Hongseok Yang

1. An Introduction to Probabilistic Programming
   2018/09/27 by Jan-Willem van de Meent, van de Meent, Jan-Willem, Brooks Paige +5 · 4 voices · 10 citations
   #stat.ML #cs.AI #cs.LG #cs.PL
2. On Nesting Monte Carlo Estimators
   2017/09/18 by Tom Rainforth, Rainforth, Tom, Robert Cornish +7 · 15 citations
   Computer Science · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms #Mathematical Approximation and Integration #Methodology (stat.ME) #Statistical Methods and Inference
3. LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
   2022/02/28 by Geon-Hyeong Kim, Jongmin Lee, Kim, Geon-Hyeong +7 · 6 citations
   Computer Science · Decision Sciences · #Machine Learning and Algorithms #Domain Adaptation and Few-Shot Learning #Advanced Bandit Algorithms Research
4. Regularizing Towards Soft Equivariance Under Mixed Symmetries
   2023/06/01 by Hyunsu Kim, Kim, Hyunsu, Hyungi Lee +5 · 5 citations
   Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
5. On Correctness of Automatic Differentiation for Non-Differentiable Functions
   2020/06/11 by Wonyeol Lee, Lee, Wonyeol, Hangyeol Yu +6 · 2 citations
   Computer Science · Physics and Astronomy · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Polynomial and algebraic computation
6. Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
   2022/08/22 by Wonyeol Lee, Lee, Wonyeol, Xavier Rival +3 · 2 citations
   Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #Programming Languages (cs.PL)
7. Particle Gibbs with Ancestor Sampling for Probabilistic Programs
   2015/01/27 by Jan-Willem van de Meent, van de Meent, Jan-Willem, Hongseok Yang +5 · 2 citations
   Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Machine Learning and Algorithms #Programming Languages (cs.PL)
8. Scale Mixtures of Neural Network Gaussian Processes
   2021/07/03 by Hyungi Lee, EungGu Yun, Lee, Hyungi +5 · 1 citation
   Computer Science · #Gaussian Processes and Bayesian Inference #Stochastic Gradient Optimization Techniques #Neural Networks and Applications
9. An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
   2023/12/06 by Taeyoung Kim, Hongseok Yang, Kim, Taeyoung +1 · 1 citation
   Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and ELM #Neural Networks and Applications
10. Formalizing Flag Algebras in Lean
    2026/07/26 by Gyeongwon Jeong, Seonghun Park, Jihoon Hyun +2
    #cs.LO #cs.AI #cs.PL #math.CO