We study the problem of fairly allocating a set of indivisible items among $n$ agents. Typically, the literature has focused on one-shot algorithms. In this talk we depart from this paradigm and allow items to arrive online. When an item arrives we must immediately and irrevocably allocate it to an agent. A paradigmatic example is that of food banks: food donations arrive, and must be delivered to nonprofit organizations such as food pantries and soup kitchens.
Policy gradient methods for reinforcement learning and continuous control are popular in practice and have helped recent advances in robotic navigation and in game playing. However, they lack theoretical guarantees even for the simplest case of linear dynamics and a quadratic cost, the Linear Quadratic Regulator (LQR) problem. A difficulty is that unlike the classical approaches to LQR, these methods must solve a nonconvex optimization problem to find the optimal control policy.
Today, most application developers write code without much regard for how quickly it will run. Moreover, once the code is written, it is rare for it to be reengineered to run faster. But two technology trends of historic proportions are instigating a resurgence in software performance engineering, the art of making code run fast. The first is the emergence of cloud computing, where the economics of renting computation, as opposed to buying it, heightens the utility of application speed.
Latent variable models are widely used in applications ranging from natural language processing to recommender systems. Exact inference using maximum likelihood for these models is generally NP-hard, and computationally prohibitive on big and/or high-dimensional data. This has motivated the development of approximate inference methods that balance between computational complexity and statistical efficiency. Understanding the computational and statistical tradeoff is important for analyzing approximate inference approaches as well as designing new ones.
Controlled experiments, or A/B tests, are the gold standard for optimizing websites. From Amazon's checkout flow to Google's search results, A/B tests can help companies improve workflows to download software, sign-up for subscriptions, click on content, or make a purchase. But, controlled experiments have roots far beyond optimizing websites for conversion. In its simplest form, controlled experiments help establish a causal relationship of a change for a target audience.