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
212 publications
Pieter Abbeel
161 publications
Honglak Lee
82 publications
Sung Ju Hwang
48 publications
Sewoong Oh
47 publications
Adrian Weller
42 publications
Eunho Yang
40 publications
Minsu Cho
33 publications
Michael Chertkov
29 publications
Chun-Liang Li
27 publications
Kihyuk Sohn
23 publications

State Entropy Maximization with Random Encoders for Efficient Exploration

Recent exploration methods have proven to be a recipe for improving samp...
02/18/2021 ∙ by Younggyo Seo, et al. ∙ 13 ∙ share

read it
Model-Augmented Q-learning

In recent years, Q-learning has become indispensable for model-free rein...
02/07/2021 ∙ by Youngmin Oh, et al. ∙ 0 ∙ share

read it
MASKER: Masked Keyword Regularization for Reliable Text Classification

Pre-trained language models have achieved state-of-the-art accuracies on...
12/17/2020 ∙ by Seung Jun Moon, et al. ∙ 0 ∙ share

read it
Provable Memorization via Deep Neural Networks using Sub-linear Parameters

It is known that Θ(N) parameters are sufficient for neural networks to m...
10/26/2020 ∙ by Sejun Park, et al. ∙ 0 ∙ share

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

Model-based reinforcement learning (RL) has shown great potential in var...
10/26/2020 ∙ by Younggyo Seo, et al. ∙ 0 ∙ share

read it
i-Mix: A Strategy for Regularizing Contrastive Representation Learning

Contrastive representation learning has shown to be an effective way of ...
10/17/2020 ∙ by Kibok Lee, et al. ∙ 4 ∙ share

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

Recent discoveries on neural network pruning reveal that, with a careful...
10/15/2020 ∙ by Jaeho Lee, et al. ∙ 0 ∙ share

read it
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning

While humans can solve a visual puzzle that requires logical reasoning b...
07/23/2020 ∙ by Youngsung Kim, et al. ∙ 3 ∙ share

read it
Time-Reversal Symmetric ODE Network

Time-reversal symmetry, which requires that the dynamics of a system sho...
07/22/2020 ∙ by In Huh, et al. ∙ 10 ∙ share

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

While semi-supervised learning (SSL) has proven to be a promising way fo...
07/17/2020 ∙ by Jaehyung Kim, et al. ∙ 15 ∙ share

read it
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Novelty detection, i.e., identifying whether a given sample is drawn fro...
07/16/2020 ∙ by Jihoon Tack, et al. ∙ 5 ∙ share

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

Experience replay, which enables the agents to remember and reuse experi...
07/14/2020 ∙ by Youngmin Oh, et al. ∙ 7 ∙ share

read it
Learning from Failure: Training Debiased Classifier from Biased Classifier

Neural networks often learn to make predictions that overly rely on spur...
07/06/2020 ∙ by Junhyun Nam, et al. ∙ 12 ∙ share

read it
Guiding Deep Molecular Optimization with Genetic Exploration

De novo molecular design attempts to search over the chemical space for ...
07/04/2020 ∙ by Sungsoo Ahn, et al. ∙ 15 ∙ share

read it
Learning to Generate Noise for Robustness against Multiple Perturbations

Adversarial learning has emerged as one of the successful techniques to ...
06/22/2020 ∙ by Divyam Madaan, et al. ∙ 0 ∙ share

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

We propose a novel value-based algorithm for cooperative multi-agent rei...
06/22/2020 ∙ by Kyunghwan Son, et al. ∙ 8 ∙ share

read it
Learning What to Defer for Maximum Independent Sets

Designing efficient algorithms for combinatorial optimization appears ub...
06/17/2020 ∙ by Sungsoo Ahn, et al. ∙ 0 ∙ share

read it
Minimum Width for Universal Approximation

The universal approximation property of width-bounded networks has been ...
06/16/2020 ∙ by Sejun Park, et al. ∙ 0 ∙ share

read it
Learning Bounds for Risk-sensitive Learning

In risk-sensitive learning, one aims to find a hypothesis that minimizes...
06/15/2020 ∙ by Jaeho Lee, et al. ∙ 0 ∙ share

read it
Consistency Regularization for Certified Robustness of Smoothed Classifiers

A recent technique of randomized smoothing has shown that the worst-case...
06/07/2020 ∙ by Jongheon Jeong, et al. ∙ 7 ∙ share

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

Model-based reinforcement learning (RL) enjoys several benefits, such as...
05/14/2020 ∙ by Kimin Lee, et al. ∙ 7 ∙ share

read it
M2m: Imbalanced Classification via Major-to-minor Translation

In most real-world scenarios, labeled training datasets are highly class...
04/01/2020 ∙ by Jaehyung Kim, et al. ∙ 6 ∙ share

read it
Regularizing Class-wise Predictions via Self-knowledge Distillation

Deep neural networks with millions of parameters may suffer from poor ge...
03/31/2020 ∙ by Sukmin Yun, et al. ∙ 9 ∙ share

