A fusion of computer vision technique and a visual programming language for edutainment robots. - Chulhee Yun; Jaegon Ahn; Yeon-Ho Kim (ISR, 2013)

An implementation of computer vision technique for an edutainment robot with a visual programming language. - Chulhee Yun; Jaegon Ahn; Yeon-Ho Kim (URAI, 2013)

Face detection using Local Hybrid Patterns. - Chulhee Yun; Donghoon Lee; Chang Dong Yoo (ICASSP, 2015)

Minimax Bounds on Stochastic Batched Convex Optimization. - John C. Duchi; Feng Ruan; Chulhee Yun (COLT, 2018)

Global Optimality Conditions for Deep Neural Networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (ICLR (Poster), 2018)

A Critical View of Global Optimality in Deep Learning. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (CoRR, 2018)

Finite sample expressive power of small-width ReLU networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (CoRR, 2018)

Efficiently testing local optimality and escaping saddles for ReLU networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (ICLR (Poster), 2019)

Small nonlinearities in activation functions create bad local minima in neural networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (ICLR (Poster), 2019)

Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (NeurIPS, 2019)

Are deep ResNets provably better than linear predictors? - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (NeurIPS, 2019)

Are Transformers universal approximators of sequence-to-sequence functions? - Chulhee Yun; Srinadh Bhojanapalli; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar (ICLR, 2020)

Low-Rank Bottleneck in Multi-head Attention Models. - Srinadh Bhojanapalli; Chulhee Yun; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar (ICML, 2020)

SGD with shuffling: optimal rates without component convexity and large epoch requirements. - Kwangjun Ahn; Chulhee Yun; Suvrit Sra (NeurIPS, 2020)

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers. - Chulhee Yun; Yin-Wen Chang; Srinadh Bhojanapalli; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar (NeurIPS, 2020)

Provable Memorization via Deep Neural Networks using Sub-linear Parameters. - Sejun Park; Jaeho Lee 001; Chulhee Yun; Jinwoo Shin (COLT, 2021)

Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (COLT, 2021)

Minimum Width for Universal Approximation. - Sejun Park; Chulhee Yun; Jaeho Lee 001; Jinwoo Shin (ICLR, 2021)

A unifying view on implicit bias in training linear neural networks. - Chulhee Yun; Shankar Krishnan; Hossein Mobahi (ICLR, 2021)

Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (CoRR, 2021)

Optimization for Deep Learning: Bridging the Theory-Practice Gap. - Chulhee Yun (2021)

Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. - Chulhee Yun; Shashank Rajput; Suvrit Sra (ICLR, 2022)

SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization. - Hanseul Cho 002; Chulhee Yun (ICLR, 2023)

Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond. - Jaeyoung Cha; Jaewook Lee 009; Chulhee Yun (ICML, 2023)

Provable Benefit of Mixup for Finding Optimal Decision Boundaries. - Junsoo Oh; Chulhee Yun (ICML, 2023)

On the Training Instability of Shuffling SGD with Batch Normalization. - David Xing Wu; Chulhee Yun; Suvrit Sra (ICML, 2023)

PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning. - Hojoon Lee; Hanseul Cho 002; Hyunseung Kim; Daehoon Gwak; Joonkee Kim; Jaegul Choo; Se-Young Yun; Chulhee Yun (NeurIPS, 2023)

Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint. - Junghyun Lee; Hanseul Cho 002; Se-Young Yun; Chulhee Yun (NeurIPS, 2023)

Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima. - Dongkuk Si; Chulhee Yun (NeurIPS, 2023)

Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory. - Minhak Song; Chulhee Yun (NeurIPS, 2023)

Enhancing Generalization and Plasticity for Sample Efficient Reinforcement Learning. - Hojoon Lee; Hanseul Cho 002; Hyunseung Kim; Daehoon Gwak; Joonkee Kim; Jaegul Choo; Se-Young Yun; Chulhee Yun (CoRR, 2023)

Large Catapults in Momentum Gradient Descent with Warmup: An Empirical Study. - Prin Phunyaphibarn; Junghyun Lee; Bohan Wang; Huishuai Zhang; Chulhee Yun (CoRR, 2023)

Linear attention is (maybe) all you need (to understand Transformer optimization). - Kwangjun Ahn; Xiang Cheng; Minhak Song; Chulhee Yun; Ali Jadbabaie; Suvrit Sra (ICLR, 2024)

Fundamental Benefit of Alternating Updates in Minimax Optimization. - Jaewook Lee 009; Hanseul Cho 002; Chulhee Yun (ICML, 2024)

Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure. - Hanseul Cho 002; Jaeyoung Cha; Pranjal Awasthi; Srinadh Bhojanapalli; Anupam Gupta 001; Chulhee Yun (NeurIPS, 2024)

Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements. - Jiseok Chae; Chulhee Yun; Donghwan Kim (NeurIPS, 2024)

Provable Benefit of Cutout and CutMix for Feature Learning. - Junsoo Oh; Chulhee Yun (NeurIPS, 2024)

DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity. - Baekrok Shin; Junsoo Oh; Hanseul Cho 002; Chulhee Yun (NeurIPS, 2024)

Position Coupling: Leveraging Task Structure for Improved Length Generalization of Transformers. - Hanseul Cho 002; Jaeyoung Cha; Pranjal Awasthi; Srinadh Bhojanapalli; Anupam Gupta 001; Chulhee Yun (CoRR, 2024)

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count. - Hanseul Cho 002; Jaeyoung Cha; Srinadh Bhojanapalli; Chulhee Yun (ICLR, 2025)

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification. - Hyunji Jung; Hanseul Cho 002; Chulhee Yun (ICLR, 2025)

Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo. - Hyunsu Kim; Giung Nam; Chulhee Yun; Hongseok Yang; Juho Lee 001 (ICLR, 2025)

Does SGD really happen in tiny subspaces? - Minhak Song; Kwangjun Ahn; Chulhee Yun (ICLR, 2025)

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty. - Yeseul Cho; Baekrok Shin; Changmin Kang; Chulhee Yun (CoRR, 2025)

Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems. - Yujun Kim; Jaeyoung Cha; Chulhee Yun (CoRR, 2025)

Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More. - Geonhui Yoo; Minhak Song; Chulhee Yun (CoRR, 2025)

Through the River: Understanding the Benefit of Schedule-Free Methods for Language Model Training. - Minhak Song; Beomhan Baek; Kwangjun Ahn; Chulhee Yun (CoRR, 2025)