activity
20212023
most citedExplainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

9 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI20231 cited

Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport

Zihao Wang, Weizhi Fei, Hang Yin +3

Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-b…

cs.CV2023

Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models

Wenjie Yin, Ruibo Tu, Hang Yin +3

Data-driven and controllable human motion synthesis and prediction are active research areas with various applications in interactive media and social robotics. Challenges remain i…

cs.LG2022

Behavioral graph fraud detection in E-commerce

Hang Yin, Zitao Zhang, Zhurong Wang +5

In e-commerce industry, graph neural network methods are the new trends for transaction risk modeling.The power of graph algorithms lie in the capability to catch transaction linki…

cs.LG20221 cited

Back to the Manifold: Recovering from Out-of-Distribution States

Alfredo Reichlin, Giovanni Luca Marchetti, Hang Yin +2

Learning from previously collected datasets of expert data offers the promise of acquiring robotic policies without unsafe and costly online explorations. However, a major challeng…

cs.LG2022

On the Subspace Structure of Gradient-Based Meta-Learning

Gustaf Tegnér, Alfredo Reichlin, Hang Yin +2

In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods. Previous work has noticed how, for the cas…

cs.IT2021

Adaptive List Decoder with Flip Operations for Polar Codes

Yansong Lv, Hang Yin, Zhanxin Yang +3

Successive cancellation list decoders with flip operations (SCL-Flip) can utilize re-decoding attempts to significantly improve the error-correction performance of polar codes. How…