Research

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

2018

M. P. Drumond Lages De Oliveira; T. Lin; M. Jaggi; B. Falsafi : Training DNNs with Hybrid Block Floating Point. 2018-12-04. Neural Information Processing Systems, Montréal Canada, December 2-8, 2018.
A. d'Aspremont; C. Guzmán; M. Jaggi : Optimal Affine-Invariant Smooth Minimization Algorithms; SIAM Journal on Optimization. 2018-09-19. DOI : 10.1137/17M1116842.
A. Hug : Unsupervised Learning of Representations for Lexical Entailment Detection ; 2018-09-04.
M. Pagliardini; P. Gupta; M. Jaggi : Unsupervised learning of sentence embeddings using compositional n-gram features. 2018-05-01. NAACL 2018 - Conference of the North American Chapter of the Association for Computational Linguistics.
K. Bennani-Smires; C.-C. Musat; A. Hossmann; M. Baeriswyl; M. Jaggi : Simple Unsupervised Keyphrase Extraction using Sentence Embeddings. 2018-05-01. CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning.
F. Locatello; A. Raj; S. P. R. Karimireddy; G. Rätsch; B. Schölkopf et al. : On Matching Pursuit and Coordinate Descent. 2018-05-01.
S. P. R. Karimireddy; M. Jaggi; S. Stich : Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. 2018-05-01.
E. Grave; P. Bojanowski; P. Gupta; A. Joulin; T. Mikolov : Learning Word Vectors for 157 Languages. 2018-02-19. Language Resources and Evaluation Conference, Miyazaki, Japan, May 7-12, 2018.
K. Maran; S. Reddy; D. Sharma; A. Tripathi : SOME RESULTS ON A CLASS OF MIXED VAN DER WAERDEN NUMBERS; Rocky Mt. J. Math.. 2018. DOI : 10.1216/RMJ-2018-48-3-885.
J. Meng; W. Xu; X. Chen; T. Lin; X. Deng : Gene locations may contribute to predicting gene regulatory relationships; J. Zhejiang Univ.-SCI. B. 2018. DOI : 10.1631/jzus.B1700303.
V. Smith; S. Forte; C. Ma; M. Takac; M. Jordan et al. : CoCoA: A General Framework for Communication-Efficient Distributed Optimization.
I. Bahej; I. Clay; M. Jaggi; V. De Luca : Prediction of patient-reported physical activity scores from wearable accelerometer data: a feasibility study. 2018. ICNR2018 - International Conference on NeuroRehabilitation, Pisa, Italy, October 16 - 20, 2018.

2017

A. S. Chiappa : Asynchronous updates for stochastic gradient descent ; 2017-06-09.
F. Künstner : Fully Quantized Distributed Gradient Descent ; 2017.
S. U. Stich; A. Raj; M. Jaggi : Safe Adaptive Importance Sampling. 2017. Neural Information Processing Systems (NIPS), Long Beach, USA, December 4-9, 2017.
R. Fulek; H. N. Mojarrad; M. Naszódi; J. Solymosi; S. U. Stich et al. : On the existence of ordinary triangles; Computational Geometry. 2017. DOI : 10.1016/j.comgeo.2017.07.002.
P. Kaiser; J. D. Wegner; A. Lucchi; M. Jaggi; T. Hofmann et al. : Learning Aerial Image Segmentation from Online Maps; IEEE Transactions on Geoscience and Remote Sensing. 2017. DOI : 10.1109/TGRS.2017.2719738.
S. U. Stich; A. Raj; M. Jaggi : Approximate Steepest Coordinate Descent. 2017. International Conference on Machine Learning (ICML 2017), Sydney, Australia, Aug 6-11, 2017.
T. Tina Fang; M. Jaggi; K. Argyraki : Generating Steganographic Text with LSTMs. 2017. ACL Student Research Workshop 2017, Vancouver, Canada, July 30-August 4, 2017. p. 100–106. DOI : 10.18653/v1/P17-3017.
C. Ma; J. Konecný; M. Jaggi; V. Smith; M. I. Jordan et al. : Distributed Optimization with Arbitrary Local Solvers; Journal of Optimization Methods and Software. 2017. DOI : 10.1080/10556788.2016.1278445.
M. Pagliardini; P. Gupta; M. Jaggi : Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features. 2017.
F. Locatello; R. Khanna; M. Tschannen; M. Jaggi : A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. 2017. 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, California, United States, April 20-22, 2017.
J. Deriu; A. Lucchi; V. D. Luca; A. Severyn; S. Müller et al. : Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. 2017. International World Wide Web Conference (WWW) 2017, Perth, Australia, April 3-7, 2017. p. 1045-1052. DOI : 10.1145/3038912.3052611.
D. Perekrestenko; V. Cevher; M. Jaggi : Faster Coordinate Descent via Adaptive Importance Sampling. 2017. 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA, April 20-22, 2017.

