Research
Neural networks have become the main workhorse of supervised learning, and their efficient training is an important technical challenge which has received a lot of attention. While stochastic gradient descent (SGD) with momentum works well…
Many real-world problems require making decisions that involve multiple possibly conflicting objectives. To succeed in such tasks, intelligent agents need algorithms that can efficiently find different ways of balancing the trade-offs that such objectives present.…
We proposed a doubly stochastic primal-dual coordinate optimization algorithm for regularized empirical risk minimization that can be formulated as a saddle-point problem using convex conjugate functions. Different from the existing coordinate methods, the proposed method…
By Jeannette M. Wing, corporate vice president, Microsoft Research “Not in my lifetime.” That’s what I said when I was asked whether we would ever see computer science taught in K-12. It was 2009, and…
In this talk I provide an overview of information-theoretic bounded-rationality for planning in sequential decision problems. I show how to ground the theory on a stochastic computation model for large-scale choice spaces and then derive…
