Customer-obsessed science

Structured prediction from bandit feedback describes a learning scenario where instead of having access to a gold standard structure, a learner only receives partial feedback in form of the loss value of a predicted structure. We present new learning objectives and algorithms for this interactive scenario, focusing on convergence speed and ease of elicitability of feedback. We present supervised-to-bandit

We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints, which are constraints that need to be satisfied when accumulated over a finite number of rounds T, but can be violated in intermediate rounds. For some user-defined trade-off parameter β ∈ (0, 1), the proposed algorithm achieves cumulative regret bounds of O(T max{β,1−β} ) and

We compare submodular and Jaccard methods to diversify Amazon Music recommendations. Submodularity significantly improves recommendation quality and user engagement. Unlike the Jaccard method, our submodular approach incorporates item relevance score within its optimization function, and produces a relevant and uniformly diverse set.

In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem. The proposed approach is on the Riemannian Grassmann manifold that allows local matrix completion by different agents while achieving asymptotic consensus on the global low-rank factors. The resulting approach is scalable and parallelizable. Our numerical experiments show the good performance of the

We propose two new methods of speech detection in the context of voice-controlled far-field appliances. While conventional detection methods are designed to differentiate between speech and nonspeech, we aim at distinguishing desired speech, which we define as speech originating from the person interacting with the device, from background noise and interfering talkers. Our two proposed methods use the first