Christopher Tosh

Columbia University
Data Science Institute

Academic CV
Google Scholar
E-mail: c DOT tosh AT columbia DOT edu

I am a postdoc in the Data Science Institute at Columbia University. Previously, I was a graduate student at UCSD, advised by Sanjoy Dasgupta.

Publication(s)

C. Tosh and D. Hsu. Diameter-based interactive structure discovery.
Twenty-third International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [Full version]

C. Tosh and S. Dasgupta. The relative complexity of maximum likelihood estimation, MAP estimation, and sampling.
Conference on Learning Theory (COLT), 2019.

C. Tosh and S. Dasgupta. Interactive structure learning with structural query-by-committee.
Neural Information Processing Systems (NeurIPS), 2018. [Full version]

C. Tosh and S. Dasgupta. Maximum likelihood estimation for mixtures of spherical Gaussians is NP-hard.
Journal of Machine Learning Research, 18(175):1-11, 2018.

C. Tosh and S. Dasgupta. Diameter-based active learning.
Thirty-fourth International Conference on Machine Learning (ICML), 2017. [Full version]

C. Tosh. Mixing Rates for the alternating Gibbs sampler over Restricted Boltzmann Machines and friends.
Thirty-third International Conference on Machine Learning (ICML), 2016. [Full version]

C. Tosh and S. Dasgupta. Lower bounds for the Gibbs sampler over mixtures of Gaussians.
Thirty-first International Conference on Machine Learning (ICML), 2014. [Full version]