Publications de Young Jun Ko | EPFL Graph Search

Revealing the Surface Chemistry for CO2 Hydrogenation on Cu/CeO2–x Using Near-Ambient-Pressure X-ray Photoelectron Spectroscopy

Emad Oveisi, Young Jun Ko, Thi Ha My Pham, Andreas Züttel, Wen Luo, Mo Li

Catalytic reduction of CO2 to valuable products is an attractive route for CO2 recycling. CeO2-supported Cu catalysts have shown high activity and selectivity for the hydrogenation of CO2 to CO. To uncover the origin of their high performance, we prepared ...

ACS2021

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Matthias Grossglauser, Lucas Maystre, Young Jun Ko

Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...

2016

Expectation Propagation for Rectified Linear Poisson Regression

Young Jun Ko, Matthias Seeger

The Poisson likelihood with rectified linear function as non-linearity is a physically plausible model to discribe the stochastic arrival process of photons or other particles at a detector. At low emission rates the discrete nature of this process leads t ...

2015

Scalable Collaborative Bayesian Preference Learning

Young Jun Ko, Matthias Seeger, Mohammad Emtiyaz Khan

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

2014

Variational Gaussian Inference for Bilinear Models of Count Data

Young Jun Ko, Mohammad Emtiyaz Khan

Bilinear models of count data with Poisson distribution are popular in applications such as matrix factorization for recommendation systems, modeling of receptive fields of sensory neurons, and modeling of neural-spike trains. Bayesian inference in such mo ...

2014

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models

Young Jun Ko, Matthias Seeger

Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, be ...

2012