32 citations · 116 across the 11 of their papers we have counts for
12 papers
RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting
Hao-Nan Shi, Tong Wu, Chen-Cong Sun +3
Time-series foundation models (TSFMs) perform strongly across forecasting tasks, but their per-series inference is ill-suited to neighbor-rich forecasting, where each query has acc…
Theoretical Exploration of Flexible Transmitter Model
Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang +1
Neural network models generally involve two important components, i.e., network architecture and neuron model. Although there are abundant studies about network architectures, only…
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values
Zhao-Yu Zhang, Shao-Qun Zhang, Yuan Jiang +1
Multivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values. In recent years, there has been an increasing interest in…
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning
Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen +3
In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces. This situation brings significant obstacles to existin…
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport
Yang Yang, Zhao-Yang Fu, De-Chuan Zhan +2
Complex objects are usually with multiple labels, and can be represented by multiple modal representations, e.g., the complex articles contain text and image information as well as…
A Simple Approach for Non-stationary Linear Bandits
Peng Zhao, Lijun Zhang, Yuan Jiang +1
This paper investigates the problem of non-stationary linear bandits, where the unknown regression parameter is evolving over time. Existing studies develop various algorithms and…
Soft Gradient Boosting Machine
Ji Feng, Yi-Xuan Xu, Yuan Jiang +1
Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas. However, since the training procedure of each base l…
Provably Robust Metric Learning
Lu Wang, Xuanqing Liu, Jinfeng Yi +2
Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less…
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data
Lu Wang, Huan Zhang, Jinfeng Yi +2
Adversarial black-box attacks aim to craft adversarial perturbations by querying input-output pairs of machine learning models. They are widely used to evaluate the robustness of p…
Multi-Label Learning with Deep Forest
Liang Yang, Xi-Zhu Wu, Yuan Jiang +1
In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods…
Imitation Learning from Pixel-Level Demonstrations by HashReward
Xin-Qiang Cai, Yao-Xiang Ding, Yuan Jiang +1
One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space. This problem is much more severe…
Multi-Label Active Learning from Crowds
Shao-Yuan Li, Yuan Jiang, Zhi-Hua Zhou
Multi-label active learning is a hot topic in reducing the label cost by optimally choosing the most valuable instance to query its label from an oracle. In this paper, we consider…