Jinwoo Shin

is this you? claim profile

∙

0 followers

∙

KAIST 수리과학과

∙

ibm

Assistant Professor at School of Electrical Engineering at KAIST, Korea, Ph.D. degree from Massachusetts Institute of Technology in 2010, Researcher at Algorithms & Randomness Center, Georgia Institute of Technology from 2010-2012, Business Analytics and Mathematical Sciences Department, IBM T. J. Watson Research from 2012-2013, Kenneth C. Sevcik Award at ACM SIGMETRICS/Performance 2009, Best Publication Award from INFORMS Applied Probability Society 2013, Best Paper Award at ACM MOBIHOC 2013, Bloomberg Scientific Research Award 2015 and ACM SIGMETRICS Rising Star Award 2015.

Featured Co-authors

Bo Li
340 publications
Pieter Abbeel
217 publications
Tie-Yan Liu
166 publications
Chang Liu
166 publications
Tao Qin
128 publications
Honglak Lee
105 publications
Le Song
100 publications
Sung Ju Hwang
80 publications
Adrian Weller
73 publications
Minsu Cho
65 publications
Sewoong Oh
61 publications

∙ 08/10/2022

Robust Continual Test-time Adaptation: Instance-aware BN and Prediction-balanced Memory

Test-time adaptation (TTA) is an emerging paradigm that addresses distri...

Taesik Gong, et al. ∙

∙ 07/19/2022

Time Is MattEr: Temporal Self-supervision for Video Transformers

Understanding temporal dynamics of video is an essential aspect of learn...

Sukmin Yun, et al. ∙

∙ 06/16/2022

Patch-level Representation Learning for Self-supervised Vision Transformers

Recent self-supervised learning (SSL) methods have shown impressive resu...

Sukmin Yun, et al. ∙

∙ 04/05/2022

Zero-shot Blind Image Denoising via Implicit Neural Representations

Recent denoising algorithms based on the "blind-spot" strategy show impr...

Chaewon Kim, et al. ∙

∙ 04/05/2022

Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation

The paradigm of worst-group loss minimization has shown its promise in a...

Junhyun Nam, et al. ∙

∙ 03/18/2022

SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning

Preference-based reinforcement learning (RL) has shown potential for tea...

Jongjin Park, et al. ∙

∙ 02/21/2022

Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

In the deep learning era, long video generation of high-quality still re...

Sihyun Yu, et al. ∙

∙ 12/16/2021

Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing

The Mixup scheme suggests mixing a pair of samples to create an augmente...

Joonhyung Park, et al. ∙

∙ 11/22/2021

DAPPER: Performance Estimation of Domain Adaptation in Mobile Sensing

Many applications that utilize sensors in mobile devices and apply machi...

Taesik Gong, et al. ∙

∙ 11/18/2021

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Recent unsupervised representation learning methods have shown to be eff...

Hankook Lee, et al. ∙

∙ 11/17/2021

SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness

Randomized smoothing is currently a state-of-the-art method to construct...

Jongheon Jeong, et al. ∙

∙ 10/27/2021

Meta-Learning Sparse Implicit Neural Representations

Implicit neural representations are a promising new avenue of representi...

Jaeho Lee, et al. ∙

∙ 10/27/2021

RoMA: Robust Model Adaptation for Offline Model-based Optimization

We consider the problem of searching an input maximizing a black-box obj...

Sihyun Yu, et al. ∙

∙ 10/27/2021

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning

Behavioral cloning has proven to be effective for learning sequential de...

Jongjin Park, et al. ∙

∙ 10/26/2021

Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning

Goal-conditioned hierarchical reinforcement learning (HRL) has shown pro...

Junsu Kim, et al. ∙

∙ 07/30/2021

Object-aware Contrastive Learning for Debiased Scene Representation

Contrastive self-supervised learning has shown impressive results in lea...

Sangwoo Mo, et al. ∙

∙ 07/22/2021

Abstract Reasoning via Logic-guided Generation

Abstract reasoning, i.e., inferring complicated patterns from given obse...

Sihyun Yu, et al. ∙

∙ 07/01/2021

Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble

Recent advance in deep offline reinforcement learning (RL) has made it p...

Seunghyun Lee, et al. ∙

∙ 06/28/2021

Co^2L: Contrastive Continual Learning

Recent breakthroughs in self-supervised learning show that such algorith...

Hyuntak Cha, et al. ∙

∙ 06/15/2021

Scaling Neural Tangent Kernels via Sketching and Random Features

The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely...

Amir Zandieh, et al. ∙

∙ 06/09/2021

Self-Improved Retrosynthetic Planning

Retrosynthetic planning is a fundamental problem in chemistry for findin...

Junsu Kim, et al. ∙

∙ 05/03/2021

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

Retrosynthesis, of which the goal is to find a set of reactants for synt...

