Research
A fundamental goal in data analysis is learning which actions (i.e. interventions) are optimal for producing a desired outcome within specific individuals or over an entire population. While advances in reinforcement learning, Bayesian optimization, and…
Quadratic assignment is a very general problem in theoretical computer science. It includes graph matching, the traveling salesman problem, and the Gromov-Hausdorff distance between finite metric spaces as particular cases. Quadratic assignment is in general…
Stochastic gradient-based methods are the state-of-the-art in large-scale machine learning optimization due to their extremely efficient per-iteration computational cost. Second-order methods, that use the second derivative of the optimization objective, are known to enable faster…
Algorithms are sometimes used in strategic or noisy environments. These factors can completely change the solutions of the problems. In this talk, I am going to talk about two projects. In the first one, we…
Developing reliable numerical software has traditionally been a tedious process which requires significant expertise. Recently, our team at the University of Washington has been investigating how tools may lower the barrier to entry for non-experts.…
In this talk I will discuss the power of simple, randomized methods such as hashing, importance sampling, and stochastic iteration in data science and machine learning. In particular, I will overview my efforts to apply…
Facility location problems arise in a wide range of applications such as plant or warehouse location problems and network design problems, and have been widely studied in Computer Science and Operations Research literature. These problems…
We study the problem of recovering a low-rank matrix from linear measurements using an over-parameterized model. We show that the gradient descent process on the square loss function, starting from a small initialization, can converge…
