151 citations · 498 across the 65 of their papers we have counts for
8 papers · 1 filter
Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach
Liyan Xu, Chenwei Zhang, Xian Li +2
We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention. Ou…
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Jiayun Zhang, Xiyuan Zhang, Xinyang Zhang +3
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set…
Toward Student-Oriented Teacher Network Training For Knowledge Distillation
Chengyu Dong, Liyuan Liu, Jingbo Shang
How to conduct teacher training for knowledge distillation is still an open problem. It has been widely observed that a best-performing teacher does not necessarily yield the best-…
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
Chengyu Dong, Liyuan Liu, Jingbo Shang
We show that label noise exists in adversarial training. Such label noise is due to the mismatch between the true label distribution of adversarial examples and the label inherited…
Data Quality Matters For Adversarial Training: An Empirical Study
Chengyu Dong, Liyuan Liu, Jingbo Shang
Multiple intriguing problems are hovering in adversarial training, including robust overfitting, robustness overestimation, and robustness-accuracy trade-off. These problems pose g…
Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training
Zichao Li, Liyuan Liu, Chengyu Dong +1
Our goal is to understand why the robustness drops after conducting adversarial training for too long. Although this phenomenon is commonly explained as overfitting, our analysis s…
DPPred: An Effective Prediction Framework with Concise Discriminative Patterns
Jingbo Shang, Meng Jiang, Wenzhu Tong +3
In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models,…
A Parallel and Efficient Algorithm for Learning to Match
Jingbo Shang, Tianqi Chen, Hang Li +2
Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image t…