In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features should lead to better performance. In the domain of kernel methods, a principled way to use multiple features is the Multi Kernel Learning (MKL) approach. Here we present a MKL optimization algorithm based on stochastic gra...

Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader. In this paper, we present an online algorithm based on a completely different approach, which combines “random playout” and randomized rounding of loss subgradients. As an application of our approach, we provide the first computationally efficient online algorithm for collaborativ...

Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader. In this paper, we present an online algorithm based on a completely different approach, which combines “random playout” and randomized rounding of loss subgradients. As an application of our approach, we provide the first computationally efficient online algorithm for collaborativ...

In this paper, we study convergence and e ciency of the batch estimator and natural gradient algorithm for blind deconvolution. First, the blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. To improve the learning e ciency of the online algorithm, explicit standardized estimating function...

It is well-known that everything that is learnable in the difficult online setting, where an arbitrary sequences of examples must be labeled one at a time, is also learnable in the batch setting, where examples are drawn independently from a distribution. We show a result in the opposite direction. We give an efficient conversion algorithm from batch to online that is transductive: it uses futu...

Topic modeling provides a powerful way to analyze the content of a collection of documents. It has become a popular tool in research areas such as text mining, information retrieval, natural language processing, and other related fields. In realworld applications, however, the usefulness of topic modeling is limited due to scalability issues. Scaling to larger document collections via paralleli...

Factorization Machine (FM) is a supervised learning approach with a powerful capability of feature engineering. It yields state-ofthe-art performance in various batch learning tasks where all the training data is made available prior to the training. However, in real-world applications where the data arrives sequentially in a streaming manner, the high cost of re-training with batch learning al...

The TREC-8 ltering track measures the ability of systems to build persistent user prooles which successfully separate relevant and non-relevant documents. It consists of three major subtasks: adaptive ltering, batch ltering, and routing. In adaptive ltering, the system begins with only a topic statement and must learn a better proole from on-line feedback. Batch ltering and routing are more tra...