Deep reinforcement learning methods are behind some of the most publicized recent results in machine learning. In spite of these successes, however, deep RL methods face a number of systemic issues: brittleness to small changes…
We study high-dimensional estimation in a setting where an adversary is allowed to arbitrarily corrupt an $\varepsilon$-fraction of the samples. Such questions have a rich history spanning statistics, machine learning and theoretical computer science. Even…
The usability and practicality of machine learning are largely influenced by two critical factors: low latency and low cost. However, achieving low latency and low cost is very challenging when machine learning depends on real-world…
Data lakes (e.g., enterprise data catalogs and Open Data portals) are data dumps if users cannot find and utilize the data in them. In this talk, I present two problems in massive, dynamic data lakes:…
