Nec Corporation

Ammar Shaker

Francesco Alesiani

Anil Goyal

Xiao He

Shujian Yu

Christoph Gaertner

A method for lifelong machine learning using boosting includes receiving a new task and a learning sample for the new task. A distribution of weights is learned over the learning sample using previously learned classifiers from old tasks. A set of task-specific classifiers are learned for the new task using a boosting algorithm and the distribution of weights over the learning sample, whereby the distribution of weights over the learning sample is updated using the task-specific classifiers for the new task.

Machine learning knowledge management based on lifelong boosting in presence of less data

A method for measuring relatedness between prediction tasks includes receiving data for a first prediction task. The method further includes measuring the relatedness of the first prediction task to at least one previous prediction task as a difference between divergence of conditional probabilities of the tasks. The method can be advantageously applied in artificial intelligence or continual learning systems.

Measuring relatedness between prediction tasks in artificial intelligence and continual learning systems

A method for scalable multi-task learning with convex clustering includes: extracting features from a dataset of a plurality of tasks; generating a graph from the extracted features, nodes of the graph representing linear learning models, each of the linear learning models being for one of the tasks; constraining the graph using convex clustering to generate a convex cluster constrained graph; and obtaining a global solution by minimizing a graph variable loss function, the minimizing the graph variable loss function comprising: introducing auxiliary variables for each connection between nodes in the convex cluster constrained graph; iteratively performing the following operations until convergence: updating the linear learning models by solving a sparse linear system; and updating the auxiliary variables by solving an equation having the auxiliary variables each be proportional to a vector norm for their respective nodes.

Method and system for scalable multi-task learning with convex clustering

A method of continual learning in an artificial intelligence system through bi-level optimization includes providing a stored data sample of a current task and providing a neural network subdivided into two parts including a parameter part and a hyper-parameter part. The method further includes performing bi-level optimization by separately training the two parts of the neural network. The neural network has been trained, prior to the bi-level optimization, on data samples of previous tasks.

Continual learning of artificial intelligence systems based on bi-level optimization

A method for online learning from a data stream with an ensemble of meta-trees includes: observing a data instance from the data stream; for each of the meta-trees, replicating the data instance to generate a number of replicated data instances; for each of the meta-trees, updating meta-tree components using the number or replicated data instances; and inducing each of the meta-trees based on the data instance and the updated meta-tree components. Inducing each of the meta-trees includes employing inequality concentration bound to determine whether a split criterion is satisfied.

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Heidelberg, Germany

Ammar Shaker