu; K. B. Smires; M. Jaggi; A. C. Davison et al. 

2019. ICML 2019 – 36th International Conference on Machine Learning, Long Beach, California, USA, June 09-15, 2019. p. 594-603.

Open-Vocabulary Keyword Spotting with Audio and Text Embeddings

N. Sacchi; A. Nanchen; M. Jaggi; M. Cernak 

2019. INTERSPEECH 2019 – IEEE International Conference on Acoustics, Speech, and Signal Processing, Graz, Austria, DOI : 10.21437/Interspeech.2019-1846.

Better Word Embeddings by Disentangling Contextual n-Gram Information

P. Gupta; M. Pagliardini; M. Jaggi 

2019. NAACL 2019 – Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Minnesota, USA, June 2-7. 2019.

2018

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

R. M. Gower; F. Hanzely; P. Richtárik; S. U. Stich 

2018-12-02. 32nd Annual Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, December 2-8, 2018.

Sparsified SGD with Memory

S. U. Stich; J-B. Cordonnier; M. Jaggi 

2018-12-02. NeurIPS 2018 – 32nd Annual Conference on Neural Information Processing Systems, Montréal, Canada, December 2-8, 2018.

Optimal Affine-Invariant Smooth Minimization Algorithms

A. d’Aspremont; C. Guzmán; M. Jaggi 

SIAM Journal on Optimization. 2018-09-19. Vol. 28, num. 3, p. 2384-2405. DOI : 10.1137/17M1116842.

Unsupervised Learning of Representations for Lexical Entailment Detection

A. Hug 

2018-09-04.

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

V. Smith; S. Forte; C. Ma; M. Takac; M. I. Jordan et al. 

Journal of Machine Learning Research. 2018-07-01. Vol. 18.

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

J-B. Cordonnier 

2018-06-27.

Detecting Steganography

M-J. Lagarde 

2018-06-21.

Decoupling Backpropagation using Constrained Optimization Methods

A. Gotmare; V. Thomas; J. M. Brea; M. Jaggi 

2018-06-18. ICML 2018 35th International Conference on Machine Learning, Stockholm, SWEDEN, July 10-15, 2018.

Unsupervised learning of sentence embeddings using compositional n-gram features

M. Pagliardini; P. Gupta; M. Jaggi 

2018-05-01. NAACL 2018 – Conference of the North American Chapter of the Association for Computational Linguistics. DOI : 10.18653/v1/N18-1049.

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

K. Bennani-Smires; C-C. Musat; A. Hossmann; M. Baeriswyl; M. Jaggi 

2018-05-01. CoNLL 2018 – SIGNLL Conference on Computational Natural Language Learning.

On Matching Pursuit and Coordinate Descent

F. Locatello; A. Raj; S. P. R. Karimireddy; G. Rätsch; B. Schölkopf et al. 

2018-05-01. ICML 2018 – 35th International Conference on Machine Learning.

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems

S. P. R. Karimireddy; M. Jaggi; S. Stich 

2018-05-01. AISTATS 2018 – International Conference on Artificial Intelligence and Statistics.

Learning Word Vectors for 157 Languages

E. Grave; P. Bojanowski; P. Gupta; A. Joulin; T. Mikolov 

2018-02-19. Language Resources and Evaluation Conference, Miyazaki, Japan, May 7-12, 2018.

A Distributed Second-Order Algorithm You Can Trust

C. Mendler-Dünner; A. Lucchi; M. Gargiani; Y. A. Bian; T. Hofmann et al. 

2018. 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 10-15 July 2018. p. 1358-1366.

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

R. M. Gower; F. Hanzely; P. Richtarik; S. U. Stich 

2018-01-01. 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, CANADA, Dec 02-08, 2018.

COLA: Decentralized Linear Learning

L. He; A. Bian; M. Jaggi 

2018-01-01. 32nd Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada, Dec 02-08, 2018.

Training DNNs with Hybrid Block Floating Point

M. Drumond; T. Lin; M. Jaggi; B. Falsafi 

2018-01-01. NeurIPS 2018 – 32nd Conference on Neural Information Processing Systems, Montreal, CANADA, Dec 02-08, 2018.