2016

V. Smith; S. Forte; C. Ma; M. Takác; M. I. Jordan et al. : CoCoA: A General Framework for Communication-Efficient Distributed Optimization. 2016.
A. Raj; J. Olbrich; B. Gärtner; B. Schölkopf; M. Jaggi : Screening Rules for Convex Problems. 2016.
R. Khanna; M. Tschannen; M. Jaggi : Pursuits in Structured Non-Convex Matrix Factorizations. 2016.
J. Deriu; M. Gonzenbach; F. Uzdilli; A. Lucchi; V. D. Luca et al. : SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision. 2016. SemEval@NAACL-HLT 2016, San Diego, CA, USA, June 16-17, 2016. p. 1124-1128.
C. Dünner; S. Forte; M. Takác; M. Jaggi : Primal-Dual Rates and Certificates. 2016. International Conference on Machine Learning (ICML) 2016, USA, NY, New York City, June 19-24, 2016. p. 783-792.
E. Sprengel; M. Jaggi; Y. Kilcher; T. Hofmann : Audio Based Bird Species Identification using Deep Learning Techniques. 2016. Conference and Labs of the Evaluation Forum (CLEF) 2016, Évora, Portugal, 5-8 September, 2016. p. 547-559.

2015

V. Smith; S. Forte; M. I. Jordan; M. Jaggi : L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. 2015.
F. Uzdilli; M. Jaggi; D. Egger; P. Julmy; L. Derczynski et al. : Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment.. 2015. SemEval@NAACL-HLT 2015, Denver, Colorado, USA, June 4-5, 2015. p. 608-612.
S. Lacoste-Julien; M. Jaggi : On the Global Linear Convergence of Frank-Wolfe Optimization Variants.. 2015. Neural Information Processing Systems (NIPS) 2015, Montreal, Quebec, Canada, December 7-12, 2015. p. 496-504.
C. Ma; V. Smith; M. Jaggi; M. I. Jordan; P. Richtárik et al. : Adding vs. Averaging in Distributed Primal-Dual Optimization.. 2015. International Conference on Machine Learning (ICML) 2015, Lille, France, 6-11 July 2015. p. 1973-1982.

2014

M. Jaggi : An Equivalence between the Lasso and Support Vector Machines. 2014.
M. Jaggi; F. Uzdilli; M. Cieliebak : Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams.. 2014. SemEval@COLING 2014, Dublin, Ireland, August 23-24, 2014. p. 601-604.
M. Jaggi; V. Smith; M. Takác; J. Terhorst; S. Krishnan et al. : Communication-Efficient Distributed Dual Coordinate Ascent.. 2014. Neural Information Processing Systems (NIPS) 2014, Montreal, Quebec, Canada, December 8-13 2014. p. 3068-3076.

2013

M. Jaggi : Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization.. 2013. International Conference on Machine Learning (ICML) 2013, Atlanta, GA, USA, 16-21 June 2013. p. 427-435.
S. Lacoste-Julien; M. Jaggi; M. W. Schmidt; P. Pletscher : Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. 2013. International Conference on Machine Learning (ICML) 2013, Atlanta, GA, USA, 16-21 June 2013. p. 53-61.

2012

J. Giesen; M. Jaggi; S. Laue : Regularization Paths with Guarantees for Convex Semidefinite Optimization.. 2012. International Conference on Artificial Intelligence and Statistics (AISTATS) 2012, La Palma, Canary Islands, April 21-23, 2012. p. 432-439.
J. Giesen; M. Jaggi; S. Laue : Optimizing over the Growing Spectrahedron. 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.
J. Giesen; M. Jaggi; S. Laue : Approximating parameterized convex optimization problems; ACM Trans. Algorithms. 2012. DOI : 10.1145/2390176.2390186.
B. Gärtner; M. Jaggi; C. Maria : An Exponential Lower Bound on the Complexity of Regularization Paths; JoCG - Journal of Computational Geometry. 2012. DOI : 10.20382/jocg.v3i1a9.
J. Giesen; M. Jaggi; S. Laue : Optimizing over the Growing Spectrahedron; Algorithms – ESA 2012; Springer Berlin Heidelberg, 2012. p. 503-514.

2011

M. Jaggi : Sparse convex optimization methods for machine learning. ETH Zürich, 2011.
M. Jaggi : Convex Optimization without Projection Steps. 2011.

2010

M. Jaggi; M. Sulovský : A Simple Algorithm for Nuclear Norm Regularized Problems.. 2010. International Conference on Machine Learning (ICML) 2010, Haifa, Israel, June 21-24, 2010. p. 471-478.
J. Giesen; M. Jaggi; S. Laue : Approximating Parameterized Convex Optimization Problems.. 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.
J. Giesen; M. Jaggi; S. Laue : Approximating Parameterized Convex Optimization Problems; European Symposia on Algorithms (ESA) 2010; Springer Berlin Heidelberg, 2010. p. 524-535.

2009

B. Gärtner; J. Giesen; M. Jaggi; T. Welsch : A Combinatorial Algorithm to Compute Regularization Paths. 2009.
B. Gärtner; M. Jaggi : Coresets for polytope distance. 2009. Symposium on Computational Geometry 2009, Aarhus, Denmark, June 8-10, 2009. p. 33-42. DOI : 10.1145/1542362.1542370.