Search Results for author: Alex J. Smola

Found 12 papers, 0 papers with code

Learning Networks of Heterogeneous Influence

no code implementations NeurIPS 2012 Nan Du, Le Song, Ming Yuan, Alex J. Smola

However, the underlying transmission networks are often hidden and incomplete, and we observe only the time stamps when cascades of events happen.

Optimal Web-Scale Tiering as a Flow Problem

no code implementations NeurIPS 2010 Gilbert Leung, Novi Quadrianto, Kostas Tsioutsiouliklis, Alex J. Smola

We present a fast online solver for large scale maximum-flow problems as they occur in portfolio optimization, inventory management, computer vision, and logistics.

Management Portfolio Optimization

Parallelized Stochastic Gradient Descent

no code implementations NeurIPS 2010 Martin Zinkevich, Markus Weimer, Lihong Li, Alex J. Smola

With the increase in available data parallel machine learning has become an increasingly pressing problem.

Multitask Learning without Label Correspondences

no code implementations NeurIPS 2010 Novi Quadrianto, James Petterson, Tibério S. Caetano, Alex J. Smola, S. V. N. Vishwanathan

We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available.

Data Integration General Classification

Word Features for Latent Dirichlet Allocation

no code implementations NeurIPS 2010 James Petterson, Wray Buntine, Shravan M. Narayanamurthy, Tibério S. Caetano, Alex J. Smola

We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the encoding of side information in the distribution over words.

Slow Learners are Fast

no code implementations NeurIPS 2009 Martin Zinkevich, John Langford, Alex J. Smola

Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems.

Distribution Matching for Transduction

no code implementations NeurIPS 2009 Novi Quadrianto, James Petterson, Alex J. Smola

Many transductive inference algorithms assume that distributions over training and test estimates should be related, e. g. by providing a large margin of separation on both sets.

General Classification regression

Kernelized Sorting

no code implementations NeurIPS 2008 Novi Quadrianto, Le Song, Alex J. Smola

Object matching is a fundamental operation in data analysis.

Robust Near-Isometric Matching via Structured Learning of Graphical Models

no code implementations NeurIPS 2008 Alex J. Smola, Julian J. McAuley, Tibério S. Caetano

Models for near-rigid shape matching are typically based on distance-related features, in order to infer matches that are consistent with the isometric assumption.

Structured Prediction

Kernel Measures of Independence for non-iid Data

no code implementations NeurIPS 2008 Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola

Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion.

BIG-bench Machine Learning Clustering

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