Hongseok Yang
21 papers · 2015–2025 · 7 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (10) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird
🏃
Academic Marathon
(10)
🧭
Keyword Pioneer
🐣
Hot Topic Early Bird
🏆
Grand Slam
🗃️
Keyword Collector
(78)
💎
Century Club
(21)
🔥
Unstoppable
(8)
Conferences
ICML (7)
NIPS (6)
ICLR (3)
AISTATS (2)
AAAI (1)
ACML (1)
JMLR (1)
Top co-authors
Research topics
Keywords
bayesian inference
(5)
variational inference
(5)
markov chain monte carlo
(4)
probabilistic programming
(3)
deep learning
(2)
offline learning
(2)
stochastic variational inference
(2)
variance reduction
(2)
imitation learning
(2)
stationary distribution
(2)
non-differentiable model
(2)
automatic differentiation
(1)
policy learning
(1)
divergence minimization
(1)
feature learning
(1)
motion forecasting
(1)
covariate shift
(1)
deep learning theory
(1)
importance sampling
(1)
time series
(1)
Papers
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
ICLR 2025
Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction
NIPS 2024
An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
ICML 2024
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
ICML 2024
Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility
JMLR 2023
Regularizing Towards Soft Equivariance Under Mixed Symmetries
ICML 2023
Regularized Behavior Cloning for Blocking the Leakage of Past Action Information
NIPS 2023
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
ICLR 2022
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
NIPS 2022
Learning Symmetric Rules with SATNet
NIPS 2022
Scale Mixtures of Neural Network Gaussian Processes
ICLR 2022
Probabilistic Programs with Stochastic Conditioning
ICML 2021
On Correctness of Automatic Differentiation for Non-Differentiable Functions
NIPS 2020
Variational Inference for Sequential Data with Future Likelihood Estimates
ICML 2020
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
ICML 2020
Differentiable Algorithm for Marginalising Changepoints
AAAI 2020
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
AISTATS 2019
Trust Region Sequential Variational Inference
ACML 2019
On Nesting Monte Carlo Estimators
ICML 2018
Reparameterization Gradient for Non-differentiable Models
NIPS 2018
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
AISTATS 2015