Mikhail Yurochkin

PhD candidate

University of Michigan

I am a 5th year PhD student in Statistics at the University of Michigan, advised by Prof. Long Nguyen. I received my bachelor degree in applied mathematics and physics from Moscow Institute of Physics and Technology. I enjoy switching topics of my research, which helps me to build broad expertise of Machine learning and be able to answer a question "here is my data, what do I do?" for wide range of data types and problems. For detailed description of my current and past work please see the Research section. I am interested in Research Scientist type position after completing my PhD.

Research

Current topics of my research: Deep Learning on graph structured data, applications of Wasserstein distance and barycenters, scalable MCMC, Topic Modeling.

News: Two papers accepted at NIPS 2017! Submitted a paper to ICLR 2018 based on my work at Adobe this past summer.


My major research topic is Topic Modeling and Latent Dirichlet Allocation inference. Recasting the inference of topics as a geometric learning a simplex type problem allowed for two new algorithms for topic estimation, both are much faster and as accurate as Gibbs sampler, additionally second algorithm can estimate number of topics, i.e. vertices in the simplex. First algorithm consists of k-means clustering step with a computationally cheap geometric post processing of inferred cluster centroids.

Geometric Dirichlet Means algorithm for topic inference

Yurochkin M. & Nguyen X. [Link, PDF (arXiv), Code]

Second algorithm is joint work with Aritra Guha. By defining a cone hanging at the center of the data and scanning the space of documents using this cone we can both find the topics and estimate their number very accurately. I will present this work at NIPS 2017.

Conic Scan-and-Cover algorithms for nonparametric topic modeling

Yurochkin M., Guha A. & Nguyen X. [Link, PDF (arXiv), Code, NIPS poster]


During summer 2016 internship at LogicBlox (supervised by Nikolaos Vasiloglou) I was working with Factorization Machines and proposed a new Bayesian model for learning high order interactions among variables in the data. The key idea is to represent interactions between variables as a hypergraph, which in turn has corresponding incidence matrix. Incidence matrix is binary and I utilized Indian Buffet Process as a prior on it, additionally proposing a new modification of the IBP to better model interactions. Factorization Machines construction came in handy to be able to estimate coefficients of previously unseen interactions on the fly. I will present this work at NIPS 2017.

Multi-way Interacting Regression via Factorization Machines

Yurochkin M., Nguyen X. & Vasiloglou N. [Link, PDF (arXiv), Code, NIPS poster]


I'm working jointly with Nhat Ho on finding interesting applications of Wasserstein distance and barycenters. While these are very elegant mathematical tools, I believe that we have not yet found a lot of important use cases and fully understood the meaning of barycenter. We published a paper in ICML 2017 about using Wasserstein distances for clustering data with multilevel structure. I was responsible for implementation and simulations design.

Multilevel clustering via Wasserstein means

Ho N., Nguyen X., Yurochkin M., Bui H., Huynh V. & Phung D. [Link, PDF (arXiv), Code]


Reviewer experience: Invited reviewer for ICLR 2018, NIPS 2017 and ICML 2017. Volunteer reviewer for NIPS 2016. Reviewed for Journal of Computational and Graphical Statistics 2016.

Curriculum vitae