Online learning has been in the spotlight from the machine learning society for a long time. To handle massive data in Big Data era, one single learner could never efficiently finish this heavy task. Hence, in this paper, we propose a novel distributed online learning algorithm to solve the problem. Comparing to typical centralized online learner, the distributed learners optimize their own lea...

Several recent efforts in the field of reinforcement learning have focused attention on the importance of regularization, but the techniques for incorporating regularization into reinforcement learning algorithms, and the effects of these changes upon the convergence of these algorithms, are ongoing areas of research. In particular, little has been written about the use of regularization in onl...

Most of the performance bounds for on-line learning algorithms are proven assuming a constant learning rate. To optimize these bounds, the learning rate must be tuned based on quantities that are generally unknown, as they depend on the whole sequence of examples. In this paper we show that essentially the same optimized bounds can be obtained when the algorithms adaptively tune their learning ...

Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns pedestrian motion. Most existing algorithms use offline learning approaches learn such motion behaviors. However, these cannot take advantage streams data that available after training has concluded, typically not generalizable they have seen before. To solv...

We present online nested expectation maximization for model-free reinforcement learning in a POMDP. The algorithm evaluates the policy only in the current learning episode, discarding the episode after the evaluation and memorizing the sufficient statistic, from which the policy is computed in closedform. As a result, the online algorithm has a time complexity O ( n ) and a memory complexity O(...

in this paper, tender problems in an automobile company for procuring needed items from potential suppliers have been resolved by the learning algorithm q. in this case the purchaser with respect to proposals received from potential providers, including price and delivery time is proposed; order the needed parts to suppliers assigns. the buyer’s objective is minimizing the procurement costs thr...

Factorization Machine (FM) is a supervised learning approach with a powerful capability of feature engineering. It yields state-ofthe-art performance in various batch learning tasks where all the training data is made available prior to the training. However, in real-world applications where the data arrives sequentially in a streaming manner, the high cost of re-training with batch learning al...

In this paper, we consider the problem of preserving privacy in the online learning setting. Online learning involves learning from the data in real-time, so that the learned model as well as its outputs are also continuously changing. This makes preserving privacy of each data point significantly more challenging as its effect on the learned model can be easily tracked by changes in the subseq...