704 citations · 4.8k across the 117 of their papers we have counts for
12 papers · 1 filter
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning
Chenxin Ma, Martin Jaggi, Frank E. Curtis +2
Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Conf…
Safe Adaptive Importance Sampling
Sebastian U. Stich, Anant Raj, Martin Jaggi
Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defi…
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
Celestine Dünner, Thomas Parnell, Martin Jaggi
We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems. Our scheme allows to efficiently employ compute…
Learning Aerial Image Segmentation from Online Maps
Pascal Kaiser, Jan Dirk Wegner, Aurelien Lucchi +3
This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for a…
Unsupervised robust nonparametric learning of hidden community properties
Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi
We consider learning of fundamental properties of communities in large noisy networks, in the prototypical situation where the nodes or users are split into two classes according t…
Approximate Steepest Coordinate Descent
Sebastian U. Stich, Anant Raj, Martin Jaggi
We propose a new selection rule for the coordinate selection in coordinate descent methods for huge-scale optimization. The efficiency of this novel scheme is provably better than…
Generating Steganographic Text with LSTMs
Tina Fang, Martin Jaggi, Katerina Argyraki
Motivated by concerns for user privacy, we design a steganographic system ("stegosystem") that enables two users to exchange encrypted messages without an adversary detecting that…
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Francesco Locatello, Michael Tschannen, Gunnar Rätsch +1
Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe (FW) algorithms regained popularity in recent years due to their simplicity, effectiveness and theoretical…
Faster Coordinate Descent via Adaptive Importance Sampling
Dmytro Perekrestenko, Volkan Cevher, Martin Jaggi
Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive ru…
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
Jan Deriu, Aurelien Lucchi, Valeria De Luca +5
This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than En…
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
Francesco Locatello, Rajiv Khanna, Michael Tschannen +1
Two of the most fundamental prototypes of greedy optimization are the matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified view on both classes of methods,…
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Matteo Pagliardini, Prakhar Gupta, Martin Jaggi
The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (…