Yang Gao

Understanding Sequential User Behavior in Social Computing: To Answer or to Vote?

2015 · IEEE Transactions on Network Science and Engineering

Understanding how users participate is of key importance to social computing systems since their value is created from user contributions. In many social computing systems, users decide sequentially whether to participate or not and, if …

Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion

2017

Free-text keystroke is a form of behavioral biometrics which has great potential for addressing the security limitations of conventional one-time authentication by continuously monitoring the user's typing behaviors. This paper presents a new, enhanced continuous …

Target situation assessment based on twice variable weight strategy

2018 · IOP Conference Series Materials Science and Engineering

How to reasonably calculate index weight is an important topic in target situation assessment and other information processing studies. The traditional method is to obtain the subjective weight and the objective weight respectively, and then …

SIM: Open-World Multi-Task Stream Classifier with Integral Similarity Metrics

2019

One of the key challenges of performing label predictions over a data stream is concerned with the emergence of instances belonging to unobserved (or novel) classes over time. Although existing studies have proposed various solutions …

MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization

2022 · arXiv (Cornell University)

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore …

PLACE Dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization

2023 · ACM Transactions on Multimedia Computing Communications and Applications

Domain generalization (DG) aims to learn a generic model from multiple observed source domains that generalizes well to arbitrary unseen target domains without further training. The major challenge in DG is that the model inevitably …

Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning

2024 · arXiv (Cornell University)

In representation learning, a disentangled representation is highly desirable as it encodes generative factors of data in a separable and compact pattern. Researchers have advocated leveraging disentangled representations to complete downstream tasks with encouraging empirical …

Scalable and Domain-General Abstractive Proposition Segmentation

2024 · arXiv (Cornell University)

Segmenting text into fine-grained units of meaning is important to a wide range of NLP applications. The default approach of segmenting text into sentences is often insufficient, especially since sentences are usually complex enough to …

Causality-Aware Efficient Exploration for Cooperative Multi-Agent Reinforcement Learning

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Exploration is critical for cooperative multi agent reinforcement learning (MARL) to improve sample efficiency. However, existing intrinsic motivation based exploration strategies in MARL overlook the causal relationships among agents, global states, and rewards, suffering from …

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

2019

Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing …