Optimising Density Computations in Probabilistic Programs via Automatic Loop Vectorisation
Sangho Lim, Hyoungjin Lim, Wonyeol Lee, Xavier Rival, Hongseok Yang
POPL 2026 (ACM Symposium on Principles of Programming Languages) [포스텍 네 번째 POPL 논문]
[code]
Floating-Point Neural Networks Are Provably Robust Universal Approximators
Geonho Hwang*, Wonyeol Lee*, Yeachan Park, Sejun Park, Feras Saad
CAV 2025 (International Conference on Computer Aided Verification) [포스텍 첫 번째 CAV 정규논문]
[paper | slides | code]
Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions
Geonho Hwang, Yeachan Park, Wonyeol Lee, Sejun Park
ICML 2025 (International Conference on Machine Learning)
[paper]
Random Variate Generation with Formal Guarantees
Feras Saad, Wonyeol Lee
PLDI 2025 (ACM Conference on Programming Language Design and Implementation) [포스텍 첫 번째 PLDI 논문]
[paper | slides | video | code]
Semantics of Integrating and Differentiating Singularities
Jesse Michel, Wonyeol Lee†, Hongseok Yang
PLDI 2025 (ACM Conference on Programming Language Design and Implementation) [포스텍 첫 번째 PLDI 논문]
[paper | slides | video | code]
What Does Automatic Differentiation Compute for Neural Networks?
Sejun Park, Sanghyuk Chun, Wonyeol Lee
ICLR 2024 (Spotlight) (International Conference on Learning Representations)
[paper | code]
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Wonyeol Lee, Sejun Park, Alex Aiken
ICML 2023 (International Conference on Machine Learning)
[paper | slides]
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
Wonyeol Lee, Xavier Rival, Hongseok Yang
POPL 2023 (ACM Symposium on Principles of Programming Languages)
[paper | slides | video | code]
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang
NeurIPS 2020 (Spotlight) (Annual Conference on Neural Information Processing Systems)
[paper | slides | video]
Differentiable Algorithm for Marginalising Changepoints
Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee, Hongseok Yang
AAAI 2020 (AAAI Conference on Artificial Intelligence)
[paper]
Towards Verified Stochastic Variational Inference for Probabilistic Programs
Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang
POPL 2020 (ACM Symposium on Principles of Programming Languages) [카이스트 세 번째 POPL 논문]
[paper | slides | video | code]
Reparameterization Gradient for Non-Differentiable Models
Wonyeol Lee, Hangyeol Yu, Hongseok Yang
NeurIPS 2018 (Annual Conference on Neural Information Processing Systems)
[paper | slides | code]
On Automatically Proving the Correctness of math.h Implementations
Wonyeol Lee, Rahul Sharma, Alex Aiken
POPL 2018 (ACM Symposium on Principles of Programming Languages)
[paper | slides (short) | video]
Verifying Bit-Manipulations of Floating-Point
Wonyeol Lee, Rahul Sharma, Alex Aiken
PLDI 2016 (ACM Conference on Programming Language Design and Implementation)
[paper | slides | video]
A Proof System for Separation Logic with Magic Wand
Wonyeol Lee, Sungwoo Park
POPL 2014 (ACM Symposium on Principles of Programming Languages) [포스텍 두 번째 POPL 논문]
[paper]
CT-IC: Continuously Activated and Time-Restricted Independent Cascade Model for Viral Marketing
Wonyeol Lee, Jinha Kim, Hwanjo Yu
ICDM 2012 (IEEE International Conference on Data Mining)
[paper | journal | slides]
Edge Detection Using Morphological Amoebas in Noisy Images
Wonyeol Lee, Seyun Kim, Youngwoo Kim, Jaeyoung Lim, Dong Hoon Lim
ICIP 2009 (IEEE International Conference on Image Processing)
[paper | journal]
Expressive Power of ReLU and Step Networks under Floating-Point Operations
Yeachan Park, Geonho Hwang, Wonyeol Lee, Sejun Park
Neural Networks, 2024
[paper]
Training with Mixed-Precision Floating-Point Assignments
Wonyeol Lee, Rahul Sharma, Alex Aiken
TMLR, 2023 (Transactions on Machine Learning Research)
[paper | code]
Reasoning About Floating Point in Real-World Systems
Wonyeol Lee
PhD Dissertation, 2023 (Stanford University)
[paper | slides]