Some results on a class of mixed van der Waerden numbers

K. Maran; S. Reddy; D. Sharma; A. Tripathi 

Rocky Mountain Journal of Mathematics. 2018. Vol. 48, num. 3, p. 885-904. DOI : 10.1216/RMJ-2018-48-3-885.

Gene locations may contribute to predicting gene regulatory relationships

J. Meng; W. Xu; X. Chen; T. Lin; X. Deng 

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B. 2018. Vol. 19, num. 1, p. 25-37. DOI : 10.1631/jzus.B1700303.

Prediction of patient-reported physical activity scores from wearable accelerometer data: a feasibility study

I. Bahej; I. Clay; M. Jaggi; V. De Luca 

2018. ICNR2018 – International Conference on NeuroRehabilitation, Pisa, Italy, October 16 – 20, 2018.

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

V. Smith; S. Forte; C. Ma; M. Takác; M. I. Jordan et al. 

2018. 

2017

Asynchronous updates for stochastic gradient descent

A. S. Chiappa 

2017-06-09.

Fully Quantized Distributed Gradient Descent

F. Künstner 

2017.

Safe Adaptive Importance Sampling

S. U. Stich; A. Raj; M. Jaggi 

2017. Neural Information Processing Systems (NIPS), Long Beach, USA, December 4-9, 2017.

On the existence of ordinary triangles

R. Fulek; H. N. Mojarrad; M. Naszódi; J. Solymosi; S. U. Stich et al. 

Computational Geometry. 2017. Vol. 66, p. 28-31. DOI : 10.1016/j.comgeo.2017.07.002.

Learning Aerial Image Segmentation from Online Maps

P. Kaiser; J. D. Wegner; A. Lucchi; M. Jaggi; T. Hofmann et al. 

IEEE Transactions on Geoscience and Remote Sensing. 2017. Vol. 55, num. 11, p. 6054-6068. DOI : 10.1109/TGRS.2017.2719738.

Approximate Steepest Coordinate Descent

S. U. Stich; A. Raj; M. Jaggi 

2017. ICML 2017 – International Conference on Machine Learning, Sydney, Australia, Aug 6-11, 2017.

Generating Steganographic Text with LSTMs

T. Tina Fang; M. Jaggi; K. Argyraki 

2017. ACL Student Research Workshop 2017, Vancouver, Canada, July 30-August 4, 2017. p. 100-106. DOI : 10.18653/v1/P17-3017.

Distributed Optimization with Arbitrary Local Solvers

C. Ma; J. Konecný; M. Jaggi; V. Smith; M. I. Jordan et al. 

Journal of Optimization Methods and Software. 2017. Vol. 32. DOI : 10.1080/10556788.2016.1278445.

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

M. Pagliardini; P. Gupta; M. Jaggi 

2017

A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe

F. Locatello; R. Khanna; M. Tschannen; M. Jaggi 

2017. 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, California, United States, April 20-22, 2017.

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

J. Deriu; A. Lucchi; V. D. Luca; A. Severyn; S. Müller et al. 

2017. International World Wide Web Conference (WWW) 2017, Perth, Australia, April 3-7, 2017. p. 1045-1052. DOI : 10.1145/3038912.3052611.

Faster Coordinate Descent via Adaptive Importance Sampling

D. Perekrestenko; V. Cevher; M. Jaggi 

2017. 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA, April 20-22, 2017.

2016

Screening Rules for Convex Problems

A. Raj; J. Olbrich; B. Gärtner; B. Schölkopf; M. Jaggi 

2016

Pursuits in Structured Non-Convex Matrix Factorizations

R. Khanna; M. Tschannen; M. Jaggi 

2016

SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

J. Deriu; M. Gonzenbach; F. Uzdilli; A. Lucchi; V. D. Luca et al. 

2016. [email protected] 2016, San Diego, CA, USA, June 16-17, 2016. p. 1124-1128.

Primal-Dual Rates and Certificates

C. Dünner; S. Forte; M. Takác; M. Jaggi 

2016. ICML 2016 – International Conference on Machine Learning, USA, NY, New York City, June 19-24, 2016. p. 783-792.

Audio Based Bird Species Identification using Deep Learning Techniques

E. Sprengel; M. Jaggi; Y. Kilcher; T. Hofmann 

2016. Conference and Labs of the Evaluation Forum (CLEF) 2016, Évora, Portugal, 5-8 September, 2016. p. 547-559.

2015

L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework

V. Smith; S. Forte; M. I. Jordan; M. Jaggi 

2015

Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment.

