Einstein-Podolsky-Rosen correlation seen from moving observers. - Hiroaki Terashima; Masahito Ueda (Quantum Inf. Comput., 2003)

Deep Gamblers: Learning to Abstain with Portfolio Theory. - Liu Ziyin 001; Zhikang Wang; Paul Pu Liang; Ruslan Salakhutdinov; Louis-Philippe Morency; Masahito Ueda (NeurIPS, 2019)

Deep Reinforcement Learning Control of Quantum Cartpoles. - Zhikang Wang; Yuto Ashida; Masahito Ueda (CoRR, 2019)

Neural Networks Fail to Learn Periodic Functions and How to Fix It. - Liu Ziyin 001; Tilman Hartwig; Masahito Ueda (NeurIPS, 2020)

LaProp: a Better Way to Combine Momentum with Adaptive Gradient. - Liu Ziyin 001; Zhikang Wang; Masahito Ueda (CoRR, 2020)

Learning Not to Learn in the Presence of Noisy Labels. - Liu Ziyin 001; Blair Chen; Ru Wang; Paul Pu Liang; Ruslan Salakhutdinov; Louis-Philippe Morency; Masahito Ueda (CoRR, 2020)

Volumization as a Natural Generalization of Weight Decay. - Liu Ziyin 001; Zihao Wang 001; Makoto Yamada; Masahito Ueda (CoRR, 2020)

Is deeper better? It depends on locality of relevant features. - Takashi Mori; Masahito Ueda (CoRR, 2020)

Improved generalization by noise enhancement. - Takashi Mori; Masahito Ueda (CoRR, 2020)

Stochastic Gradient Descent with Large Learning Rate. - Kangqiao Liu; Liu Ziyin 001; Masahito Ueda (CoRR, 2020)

Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent. - Kangqiao Liu; Liu Ziyin 001; Masahito Ueda (ICML, 2021)

On Minibatch Noise: Discrete-Time SGD, Overparametrization, and Bayes. - Liu Ziyin 001; Kangqiao Liu; Takashi Mori; Masahito Ueda (CoRR, 2021)

Logarithmic landscape and power-law escape rate of SGD. - Takashi Mori; Liu Ziyin 001; Kangqiao Liu; Masahito Ueda (CoRR, 2021)

A Convergent and Efficient Deep Q Network Algorithm. - Zhikang T. Wang; Masahito Ueda (CoRR, 2021)

SGD May Never Escape Saddle Points. - Liu Ziyin 001; Botao Li; Masahito Ueda (CoRR, 2021)

Strength of Minibatch Noise in SGD. - Liu Ziyin 001; Kangqiao Liu; Takashi Mori; Masahito Ueda (ICLR, 2022)

SGD Can Converge to Local Maxima. - Liu Ziyin 001; Botao Li; James B. Simon; Masahito Ueda (ICLR, 2022)

Convergent and Efficient Deep Q Learning Algorithm. - Zhikang T. Wang; Masahito Ueda (ICLR, 2022)

Power-Law Escape Rate of SGD. - Takashi Mori; Liu Ziyin 001; Kangqiao Liu; Masahito Ueda (ICML, 2022)

Interplay between depth of neural networks and locality of target functions. - Takashi Mori; Masahito Ueda (CoRR, 2022)

Stochastic Neural Networks with Infinite Width are Deterministic. - Liu Ziyin 001; Hanlin Zhang 002; Xiangming Meng; Yuting Lu; Eric P. Xing; Masahito Ueda (CoRR, 2022)

Exact Phase Transitions in Deep Learning. - Liu Ziyin 001; Masahito Ueda (CoRR, 2022)

Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines. - Lennart Dabelow; Masahito Ueda (CoRR, 2022)

What shapes the loss landscape of self supervised learning? - Liu Ziyin 001; Ekdeep Singh Lubana; Masahito Ueda; Hidenori Tanaka (ICLR, 2023)

The Probabilistic Stability of Stochastic Gradient Descent. - Liu Ziyin 001; Botao Li; Tomer Galanti; Masahito Ueda (CoRR, 2023)

Law of Balance and Stationary Distribution of Stochastic Gradient Descent. - Liu Ziyin 001; Hongchao Li; Masahito Ueda (CoRR, 2023)

Symbolic equation solving via reinforcement learning. - Lennart Dabelow; Masahito Ueda (Neurocomputing, 2025)