Juho Lee

Juho Lee

Bayesian nonparametric models, random graphs

I’m a postdoc working with François Caron on Bayesian nonparametric models and random graphs. Before joining Oxford, I completed my PhD in computer science & engineering at POSTECH, South Korea. My thesis was about developing efficient posterior inference algorithms for Bayesian nonparametric models. I’m also interested in Bayesian deep learning and deep learning aided Bayesian modelling, especially with Bayesian nonparametric models.

Publications

2019

  • J. Lee , Y. Lee , J. Kim , A. Kosiorek , S. Choi , Y. W. Teh , Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks, in International Conference on Machine Learning (ICML), 2019.
    Project: bigbayes
  • J. Lee , L. James , S. Choi , F. Caron , A Bayesian model for sparse graphs with flexible degree distributionand overlapping community structure, in Artificial Intelligence and Statistics (AISTATS), 2019.
    Project: bigbayes
  • F. Ayed , J. Lee , F. Caron , Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior, 2019.
  • H. Xing , G. Nicholls , J. Lee , Calibrated Approximate Bayesian Inference, in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, 2019, 6912–6920.

2018

  • J. Heo , H. Lee , S. Kim , J. Lee , K. Kim , E. Yang , S. Hwang , Uncertainty-aware attention for reliable interpretation and prediction, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
    Project: bigbayes
  • H. Lee , J. Lee , S. Kim , E. Yang , S. Hwang , DropMax: adaptive variational softmax, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
    Project: bigbayes
  • J. Lee , G. Nicholls , R. Ryder , Calibration procedures for approximate Bayesian credible sets, Bayesian Analysis, to appear, arXiv:1810.06433, 2018.

2017

  • J. Lee , C. Heakulani , Z. Ghahramani , L. F. James , S. Choi , Bayesian inference on random simple graphs with power law degree distributions, in International Conference on Machine Learning (ICML), 2017.

2016

  • J. Lee , L. F. James , S. Choi , Finite-dimensional BFRY priors and variational Bayesian inference for power law models, in Advances in Neural Information Processing Systems (NeurIPS), 2016.

2015

  • J. Lee , S. Choi , Tree-guided MCMC inference for normalized random measure mixture models, in Advances in Neural Information Processing Systems (NeurIPS), 2015.
  • J. Lee , S. Choi , Bayesian hierarchical clustering with exponential family: small-variance asymptotics and reducibility, in Artificial Intelligence and Statistics (AISTATS), 2015.

2014

  • J. Lee , S. Choi , Incremental tree-based inference with dependent normalized random measures, in Artificial Intelligence and Statistics (AISTATS), 2014.