Neural Information Processing Systems http://nips.cc/

Time series are ubiquitous in many classification/regression applications. However, the time series data in real applications may contain many missing values. Hence, given multiple (possibly correlated) time series data, it is important to fill in missing values and at the same time to predict their class labels. Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose a novel method, called BRITS, based on recurrent neural networks for missing value imputation in time series data.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Time series are ubiquitous in many classification/regression applications. However, the time series data in real applications may contain many missing values. Hence, given multiple (possibly correlated) time series data, it is important to fill in missing values and at the same time to predict their class labels. Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose a novel method, called BRITS, based on recurrent neural networks for missing value imputation in time series data.

In this paper, we study the generalization performance of multi-class classification and obtain a shaper data-dependent generalization error bound with fast convergence rate, substantially improving the state-of-art bounds in the existing data-dependent generalization analysis. The theoretical analysis motivates us to devise two effective multi-class kernel learning algorithms with statistical guarantees. Experimental results show that our proposed methods can significantly outperform the existing multi-class classification methods.

In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our framework enables a much larger class of reward functions such as the max() function and nonlinear utility functions. Existing techniques relying on accurate estimations of the means of random variables, such as the upper confidence bound (UCB) technique, do not work directly on these functions. We propose a new algorithm called stochastically dominant confidence bound (SDCB), which estimates the distributions of underlying random variables and their stochastically dominant confidence bounds. We prove that SDCB can achieve O(log T) distribution-dependent regret and Õ( T) distribution-independent regret, where T is the time horizon. We apply our results to the K-MAX problem and expected utility maximization problems. In particular, for K-MAX, we provide the first polynomial-time approximation scheme (PTAS) for its offline problem, and give the first Õ( T) bound on the (1 ɛ)-approximation regret of its online problem, for any ɛ > 0.

The greedy algorithm is extensively studied in the field of combinatorial optimization for decades. In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time. We first propose the greedy regret and ɛ-quasi greedy regret as learning metrics comparing with the performance of offline greedy algorithm. We then propose two online greedy learning algorithms with semi-bandit feedbacks, which use multi-armed bandit and pure exploration bandit policies at each level of greedy learning, one for each of the regret metrics respectively. Both algorithms achieve O(log T) problem-dependent regret bound (T being the time horizon) for a general class of combinatorial structures and reward functions that allow greedy solutions. We further show that the bound is tight in T and other problem instance parameters.