Ziyin Liu

According to our database, Ziyin Liu authored at least 32 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Symmetry Leads to Structured Constraint of Learning.

Ziyin Liu

CoRR, 2023

Law of Balance and Stationary Distribution of Stochastic Gradient Descent.

Ziyin Liu, Hongchao Li, Masahito Ueda

CoRR, 2023

The Probabilistic Stability of Stochastic Gradient Descent.

Ziyin Liu, Botao Li, Tomer Galanti, Masahito Ueda

CoRR, 2023

On the Stepwise Nature of Self-Supervised Learning.

James B. Simon, Maksis Knutins, Ziyin Liu, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht

Proceedings of the International Conference on Machine Learning, 2023

spred: Solving L1 Penalty with SGD.

Ziyin Liu, Zihao Wang

Proceedings of the International Conference on Machine Learning, 2023

What shapes the loss landscape of self supervised learning?

Ziyin Liu, Ekdeep Singh Lubana, Masahito Ueda, Hidenori Tanaka

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Sparsity by Redundancy: Solving L₁ with a Simple Reparametrization.

Ziyin Liu, Zihao Wang

CoRR, 2022

Exact Phase Transitions in Deep Learning.

Ziyin Liu, Masahito Ueda

CoRR, 2022

Stochastic Neural Networks with Infinite Width are Deterministic.

Ziyin Liu, Hanlin Zhang, Xiangming Meng, Yuting Lu, Eric P. Xing, Masahito Ueda

CoRR, 2022

Exact Solutions of a Deep Linear Network.

Ziyin Liu, Botao Li, Xiangming Meng

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Posterior Collapse of a Linear Latent Variable Model.

Zihao Wang, Ziyin Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Power-Law Escape Rate of SGD.

Takashi Mori, Ziyin Liu, Kangqiao Liu, Masahito Ueda

Proceedings of the International Conference on Machine Learning, 2022

SGD Can Converge to Local Maxima.

Ziyin Liu, Botao Li, James B. Simon, Masahito Ueda

Proceedings of the Tenth International Conference on Learning Representations, 2022

Strength of Minibatch Noise in SGD.

Ziyin Liu, Kangqiao Liu, Takashi Mori, Masahito Ueda

Proceedings of the Tenth International Conference on Learning Representations, 2022

Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction.

Ziyin Liu, Kentaro Minami, Kentaro Imajo

Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

2021

SGD May Never Escape Saddle Points.

Ziyin Liu, Botao Li, Masahito Ueda

CoRR, 2021

What Data Augmentation Do We Need for Deep-Learning-Based Finance?

Ziyin Liu, Kentaro Minami, Kentaro Imajo

CoRR, 2021

Logarithmic landscape and power-law escape rate of SGD.

Takashi Mori, Ziyin Liu, Kangqiao Liu, Masahito Ueda

CoRR, 2021

On Minibatch Noise: Discrete-Time SGD, Overparametrization, and Bayes.

Ziyin Liu, Kangqiao Liu, Takashi Mori, Masahito Ueda

CoRR, 2021

On the distributional properties of adaptive gradients.

Zhiyi Zhang, Ziyin Liu

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment.

Paul Pu Liang, Peter Wu, Ziyin Liu, Louis-Philippe Morency, Ruslan Salakhutdinov

Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent.

Kangqiao Liu, Ziyin Liu, Masahito Ueda

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Stochastic Gradient Descent with Large Learning Rate.

Kangqiao Liu, Ziyin Liu, Masahito Ueda

CoRR, 2020

An Investigation of how Label Smoothing Affects Generalization.

Blair Chen, Ziyin Liu, Zihao Wang, Paul Pu Liang

CoRR, 2020

Volumization as a Natural Generalization of Weight Decay.

Ziyin Liu, Zihao Wang, Makoto Yamada, Masahito Ueda

CoRR, 2020

Learning Not to Learn in the Presence of Noisy Labels.

Ziyin Liu, Blair Chen, Ru Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda

CoRR, 2020

LaProp: a Better Way to Combine Momentum with Adaptive Gradient.

Ziyin Liu, Zhikang Wang, Masahito Ueda

CoRR, 2020

Think Locally, Act Globally: Federated Learning with Local and Global Representations.

Paul Pu Liang, Terrance Liu, Ziyin Liu, Ruslan Salakhutdinov, Louis-Philippe Morency

CoRR, 2020

Neural Networks Fail to Learn Periodic Functions and How to Fix It.

Ziyin Liu, Tilman Hartwig, Masahito Ueda

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Deep Gamblers: Learning to Abstain with Portfolio Theory.

Ziyin Liu, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning.

Yixiu Zhao, Ziyin Liu

CoRR, 2018

Multimodal Language Analysis with Recurrent Multistage Fusion.

Paul Pu Liang, Ziyin Liu, Amir Zadeh, Louis-Philippe Morency

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018