Sungsoo Ahn

发表

Gauging variational inference

Jinwoo Shin, Michael Chertkov, Sungsoo Ahn, 2017, NIPS.

A Deeper Look at the Layerwise Sparsity of Magnitude-based Pruning pdf

Jinwoo Shin, Sejun Park, Jaeho Lee, 2020, ArXiv.

Gauged Mini-Bucket Elimination for Approximate Inference

Jinwoo Shin, Michael Chertkov, Adrian Weller, 2018, AISTATS.

Variational Mutual Information Distillation for Transfer Learning

Sungsoo Ahn, 2018 .

Gauge Optimization via ADMM for Approximate Inference

Jinwoo Shin, Sungsoo Ahn, M. Chertkov, 2017 .

Deep Auto-Deferring Policy for Combinatorial Optimization

Jinwoo Shin, Sungsoo Ahn, Younggyo Seo, 2019 .

Learning Debiased Classifier with Biased Committee

Suha Kwak, Sungsoo Ahn, Nayeong Kim, 2022, ArXiv.

Sample Augmented Sample Sampling Contextual Embedding Task Output Update Policy to Maximize Rewards “ Difficult ” “ Not too Different ” Augmentations : probability : magnitude Augmentation Policy

Jinwoo Shin, Sungsoo Ahn, Dongyeop Kang, 2022 .

S PANNING T REE - BASED G RAPH G ENERATION FOR M OLECULES

Sungsoo Ahn, Binghong Chen, Tianzhe Wang, 2022 .

Layer-adaptive Sparsity for the Magnitude-based Pruning

Jinwoo Shin, Sejun Park, Sungsoo Ahn, 2020, ICLR.

Substructure-Atom Cross Attention for Molecular Representation Learning

Jiye G. Kim, Sungsoo Ahn, Seungbeom Lee, 2022, ArXiv.

Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning

Jinwoo Shin, Yung Yi, Sungsoo Ahn, 2022, ICML.

Learning Debiased Classifier with Biased Committee

Suha Kwak, Sungsoo Ahn, Nayeong Kim, 2022, ArXiv.

ournal of Statistical Mechanics : J Theory and Experiment Evolutionary dynamics and statistical physics

Jinwoo Shin, Adrian Weller, Sungsoo Ahn, 2013 .

A Closer Look at the Intervention Procedure of Concept Bottleneck Models

Yohan Jo, Namhoon Lee, Sungsoo Ahn, 2023, 2302.14260.

Variational Information Distillation for Knowledge Transfer

Neil D. Lawrence, Zhenwen Dai, Sungsoo Ahn, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Learning What to Defer for Maximum Independent Sets

Jinwoo Shin, Sungsoo Ahn, Younggyo Seo, 2020, ICML.

Diffusion Probabilistic Models for Graph-Structured Prediction

Sungsoo Ahn, Sangwoo Mo, Hyosoon Jang, 2023, ArXiv.

What Makes Better Augmentation Strategies? Augment Difficult but Not too Different

Jinwoo Shin, Sungsoo Ahn, Dongyeop Kang, 2022, ICLR.

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning

Jinwoo Shin, Eunho Yang, Sung Ju Hwang, 2021, IJCAI.

Self-Improved Retrosynthetic Planning pdf

Jinwoo Shin, Sungsoo Ahn, Hankook Lee, 2021, ICML.

Learning from Failure: De-biasing Classifier from Biased Classifier

Jinwoo Shin, Jaeho Lee, Jun Hyun Nam, 2020, NeurIPS.

Learning from Failure: Training Debiased Classifier from Biased Classifier

Jinwoo Shin, Sungsoo Ahn, Jaeho Lee, 2020, ArXiv.

Guiding Deep Molecular Optimization with Genetic Exploration pdf

Jinwoo Shin, Junsu Kim, Sungsoo Ahn, 2020, NeurIPS.

QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning

Jinwoo Shin, Yung Yi, Kyunghwan Son, 2020 .

RoMA: Robust Model Adaptation for Offline Model-based Optimization pdf

Le Song, Sungsoo Ahn, Jinwoo Shin, 2021, NeurIPS.

A Closer Look at the Intervention Procedure of Concept Bottleneck Models

Yohan Jo, Namhoon Lee, Sungsoo Ahn, 2023, ArXiv.

Diffusion Probabilistic Models for Structured Node Classification

Sungsoo Ahn, Sangwoo Mo, Hyosoon Jang, 2023, NeurIPS.

Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences

Sungsoo Ahn, Jinkyoo Park, Federico Berto, 2023, NeurIPS.

QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning pdf

Jinwoo Shin, Yung Yi, Kyunghwan Son, 2020, ArXiv.

FRAGMENT-BASED MULTI-VIEW MOLECULAR CONTRASTIVE LEARNING

Jinwoo Shin, Sungsoo Ahn, Ja-Ae Nam, 2023 .

EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost

Sungsoo Ahn, Dongwoo Kim, Jaeseung Heo, 2023, ArXiv.

Learning Debiased Classifier with Biased Committee

Suha Kwak, Sungsoo Ahn, Nayeong Kim, 2022, NeurIPS.

Minimum Weight Perfect Matching via Blossom Belief Propagation

Jinwoo Shin, Michael Chertkov, Sejun Park, 2015, NIPS.

Spanning Tree-based Graph Generation for Molecules

Sungsoo Ahn, Binghong Chen, Tianzhe Wang, 2022, ICLR.

Hierarchical Graph Generation with K2-trees

Sungsoo Ahn, Dongwoo Kim, Yunhui Jang, 2023, ArXiv.

Gauging variational inference

Jinwoo Shin, Michael Chertkov, Sungsoo Ahn, 2017, NIPS.

MCMC assisted by Belief Propagaion pdf

Jinwoo Shin, Michael Chertkov, Sungsoo Ahn, 2016, ArXiv.

Synthesis of MCMC and Belief Propagation

Jinwoo Shin, Michael Chertkov, Sungsoo Ahn, 2016, NIPS.

Imitating Graph-Based Planning with Goal-Conditioned Policies

Jinwoo Shin, Younggyo Seo, Sungsoo Ahn, 2023, ICLR.

Symmetric Exploration in Combinatorial Optimization is Free!

Sungsoo Ahn, Jinkyoo Park, Minsu Kim, 2023, arXiv.org.

Bucket renormalization for approximate inference

Jinwoo Shin, Michael Chertkov, Adrian Weller, 2018, ICML.

Diffusion Probabilistic Models for Graph-Structured Prediction

Sungsoo Ahn, Sangwoo Mo, Hyosoon Jang, 2023, ArXiv.

A Closer Look at the Intervention Procedure of Concept Bottleneck Models

Yohan Jo, Namhoon Lee, Sungsoo Ahn, 2023, ICML.

QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning

Jinwoo Shin, Yung Yi, Kyunghwan Son, 2020 .

Learning What to Defer for Maximum Independent Sets

Jinwoo Shin, Younggyo Seo, Sungsoo Ahn, 2020, ICML.

Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization

Sungsoo Ahn, Minsu Kim, Hyeon-Seob Kim, 2023, ICML.