In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Potential games and decentralised partially observable MDPs (Dec–POMDPs) are two commonly used models of multi–agent interaction, for static optimisation and sequential decision– making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player’s observations of others’ actions are perturbed by random noise, and use this algorithm to construct an online learning method for solving Dec–POMDPs. Specifically, we prove that noise in observations prevents standard fictitious play from converging to Nash equilibrium in potential games, which also makes fictitious play impractical for solving Dec–POMDPs. To combat this, we derive filtered fictitious play, and provide conditions under which it converges to a Nash equilibrium in potential games with noisy observations. We then use filtered fictitious play to construct a solver for Dec–POMDPs, and demonstrate our new algorithm’s performance in a box pushing problem. Our results show that we consistently outperform the state–of–the–art Dec– POMDP solver by an average of 100% across the range of noise in the observation function.
Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs

We envision a machine learning service provider facing a continuous stream of problems with the same input domain, but with output domains that may differ. Clients present the provider with problems implicitly, by labeling a few example inputs, and then ask the provider to train models which reasonably extend their labelings to novel inputs. The provider wants to avoid constraining its users to a set of common labels, so it does not assume any particular correspondence between labels for a new task and labels for previously encountered tasks. To perform well in this setting, the provider needs a representation of the input domain which, in expectation, permits effective models for new problems to be learned efficiently from a small number of examples. While this bears a resemblance to settings considered in previous work on multitask and lifelong learning, our non-assumption of inter-task label correspondence leads to a novel algorithm: Lifelong Learner of Discriminative Representations (LLDR), which explicitly minimizes a proxy for the intra-task small-sample generalization error. We examine the relative benefits of our approach on a diverse set of real-world datasets in three significant scenarios: representation learning, multitask learning and lifelong learning.
Lifelong Learning of Discriminative Representations

<lb>We consider the problem of subspace clustering: given points that lie on or near the union of<lb>many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies<lb>sets of points close to the same subspace and uses the sets to estimate the subspaces. As the<lb>geometric structure of the clusters (linear subspaces) forbids proper performance of general<lb>distance based approaches such as K-means, many model-specific methods have been proposed.<lb>In this paper, we provide new simple and efficient algorithms for this problem. Our statistical<lb>analysis shows that the algorithms are guaranteed exact (perfect) clustering performance under<lb>certain conditions on the number of points and the affinity between subspaces. These conditions<lb>are weaker than those considered in the standard statistical literature. Experimental results on<lb>synthetic data generated from the standard unions of subspaces model demonstrate our theory.<lb>We also show that our algorithm performs competitively against state-of-the-art algorithms on<lb>real-world applications such as motion segmentation and face clustering, with much simpler<lb>implementation and lower computational cost.

Greedy Subspace Clustering