Nan Ding

Introduction

I am a research scientist at Google. I received my Ph.D. degree from the Department of Computer Science at Purdue University in May 2013. Before that, I obtained a Master's degree from Purdue University in December 2010 and a Bachelor's degree from the Department of Electronic Engineering at Tsinghua University in June 2008.

Research Interests

My primary research interests are machine learning and quantum computation.

Publications

[Google Scholar]

[Semantic Scholar]

Selected Papers (Prior to 2019)

P Sharma, N Ding, S Goodman, R Soricut

Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset for Automatic Image Captioning

Annual Meeting of the Association for Computational Linguistics (ACL), 2018

S Boixo, S Isakov, V Smelyanskiy, R Babbush, N Ding, Z Jiang, J Martinis, H Neven

Characterizing Quantum Supremacy in Near-Term Devices

Nature Physics 14(6), 595–600, 2018

N Ding, R Soricut

Cold-Start Reinforcement Learning with Softmax Policy Gradient

Advances in Neural Information Processing Systems (NIPS), 2017.

V Denchev, S Boixo, S Isakov, N Ding, R Babbush, V Smelyanskiy, J Martinis, H Neven

What is the Computational Value of Finite-Range Tunneling?

Physical Review X 6 (3), 031015, 2016.

C Chen, N Ding, L Carin

On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators

Advances in Neural Information Processing Systems (NIPS), 2015.

N Ding*, Y Fang*, R Babbush, C Chen, R D Skeel, H Neven (* = equal)

Bayesian Sampling using Stochastic Gradient Thermostats

Advances in Neural Information Processing Systems (NIPS), 2014.

J Deng, N Ding, Y Jia, A Frome, K P Murphy, S Bengio, Y Li, H Neven, H Adam

Large-scale Object Classification using Label Relation Graphs

European Conference on Computer Vision (ECCV), 2014.

(ECCV 2014 Best Paper Award)

N Ding

Statistical Machine Learning in the t-exponential Family of Distributions

Ph.D. Dissertation. Purdue University, 2013. [PDF]

N Ding, S V N Vishwanathan, M Warmuth, V Denchev

T-logistic Regression for Binary and Multiclass Classification

Technical Report, 2013. [PDF]

V Denchev, N Ding, S V N Vishwanathan, H Neven

Robust Classification with Adiabatic Quantum Optimization

International Conference on Machine Learning (ICML), 2012.

N Ding, S V N Vishwanathan, Y Qi

t-divergence Based Approximate Inference

Advances in Neural Information Processing Systems (NIPS), 2011.

N Ding, S V N Vishwanathan

t-logistic Regression

Advances in Neural Information Processing Systems (NIPS), 2010.