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

Generative adversarial networks (GANs) have shown outstanding performanc...
02/25/2020 ∙ by Sangwoo Mo, et al. ∙ 25 ∙ share

read it
Freeze Discriminator: A Simple Baseline for Fine-tuning GANs

Generative adversarial networks (GANs) have shown outstanding performanc...
02/25/2020 ∙ by Sangwoo Mo, et al. ∙ 13 ∙ share

read it
Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning

Magnitude-based pruning is one of the simplest methods for pruning neura...
02/12/2020 ∙ by Sejun Park, et al. ∙ 12 ∙ share

read it
Mining GOLD Samples for Conditional GANs

Conditional generative adversarial networks (cGANs) have gained a consid...
10/21/2019 ∙ by Sangwoo Mo, et al. ∙ 9 ∙ share

read it
Rethinking Data Augmentation: Self-Supervision and Self-Distillation

Data augmentation techniques, e.g., flipping or cropping, which systemat...
10/14/2019 ∙ by Hankook Lee, et al. ∙ 13 ∙ share

read it
A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...
10/11/2019 ∙ by Kimin Lee, et al. ∙ 24 ∙ share

read it
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix

This paper studies how to sketch element-wise functions of low-rank matr...
05/28/2019 ∙ by Insu Han, et al. ∙ 8 ∙ share

read it
Learning What and Where to Transfer

As the application of deep learning has expanded to real-world problems ...
05/15/2019 ∙ by Yunhun Jang, et al. ∙ 8 ∙ share

read it
Spectral Approximate Inference

Given a graphical model (GM), computing its partition function is the mo...
05/14/2019 ∙ by Sejun Park, et al. ∙ 3 ∙ share

read it
Training CNNs with Selective Allocation of Channels

Recent progress in deep convolutional neural networks (CNNs) have enable...
05/11/2019 ∙ by Jongheon Jeong, et al. ∙ 4 ∙ share

read it
Incremental Learning with Unlabeled Data in the Wild

Deep neural networks are known to suffer from catastrophic forgetting in...
03/29/2019 ∙ by Kibok Lee, et al. ∙ 13 ∙ share

read it
Bitcoin vs. Bitcoin Cash: Coexistence or Downfall of Bitcoin Cash?

In Aug. 2017, Bitcoin was split into the original Bitcoin (BTC) and Bitc...
02/28/2019 ∙ by Yujin Kwon, et al. ∙ 0 ∙ share

read it
Robust Inference via Generative Classifiers for Handling Noisy Labels

Large-scale datasets may contain significant proportions of noisy (incor...
01/31/2019 ∙ by Kimin Lee, et al. ∙ 7 ∙ share

read it
InstaGAN: Instance-aware Image-to-Image Translation

Unsupervised image-to-image translation has gained considerable attentio...
12/28/2018 ∙ by Sangwoo Mo, et al. ∙ 2 ∙ share

read it
Simulation-based Distributed Coordination Maximization over Networks

In various online/offline multi-agent networked environments, it is very...
09/13/2018 ∙ by Hyeryung Jang, et al. ∙ 0 ∙ share

read it
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Detecting test samples drawn sufficiently far away from the training dis...
07/10/2018 ∙ by Kimin Lee, et al. ∙ 3 ∙ share

read it
Anytime Neural Prediction via Slicing Networks Vertically

The pioneer deep neural networks (DNNs) have emerged to be deeper or wid...
07/07/2018 ∙ by Hankook Lee, et al. ∙ 0 ∙ share

read it
Hierarchical Novelty Detection for Visual Object Recognition

Deep neural networks have achieved impressive success in large-scale vis...
04/02/2018 ∙ by Kibok Lee, et al. ∙ 1 ∙ share

read it
Bucket Renormalization for Approximate Inference

Probabilistic graphical models are a key tool in machine learning applic...
03/14/2018 ∙ by Sungsoo Ahn, et al. ∙ 0 ∙ share

read it
Optimizing Spectral Sums using Randomized Chebyshev Expansions

The trace of matrix functions, often called spectral sums, e.g., rank, l...
02/18/2018 ∙ by Insu Han, et al. ∙ 0 ∙ share

read it
Gauged Mini-Bucket Elimination for Approximate Inference

Computing the partition function Z of a discrete graphical model is a fu...
01/05/2018 ∙ by Sungsoo Ahn, et al. ∙ 0 ∙ share

read it
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

The problem of detecting whether a test sample is from in-distribution (...
11/26/2017 ∙ by Kimin Lee, et al. ∙ 1 ∙ share

read it
Confident Multiple Choice Learning

Ensemble methods are arguably the most trustworthy techniques for boosti...
06/12/2017 ∙ by Kimin Lee, et al. ∙ 1 ∙ share

read it
Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models

The Gibbs sampler is a particularly popular Markov chain used for learni...
04/06/2017 ∙ by Sejun Park, et al. ∙ 0 ∙ share

read it
Sequential Local Learning for Latent Graphical Models

Learning parameters of latent graphical models (GM) is inherently much h...
03/12/2017 ∙ by Sejun Park, et al. ∙ 0 ∙ share

read it
Contextual Multi-armed Bandits under Feature Uncertainty

We study contextual multi-armed bandit problems under linear realizabili...
03/03/2017 ∙ by Se-Young Yun, et al. ∙ 0 ∙ share

read it
Gauging Variational Inference

Computing partition function is the most important statistical inference...
03/03/2017 ∙ by Sungsoo Ahn, et al. ∙ 0 ∙ share

read it