4 citations · 7 across the 13 of their papers we have counts for
14 papers
MiMuon: Mixed Muon Optimizer with Improved Generalization for Large Models
Feihu Huang, Yuning Luo, Songcan Chen
Matrix-structured parameters frequently appear in many artificial intelligence models such as large language models. More recently, an efficient Muon optimizer is designed for matr…
CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization
Feihu Huang, Guanyi Zhang, Songcan Chen
Lion optimizer is a popular learning-based optimization algorithm in machine learning, which shows impressive performance in training many deep learning models. Although convergenc…
HomeAdam: Adam and AdamW Algorithms Sometimes Go Home to Obtain Better Provable Generalization
Feihu Huang, Guanyi Zhang, Songcan Chen
Adam and AdamW are a class of default optimizers for training deep learning models in machine learning. These adaptive algorithms converge faster but generalize worse compared to S…
Negatives-Dominant Contrastive Learning for Generalization in Imbalanced Domains
Meng Cao, Jiexi Liu, Songcan Chen
Imbalanced Domain Generalization (IDG) focuses on mitigating both domain and label shifts, both of which fundamentally shape the model's decision boundaries, particularly under het…
The Finer the Better: Towards Granular-aware Open-set Domain Generalization
Yunyun Wang, Zheng Duan, Xinyue Liao +2
Open-Set Domain Generalization (OSDG) tackles the realistic scenario where deployed models encounter both domain shifts and novel object categories. Despite impressive progress wit…
Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled time series (ISTS), characterized by non-uniform time intervals with natural missingness, are prevalent in real-world applications. Existing approaches for ISTS…
LiMuon: Light and Fast Muon Optimizer for Large Models
Feihu Huang, Yuning Luo, Songcan Chen
Large models recently are widely applied in machine learning, so efficient training of large models has received widespread attention. More recently, the useful Muon optimizer is s…
TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among s…
Guidance Not Obstruction: A Conjugate Consistent Enhanced Strategy for Domain Generalization
Meng Cao, Songcan Chen
Domain generalization addresses domain shift in real-world applications. Most approaches adopt a domain angle, seeking invariant representation across domains by aligning their mar…
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Most existing methods treat ISMTS as synchronized regularly sampled time series with missing values,…
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends
Xiang Li, Jiexi Liu, Xinrui Wang +1
In reality, data often exhibit associations with multiple labels, making multi-label learning (MLL) become a prominent research topic. The last two decades have witnessed the succe…
TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning
Jiexi Liu, Songcan Chen
Learning universal time series representations applicable to various types of downstream tasks is challenging but valuable in real applications. Recently, researchers have attempte…
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
Feihu Huang, Chunyu Xuan, Xinrui Wang +2
Minimax optimization recently is widely applied in many machine learning tasks such as generative adversarial networks, robust learning and reinforcement learning. In the paper, we…
Adaptive Federated Minimax Optimization with Lower Complexities
Feihu Huang, Xinrui Wang, Junyi Li +1
Federated learning is a popular distributed and privacy-preserving learning paradigm in machine learning. Recently, some federated learning algorithms have been proposed to solve t…