In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After each point is chosen,â€¦ (More)

In an online decision problem, one makes a sequence of decisions without knowledge of the future. Each period, one pays a cost based on the decision and observed state. We give a simple approach forâ€¦ (More)

We study a general online convex optimization problem. We have a convex set <i>S</i> and an unknown sequence of cost functions <i>c</i><inf>1</inf>, <i>c</i><inf>2</inf>,..., and in each period, weâ€¦ (More)

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectorsâ€¦ (More)

We introduce an algorithm that, given n objects, learns a similarity matrix over all n pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-basedâ€¦ (More)

High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommendations mayâ€¦ (More)