101 research outputs found
A Convex Feature Learning Formulation for Latent Task Structure Discovery
This paper considers the multi-task learning problem and in the setting where
some relevant features could be shared across few related tasks. Most of the
existing methods assume the extent to which the given tasks are related or
share a common feature space to be known apriori. In real-world applications
however, it is desirable to automatically discover the groups of related tasks
that share a feature space. In this paper we aim at searching the exponentially
large space of all possible groups of tasks that may share a feature space. The
main contribution is a convex formulation that employs a graph-based
regularizer and simultaneously discovers few groups of related tasks, having
close-by task parameters, as well as the feature space shared within each
group. The regularizer encodes an important structure among the groups of tasks
leading to an efficient algorithm for solving it: if there is no feature space
under which a group of tasks has close-by task parameters, then there does not
exist such a feature space for any of its supersets. An efficient active set
algorithm that exploits this simplification and performs a clever search in the
exponentially large space is presented. The algorithm is guaranteed to solve
the proposed formulation (within some precision) in a time polynomial in the
number of groups of related tasks discovered. Empirical results on benchmark
datasets show that the proposed formulation achieves good generalization and
outperforms state-of-the-art multi-task learning algorithms in some cases.Comment: ICML201
Learning to Place New Objects
The ability to place objects in the environment is an important skill for a
personal robot. An object should not only be placed stably, but should also be
placed in its preferred location/orientation. For instance, a plate is
preferred to be inserted vertically into the slot of a dish-rack as compared to
be placed horizontally in it. Unstructured environments such as homes have a
large variety of object types as well as of placing areas. Therefore our
algorithms should be able to handle placing new object types and new placing
areas. These reasons make placing a challenging manipulation task. In this
work, we propose a supervised learning algorithm for finding good placements
given the point-clouds of the object and the placing area. It learns to combine
the features that capture support, stability and preferred placements using a
shared sparsity structure in the parameters. Even when neither the object nor
the placing area is seen previously in the training set, our algorithm predicts
good placements. In extensive experiments, our method enables the robot to
stably place several new objects in several new placing areas with 98%
success-rate; and it placed the objects in their preferred placements in 92% of
the cases
Self-Paced Multi-Task Learning
In this paper, we propose a novel multi-task learning (MTL) framework, called
Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating
all tasks and instances equally when training, SPMTL attempts to jointly learn
the tasks by taking into consideration the complexities of both tasks and
instances. This is inspired by the cognitive process of human brain that often
learns from the easy to the hard. We construct a compact SPMTL formulation by
proposing a new task-oriented regularizer that can jointly prioritize the tasks
and the instances. Thus it can be interpreted as a self-paced learner for MTL.
A simple yet effective algorithm is designed for optimizing the proposed
objective function. An error bound for a simplified formulation is also
analyzed theoretically. Experimental results on toy and real-world datasets
demonstrate the effectiveness of the proposed approach, compared to the
state-of-the-art methods
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