In practical applications, it is often observed that high-dimensional features can yield good performance, while being more costly in both computation and storage. In this paper, we propose a novel method called Bayesian Hashing to learn an optimal Hamming embedding of high-dimensional features, with a focus on the challenging application of face recognition. In particular, a boosted random FERNs classification model is designed to perform efficient face recognition, in which bit correlations are elaborately approximated with a random permutation technique. Without incurring additional storage cost, multiple random permutations are then employed to train a series of classifiers for achieving better discrimination power. In addition, we introduce a sequential forward floating search (SFFS) algorithm to perform model selection, resulting in further performance improvement. Extensive experimental evaluations and comparative studies clearly demonstrate that the proposed Bayesian Hashing approach outperforms other peer methods in both accuracy and speed. We achieve state-of-the-art results on well-known face recognition benchmarks using compact binary codes with significantly reduced computational overload and storage cost.

Most logic-based machine learning algorithms rely on an Occamist bias where textual complexity of hypotheses is minimised. Within Inductive Logic Programming (ILP), this approach fails to distinguish between the efficiencies of hypothesised programs, such as quick sort (O(n log n)) and bubble sort (O(n 2 )). This paper addresses this issue by considering techniques to minimise both the textual complexity and resource complexity of hypothesised robot strategies. We develop a general framework for the problem of minimising resource complexity and show that on two robot strategy problems, 1) Postman 2) Sorter (recursively sort letters for delivery), the theoretical resource complexities of optimal strategies vary depending on whether objects can be composed within a strategy. The approach considered is an extension of Meta-Interpretive Learning (MIL), a recently developed paradigm in ILP which supports predicate invention and the learning of recursive logic programs. We introduce a new MIL implementation, Metagol O , and prove its convergence, with increasing numbers of randomly chosen examples to optimal strategies of this kind. Our experiments show that Metagol O learns theoretically optimal robot sorting strategies, which is in agreement with the theoretical predictions showing a clear divergence in resource requirements as the number of objects grows. To the authors’ knowledge this paper is the first demonstration of a learning algorithm able to learn optimal resource complexity robot strategies and algorithms for sorting lists.

In repeated stochastic games (RSGs), an agent must quickly adapt to the behavior of previously unknown associates, who may themselves be learning. This machine-learning problem is particularly challenging due, in part, to the presence of multiple (even infinite) equilibria and inherently large strategy spaces. In this paper, we introduce a method to reduce the strategy space of two-player general-sum RSGs to a handful of expert strategies. This process, called mega, effectually reduces an RSG to a bandit problem. We show that the resulting strategy space preserves several important properties of the original RSG, thus enabling a learner to produce robust strategies within a reasonably small number of interactions. To better establish strengths and weaknesses of this approach, we empirically evaluate the resulting learning system against other algorithms in three different RSGs.

For particular sparse optimization problems, among them is the popular tasks, its low computation cost of linear subproblem proximal gradient (PG) based approach ([Beck and Teboulle, evaluation on each iteration leads to superior 2009]; [Nesterov, 2013]). This kind of methods can achieve practical performance. However, the inferior iteration the optimal rate of convergence under certain problem settings, complexity incurs excess number of gradient hence it enjoys low iteration complexity. The periteration evaluations, which can counteract the efficiency cost mainly comes from gradient evaluation and a gained by solving low cost linear subproblem. In proximal map (PM) related to the type of the regularizer. On this paper, we therefore propose a novel algorithm the one hand, due to the optimal iteration complexity, the that requires optimal graduate evaluations as proximal number of gradient evaluations is optimal for PG method.

Person re-identification concerns the matching of pedestrians across disjoint camera views. Due to the changes of viewpoints, lighting conditions and camera features, images of the same person from different views always appear differently, and thus feature representations across disjoint camera views of the same person follow different distributions. In this work, we propose an effective, low cost and easy-to-apply schema called the Mirror Representation, which embeds the view-specific feature transformation and enables alignment of the feature distributions across disjoint views for the same person. The proposed Mirror Representation is also designed to explicitly model the relation between different view-specific transformations and meanwhile control their discrepancy. With our Mirror Representation, we can enhance existing subspace/metric learning models significantly, and we particularly show that kernel marginal fisher analysis significantly outperforms the current state-of-the-art methods through extensive experiments on VIPeR, PRID450S and CUHK01.

Random feature map is popularly used to scale up kernel methods. However, employing a large number of mapped features to ensure an accurate approximation will still make the training time consuming. In this paper, we aim to improve the training efficiency of shift-invariant kernels by using fewer informative features without sacrificing precision. We propose a novel feature map method by extending Random Kitchen Sinks through fast data-dependent subspace embedding to generate the desired features. More specifically, we describe two algorithms with different tradeoffs on the running speed and accuracy, and prove that O(l) features induced by them are able to perform as accurately as O(l 2 ) features by other feature map methods. In addition, several experiments are conducted on the real-world datasets demonstrating the superiority of our proposed algorithms.

We present a novel model-metric co-learning (MMCL) methodology for sequence classification which learns in the model space -- each data item (sequence) is represented by a predictive model from a carefully designed model class. MMCL learning encourages sequences from the same class to be represented by ‘close’ model representations, well separated from those for different classes. Existing approaches to the problem either fit a single model to all the data, or a (predominantly linear) model on each sequence. We introduce a novel hybrid approach spanning the two extremes. The model class we use is a special form of adaptive high-dimensional non-linear state space model with a highly constrained and simple dynamic part. The dynamic part is identical for all data items and acts as a temporal filter providing a rich pool of dynamic features that can be selectively extracted by individual (static) linear readout mappings representing the sequences. Alongside learning the dynamic part, we also learn the global metric in the model readout space. Experiments on synthetic and benchmark data sets confirm the effectiveness of the algorithm compared to a variety of alternative methods.

We consider the problem of learning the optimal policy of an unknown Markov decision process (MDP) when expert demonstrations are available along with interaction samples. We build on classification-based policy iteration to perform a seamless integration of interaction and expert data, thus obtaining an algorithm which can benefit from both sources of information at the same time. Furthermore, we provide a full theoretical analysis of the performance across iterations providing insights on how the algorithm works. Finally, we report an empirical evaluation of the algorithm and a comparison with the state-of-the-art algorithms.

In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc., which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV shows) and efficiency (efficiently updating the model to reflect the temporal data changes) concerns have to be addressed to design real-world TV show recommendation algorithms. In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features. The cold-start TV shows can be correctly recommended to desired users via a so called space alignment technique. On the other hand, an online updating scheme is developed to utilize new user watching behaviors. We present experimental results on a real TV watch behavior data set to demonstrate the significant performance improvement over other state-of-the-art algorithms.

In this work we evaluate the performance of a policy shaping algorithm using 26 human teachers. We examine if the algorithm is suitable for human-generated data on two different boards in a pac-man domain, comparing performance to an oracle that provides critique based on one known winning policy. Perhaps surprisingly, we show that the data generated by our 26 participants yields even better performance for the agent than data generated by the oracle. This might be because humans do not discourage exploring multiple winning policies. Additionally, we evaluate the impact of different verbal instructions, and different interpretations of silence, finding that the usefulness of data is affected both by what instructions is given to teachers, and how the data is interpreted.