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Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent
Donghwa Kim, Jaewook Lee, Chulhee Yun
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
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
Yujun Kim, Jaeyoung Cha, Chulhee Yun
Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
Yeseul Cho, Baekrok Shin, Changmin Kang, Chulhee Yun
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification
Hyunji Jung, Hanseul Cho, Chulhee Yun
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
Hyunsu Kim, Giung Nam, Chulhee Yun, Hongseok Yang, Juho Lee
Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count
Hanseul Cho, Jaeyoung Cha, Srinadh Bhojanapalli, Chulhee Yun
Does SGD really happen in tiny subspaces?
Minhak Song, Kwangjun Ahn, Chulhee Yun
Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements
Jiseok Chae, Chulhee Yun, Donghwan Kim
DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity
Baekrok Shin, Junsoo Oh, Hanseul Cho, Chulhee Yun