TASK DEFINITION: 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.
PROBLEM: This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting, we first derive problem-specific regret lower bounds, and analyze how these bounds scale with the dimension of the decision space. We then propose COMBUCB, algorithms that efficiently exploit the combinatorial structure of the problem, and derive finite-time upper bound on their regrets. These bounds improve over regret upper bounds of existing algorithms, and we show numerically thatCOMBUCB significantly outperforms any other algorithm. In the adversarial setting, we propose two simple algorithms, namely COMBEXP-1 and COMBEXP-2 for semi-bandit and bandit feedback, respectively. Their regrets have similar scaling as state-of-the-art algorithms, in spite of the simplicity of their implementation.

SOLUTION: Stochastic and Adversarial Combinatorial Bandits

PROBLEM: Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.

SOLUTION: Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

PROBLEM: We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets. Like all algorithms for this task, ours necessarily has worst-case complexity exponential in the dimension of the data. However, our algorithm packages the computationally hard step into a concisely defined integer program, which can be solved non-privately using standard solvers. We prove accuracy and privacy theorems for our algorithm, and then demonstrate experimentally that our algorithm performs well in practice. For example, our algorithm can efficiently and accurately answer millions of queries on the Netflix dataset, which has over 17,000 attributes; this is an improvement on the state of the art by multiple orders of magnitude.

SOLUTION:
Dual Query: Practical Private Query Release for High Dimensional Data