F. Uzdilli; M. Jaggi; D. Egger; P. Julmy; L. Derczynski et al. 

2015. [email protected] 2015, Denver, Colorado, USA, June 4-5, 2015. p. 608-612.

On the Global Linear Convergence of Frank-Wolfe Optimization Variants

S. Lacoste-Julien; M. Jaggi 

2015. Neural Information Processing Systems (NIPS) 2015, Montreal, Quebec, Canada, December 7-12, 2015. p. 496-504.

Adding vs. Averaging in Distributed Primal-Dual Optimization

C. Ma; V. Smith; M. Jaggi; M. I. Jordan; P. Richtárik et al. 

2015. ICML 2015 – International Conference on Machine Learning, Lille, France, 6-11 July 2015. p. 1973-1982.

2014

An Equivalence between the Lasso and Support Vector Machines

M. Jaggi 

Regularization, Optimization, Kernels, and Support Vector Machines; Chapman and Hall/CRC, 2014. p. 1-26.

Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams.

M. Jaggi; F. Uzdilli; M. Cieliebak 

2014. [email protected] 2014, Dublin, Ireland, August 23-24, 2014. p. 601-604.

Communication-Efficient Distributed Dual Coordinate Ascent.

M. Jaggi; V. Smith; M. Takác; J. Terhorst; S. Krishnan et al. 

2014. Neural Information Processing Systems (NIPS) 2014, Montreal, Quebec, Canada, December 8-13 2014. p. 3068-3076.

2013

Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization

M. Jaggi 

2013. ICML 2013 – International Conference on Machine Learning, Atlanta, GA, USA, 16-21 June 2013. p. 427-435.

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

S. Lacoste-Julien; M. Jaggi; M. W. Schmidt; P. Pletscher 

2013. ICML 2013 – International Conference on Machine Learning, Atlanta, GA, USA, 16-21 June 2013. p. 53-61.

2012

Regularization Paths with Guarantees for Convex Semidefinite Optimization.

J. Giesen; M. Jaggi; S. Laue 

2012. International Conference on Artificial Intelligence and Statistics (AISTATS) 2012, La Palma, Canary Islands, April 21-23, 2012. p. 432-439.

Approximating parameterized convex optimization problems

J. Giesen; M. Jaggi; S. Laue 

ACM Transactions on Algorithms. 2012. Vol. 9, num. 1, p. 10:1-10:17. DOI : 10.1145/2390176.2390186.

An Exponential Lower Bound on the Complexity of Regularization Paths

B. Gärtner; M. Jaggi; C. Maria 

JoCG – Journal of Computational Geometry. 2012. Vol. 3, num. 1, p. 168-195. DOI : 10.20382/jocg.v3i1a9.

Optimizing over the Growing Spectrahedron

J. Giesen; M. Jaggi; S. Laue 

2012. European Symposia on Algorithms (ESA) 2012, Ljubljana, Slovenia, September 10-12, 2012. p. 503-514. DOI : 10.1007/978-3-642-33090-2_44.

2011

Sparse convex optimization methods for machine learning

M. Jaggi 

ETH Zürich, 2011. 

Convex Optimization without Projection Steps

M. Jaggi 

2011

2010

A Simple Algorithm for Nuclear Norm Regularized Problems.

M. Jaggi; M. Sulovský 

2010. International Conference on Machine Learning (ICML) 2010, Haifa, Israel, June 21-24, 2010. p. 471-478.

Approximating Parameterized Convex Optimization Problems

J. Giesen; M. Jaggi; S. Laue 

2010. European Symposia on Algorithms (ESA) 2010, Liverpool, UK, September 6-8, 2010. p. 524-535. DOI : 10.1007/978-3-642-15775-2_45.

2009

A Combinatorial Algorithm to Compute Regularization Paths

B. Gärtner; J. Giesen; M. Jaggi; T. Welsch 

2009

Coresets for polytope distance

B. Gärtner; M. Jaggi 

2009. Symposium on Computational Geometry 2009, Aarhus, Denmark, June 8-10, 2009. p. 33-42. DOI : 10.1145/1542362.1542370.