Jinwoo Shin

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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.

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

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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. ∙ 23 ∙ share

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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

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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. ∙ 12 ∙ share

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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. ∙ 12 ∙ share

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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

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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

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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

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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. ∙ 8 ∙ share

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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

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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. ∙ 7 ∙ share

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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

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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. ∙ 6 ∙ share

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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

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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. ∙ 6 ∙ share

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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

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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

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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. ∙ 2 ∙ share

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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

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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. ∙ 0 ∙ share

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Confident Multiple Choice Learning

Ensemble methods are arguably the most trustworthy techniques for boosti...
06/12/2017 ∙ by Kimin Lee, et al. ∙ 0 ∙ share

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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

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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

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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

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Gauging Variational Inference

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

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Iterative Bayesian Learning for Crowdsourced Regression

Crowdsourcing platforms emerged as popular venues for purchasing human i...
02/28/2017 ∙ by Jungseul Ok, et al. ∙ 0 ∙ share

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MCMC assisted by Belief Propagaion

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most...
05/29/2016 ∙ by Sungsoo Ahn, et al. ∙ 0 ∙ share

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Adiabatic Persistent Contrastive Divergence Learning

This paper studies the problem of parameter learning in probabilistic gr...
05/26/2016 ∙ by Hyeryung Jang, et al. ∙ 0 ∙ share

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Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization

The max-product belief propagation (BP) is a popular message-passing heu...
12/16/2014 ∙ by Sejun Park, et al. ∙ 0 ∙ share

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Optimal Inference in Crowdsourced Classification via Belief Propagation

Crowdsourcing systems are popular for solving large-scale labelling task...
02/11/2016 ∙ by Jungseul Ok, et al. ∙ 0 ∙ share

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Minimum Weight Perfect Matching via Blossom Belief Propagation

Max-product Belief Propagation (BP) is a popular message-passing algorit...
09/23/2015 ∙ by Sungsoo Ahn, et al. ∙ 0 ∙ share

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Loop Calculus and Bootstrap-Belief Propagation for Perfect Matchings on Arbitrary Graphs

This manuscript discusses computation of the Partition Function (PF) and...
06/05/2013 ∙ by Michael Chertkov, et al. ∙ 0 ∙ share

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Belief Propagation for Linear Programming

Belief Propagation (BP) is a popular, distributed heuristic for performi...
05/17/2013 ∙ by Andrew Gelfand, et al. ∙ 0 ∙ share

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The Complexity of Approximating a Bethe Equilibrium

This paper resolves a common complexity issue in the Bethe approximation...
09/08/2011 ∙ by Jinwoo Shin, et al. ∙ 0 ∙ share

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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

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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

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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. ∙ 0 ∙ share

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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

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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

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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

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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

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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

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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

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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

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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

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Jinwoo Shin

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