In this position paper we introduce Active In-ference, a paradigm for intelligently request-ing human labels for inference and learning in situations with a finite budget for apply-ing human resources for labeling cases. Many machine learning systems are applied to a stream of instances that can repeat, such as queries entered in a search engine or web pages for potential ad impressions. When a particular instance x can be subject to clas-sification more than once, we have an ad-ditional complication to the budgeted learn-ing setting. In such applications, frequently the distributions will be non-uniform; for in-stance, in the above applications the distri-butions p(x) over examples are highly skewed and thus a few x’s result in a large per...
International audienceIn this paper, we propose to reformulate the active learning problem occurring...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
Pool-based active learning is an important technique that helps reduce labeling efforts within a poo...
We present a framework for active inference, the selective acquisition of labels for cases at predic...
International audienceThis paper addresses stream-based active learning for classification. We propo...
In this paper, we propose a new research problem on active learning from data streams, where data vo...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
International audienceMislabelling is a critical problem for stream-based active learning methods be...
This paper analyses alternative techniques for deploying low-cost human resources for data acquisiti...
124 pagesThis dissertation focuses on sequential decision making for active learning and inference i...
Online active learning is a paradigm in machine learning that aims to select the most informative da...
This thesis focuses on machine learning for data classification. To reduce the labelling cost, activ...
In literature, learning with expert advice methods usually assume that a learner always obtain the t...
International audienceMost existing active learning methods for classification, assume that the obse...
As technology evolves and electronic devices become widespread, the amount of data produced in the f...
International audienceIn this paper, we propose to reformulate the active learning problem occurring...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
Pool-based active learning is an important technique that helps reduce labeling efforts within a poo...
We present a framework for active inference, the selective acquisition of labels for cases at predic...
International audienceThis paper addresses stream-based active learning for classification. We propo...
In this paper, we propose a new research problem on active learning from data streams, where data vo...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
International audienceMislabelling is a critical problem for stream-based active learning methods be...
This paper analyses alternative techniques for deploying low-cost human resources for data acquisiti...
124 pagesThis dissertation focuses on sequential decision making for active learning and inference i...
Online active learning is a paradigm in machine learning that aims to select the most informative da...
This thesis focuses on machine learning for data classification. To reduce the labelling cost, activ...
In literature, learning with expert advice methods usually assume that a learner always obtain the t...
International audienceMost existing active learning methods for classification, assume that the obse...
As technology evolves and electronic devices become widespread, the amount of data produced in the f...
International audienceIn this paper, we propose to reformulate the active learning problem occurring...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
Pool-based active learning is an important technique that helps reduce labeling efforts within a poo...