Hankook Lee, et al. ∙

∙ 05/03/2021

Consistency and Monotonicity Regularization for Neural Knowledge Tracing

Knowledge Tracing (KT), tracking a human's knowledge acquisition, is a c...

Seewoo Lee, et al. ∙

∙ 04/03/2021

Random Features for the Neural Tangent Kernel

The Neural Tangent Kernel (NTK) has discovered connections between deep ...

Insu Han, et al. ∙

∙ 03/17/2021

Training GANs with Stronger Augmentations via Contrastive Discriminator

Recent works in Generative Adversarial Networks (GANs) are actively revi...

Jongheon Jeong, et al. ∙

∙ 03/08/2021

Consistency Regularization for Adversarial Robustness

Adversarial training (AT) is currently one of the most successful method...

Jihoon Tack, et al. ∙

∙ 02/18/2021

State Entropy Maximization with Random Encoders for Efficient Exploration

Recent exploration methods have proven to be a recipe for improving samp...

Younggyo Seo, et al. ∙

∙ 02/07/2021

Model-Augmented Q-learning

In recent years, Q-learning has become indispensable for model-free rein...

Youngmin Oh, et al. ∙

∙ 12/17/2020

MASKER: Masked Keyword Regularization for Reliable Text Classification

Pre-trained language models have achieved state-of-the-art accuracies on...

Seung Jun Moon, et al. ∙

∙ 10/26/2020

Provable Memorization via Deep Neural Networks using Sub-linear Parameters

It is known that Θ(N) parameters are sufficient for neural networks to m...

Sejun Park, et al. ∙

∙ 10/26/2020

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Model-based reinforcement learning (RL) has shown great potential in var...

Younggyo Seo, et al. ∙

∙ 10/17/2020

i-Mix: A Strategy for Regularizing Contrastive Representation Learning

Contrastive representation learning has shown to be an effective way of ...

Kibok Lee, et al. ∙

∙ 10/15/2020

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

Recent discoveries on neural network pruning reveal that, with a careful...

Jaeho Lee, et al. ∙

∙ 07/23/2020

Few-shot Visual Reasoning with Meta-analogical Contrastive Learning

While humans can solve a visual puzzle that requires logical reasoning b...

Youngsung Kim, et al. ∙

∙ 07/22/2020

Time-Reversal Symmetric ODE Network

Time-reversal symmetry, which requires that the dynamics of a system sho...

In Huh, et al. ∙

∙ 07/17/2020

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

While semi-supervised learning (SSL) has proven to be a promising way fo...

Jaehyung Kim, et al. ∙

∙ 07/16/2020

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Novelty detection, i.e., identifying whether a given sample is drawn fro...

Jihoon Tack, et al. ∙

∙ 07/14/2020

Learning to Sample with Local and Global Contexts in Experience Replay Buffer

Experience replay, which enables the agents to remember and reuse experi...

Youngmin Oh, et al. ∙

∙ 07/06/2020

Learning from Failure: Training Debiased Classifier from Biased Classifier

Neural networks often learn to make predictions that overly rely on spur...

Junhyun Nam, et al. ∙

∙ 07/04/2020

Guiding Deep Molecular Optimization with Genetic Exploration

De novo molecular design attempts to search over the chemical space for ...

Sungsoo Ahn, et al. ∙

∙ 06/22/2020

Learning to Generate Noise for Robustness against Multiple Perturbations

Adversarial learning has emerged as one of the successful techniques to ...

Divyam Madaan, et al. ∙

∙ 06/22/2020

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

We propose a novel value-based algorithm for cooperative multi-agent rei...

Kyunghwan Son, et al. ∙

∙ 06/17/2020

Learning What to Defer for Maximum Independent Sets

Designing efficient algorithms for combinatorial optimization appears ub...

Sungsoo Ahn, et al. ∙

∙ 06/16/2020

Minimum Width for Universal Approximation

The universal approximation property of width-bounded networks has been ...

Sejun Park, et al. ∙

∙ 06/15/2020

Learning Bounds for Risk-sensitive Learning

In risk-sensitive learning, one aims to find a hypothesis that minimizes...

Jaeho Lee, et al. ∙

∙ 06/07/2020

Consistency Regularization for Certified Robustness of Smoothed Classifiers

A recent technique of randomized smoothing has shown that the worst-case...

Jongheon Jeong, et al. ∙

∙ 05/14/2020

Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

Model-based reinforcement learning (RL) enjoys several benefits, such as...

Kimin Lee, et al. ∙

∙ 04/01/2020

M2m: Imbalanced Classification via Major-to-minor Translation

In most real-world scenarios, labeled training datasets are highly class...

Jaehyung Kim, et al. ∙

∙ 03/31/2020

Regularizing Class-wise Predictions via Self-knowledge Distillation

Deep neural networks with millions of parameters may suffer from poor ge...

Sukmin Yun, et al. ∙

∙ 02/25/2020

Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs

Generative adversarial networks (GANs) have shown outstanding performanc...

Sangwoo Mo, et al. ∙