We apply competitive analysis onto the problem of minimizing the number of queries to an oracle to completely reconstruct a given monotone Boolean function. Besides lower and upper bounds on the competitivity we determine optimal deterministic online algorithms for the smallest problem instances.

We derive a one-period look-ahead policy for finiteand infinite-horizon online optimal learning problems with Gaussian rewards. The resulting decision rule easily extends to a variety of settings, including the case where our prior beliefs about the rewards are correlated. Experiments show that the KG policy performs competitively against other learning policies in diverse situations. In the ca...

this paper deals with the problem of user-server assignment in online social network systems. online social network applications such as facebook, twitter, or instagram are built on an infrastructure of servers that enables them to communicate with each other. a key factor that determines the facility of communication between the users and the servers is the expected transmission time (ett). a ...

Learning for maximizing AUC performance is an important research problem in machine learning. Unlike traditional batch learning methods for maximizing AUC which often suffer from poor scalability, recent years have witnessed some emerging studies that attempt to maximize AUC by single-pass online learning approaches. Despite their encouraging results reported, the existing online AUC maximizati...

The efficiency of reinforcement learning algorithms depends critically on a few metaparameters that modulates the learning updates and the trade-off between exploration and exploitation. The adaptation of the meta-parameters is an open question in reinforcement learning, which arguably has become more of an issue recently with the success of deep reinforcement learning in high-dimensional state...

We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second order approximation of the squared spectral norm, which helps us to get a reg...

This paper develops an efficient online algorithm for learning multiple consecutive tasks based on the KSVD algorithm for sparse dictionary optimization. We first derive a batch multi-task learning method that builds upon K-SVD, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current li...