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2020 – today
- 2024
[c28]Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra:
Linear attention is (maybe) all you need (to understand Transformer optimization). ICLR 2024- [c27]Jaewook Lee, Hanseul Cho, Chulhee Yun:
Fundamental Benefit of Alternating Updates in Minimax Optimization. ICML 2024 - [i26]Jaewook Lee, Hanseul Cho, Chulhee Yun:
Fundamental Benefit of Alternating Updates in Minimax Optimization. CoRR abs/2402.10475 (2024) - [i25]Minhak Song, Kwangjun Ahn, Chulhee Yun:
Does SGD really happen in tiny subspaces? CoRR abs/2405.16002 (2024) - [i24]Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi, Srinadh Bhojanapalli, Anupam Gupta, Chulhee Yun:
Position Coupling: Leveraging Task Structure for Improved Length Generalization of Transformers. CoRR abs/2405.20671 (2024) - 2023
- [c26]Hanseul Cho, Chulhee Yun:
SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization. ICLR 2023 - [c25]Jaeyoung Cha, Jaewook Lee, Chulhee Yun:
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond. ICML 2023: 3855-3912 - [c24]Junsoo Oh, Chulhee Yun:
Provable Benefit of Mixup for Finding Optimal Decision Boundaries. ICML 2023: 26403-26450 - [c23]David Xing Wu, Chulhee Yun, Suvrit Sra:
On the Training Instability of Shuffling SGD with Batch Normalization. ICML 2023: 37787-37845 - [c22]Hojoon Lee, Hanseul Cho, Hyunseung Kim, Daehoon Gwak, Joonkee Kim, Jaegul Choo, Se-Young Yun, Chulhee Yun:
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning. NeurIPS 2023 - [c21]Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun:
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint. NeurIPS 2023 - [c20]Dongkuk Si, Chulhee Yun:
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima. NeurIPS 2023 - [c19]Minhak Song, Chulhee Yun:
Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory. NeurIPS 2023 - [i23]David Xing Wu, Chulhee Yun, Suvrit Sra:
On the Training Instability of Shuffling SGD with Batch Normalization. CoRR abs/2302.12444 (2023) - [i22]Jaeyoung Cha, Jaewook Lee, Chulhee Yun:
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond. CoRR abs/2303.07160 (2023) - [i21]Junsoo Oh, Chulhee Yun:
Provable Benefit of Mixup for Finding Optimal Decision Boundaries. CoRR abs/2306.00267 (2023) - [i20]Dongkuk Si, Chulhee Yun:
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima. CoRR abs/2306.09850 (2023) - [i19]Hojoon Lee, Hanseul Cho, Hyunseung Kim, Daehoon Gwak, Joonkee Kim, Jaegul Choo, Se-Young Yun, Chulhee Yun:
Enhancing Generalization and Plasticity for Sample Efficient Reinforcement Learning. CoRR abs/2306.10711 (2023) - [i18]Minhak Song
, Chulhee Yun:
Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory. CoRR abs/2307.04204 (2023) - [i17]Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra:
Linear attention is (maybe) all you need (to understand transformer optimization). CoRR abs/2310.01082 (2023) - [i16]Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun:
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint. CoRR abs/2310.18593 (2023) - [i15]Prin Phunyaphibarn, Junghyun Lee, Bohan Wang, Huishuai Zhang, Chulhee Yun:
Large Catapults in Momentum Gradient Descent with Warmup: An Empirical Study. CoRR abs/2311.15051 (2023) - 2022
- [c18]Chulhee Yun, Shashank Rajput, Suvrit Sra:
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. ICLR 2022 - [i14]Hanseul Cho, Chulhee Yun:
SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization. CoRR abs/2210.05995 (2022) - 2021
- [b1]Chulhee Yun:
Optimization for Deep Learning: Bridging the Theory-Practice Gap. Massachusetts Institute of Technology, USA, 2021 - [c17]Sejun Park, Jaeho Lee, Chulhee Yun, Jinwoo Shin:
Provable Memorization via Deep Neural Networks using Sub-linear Parameters. COLT 2021: 3627-3661 - [c16]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? COLT 2021: 4653-4658 - [c15]Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin:
Minimum Width for Universal Approximation. ICLR 2021 - [c14]Chulhee Yun, Shankar Krishnan, Hossein Mobahi:
A unifying view on implicit bias in training linear neural networks. ICLR 2021 - [i13]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? CoRR abs/2103.07079 (2021) - [i12]Chulhee Yun, Shashank Rajput, Suvrit Sra:
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. CoRR abs/2110.10342 (2021) - 2020
- [c13]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? ICLR 2020 - [c12]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. ICML 2020: 864-873 - [c11]Kwangjun Ahn, Chulhee Yun, Suvrit Sra:
SGD with shuffling: optimal rates without component convexity and large epoch requirements. NeurIPS 2020 - [c10]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers. NeurIPS 2020 - [i11]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. CoRR abs/2002.07028 (2020) - [i10]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers. CoRR abs/2006.04862 (2020) - [i9]Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin:
Minimum Width for Universal Approximation. CoRR abs/2006.08859 (2020) - [i8]Chulhee Yun, Shankar Krishnan, Hossein Mobahi:
A Unifying View on Implicit Bias in Training Linear Neural Networks. CoRR abs/2010.02501 (2020) - [i7]Sejun Park, Jaeho Lee, Chulhee Yun, Jinwoo Shin:
Provable Memorization via Deep Neural Networks using Sub-linear Parameters. CoRR abs/2010.13363 (2020)
2010 – 2019
- 2019
- [c9]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Efficiently testing local optimality and escaping saddles for ReLU networks. ICLR (Poster) 2019 - [c8]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Small nonlinearities in activation functions create bad local minima in neural networks. ICLR (Poster) 2019 - [c7]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. NeurIPS 2019: 15532-15543 - [c6]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Are deep ResNets provably better than linear predictors? NeurIPS 2019: 15660-15669 - [i6]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Are deep ResNets provably better than linear predictors? CoRR abs/1907.03922 (2019) - [i5]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? CoRR abs/1912.10077 (2019) - 2018
- [c5]John C. Duchi, Feng Ruan, Chulhee Yun:
Minimax Bounds on Stochastic Batched Convex Optimization. COLT 2018: 3065-3162 - [c4]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Global Optimality Conditions for Deep Neural Networks. ICLR (Poster) 2018 - [i4]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
A Critical View of Global Optimality in Deep Learning. CoRR abs/1802.03487 (2018) - [i3]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Efficiently testing local optimality and escaping saddles for ReLU networks. CoRR abs/1809.10858 (2018) - [i2]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Finite sample expressive power of small-width ReLU networks. CoRR abs/1810.07770 (2018) - 2017
- [i1]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Global optimality conditions for deep neural networks. CoRR abs/1707.02444 (2017) - 2015
- [c3]Chulhee Yun, Donghoon Lee, Chang Dong Yoo:
Face detection using Local Hybrid Patterns. ICASSP 2015: 1468-1472 - 2013
- [c2]Chulhee Yun, Jaegon Ahn, Yeon-Ho Kim:
A fusion of computer vision technique and a visual programming language for edutainment robots. ISR 2013: 1-5 - [c1]Chulhee Yun, Jaegon Ahn, Yeon-Ho Kim:
An implementation of computer vision technique for an edutainment robot with a visual programming language. URAI 2013: 131-133
Coauthor Index
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