Search Results for author:
Found 16 papers, 5 papers with code
ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence
To help advance research in political science, we introduce ConfliBERT, a domain-specific pre-trained language model for conflict and political violence.
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints.
Fairness-Aware Domain Generalization under Covariate and Dependence Shifts
Achieving the generalization of an invariant classifier from source domains to shifted target domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning.
Synthesizing Political Zero-Shot Relation Classification via Codebook Knowledge, NLI, and ChatGPT
Our study underscores the efficacy of leveraging transfer learning and existing expertise to enhance research efficiency and scalability in this area.
Towards Fair Disentangled Online Learning for Changing Environments
To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor.
An Automated Vulnerability Detection Framework for Smart Contracts
With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology.
Controllable Fake Document Infilling for Cyber Deception
Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information.
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
Furthermore, to determine a good model parameter at each round, we propose a novel adaptive fairness-aware online meta-learning algorithm, namely FairSAOML, which is able to adapt to changing environments in both bias control and model precision.
Uncertainty-Aware Reliable Text Classification
Extensive empirical experiments demonstrate that our model based on evidential uncertainty outperforms other counterparts for detecting OOD examples.
SetConv: A New Approach for Learning from Imbalanced Data
For many real-world classification problems, e. g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high.
Towards Self-Adaptive Metric Learning On the Fly
In this paper, we present a new online metric learning framework that attempts to tackle the challenge by learning an ANN-based metric with adaptive model complexity from a stream of constraints.
A Primal-Dual Subgradient Approachfor Fair Meta Learning
In this work, we propose a Primal-Dual Fair Meta-learning framework, namely PDFM, which learns to train fair machine learning models using only a few examples based on data from related tasks.
Deep Learning on Knowledge Graph for Recommender System: A Survey
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS).
GUIDEGAN: ATTENTION BASED SPATIAL GUIDANCE FOR IMAGE-TO-IMAGE TRANSLATION
To tackle this problem, we propose a GuideGAN based on attention mechanism.
Generative Adversarial Network
Image-to-Image Translation
+1
Co-Representation Learning For Classification and Novel Class Detection via Deep Networks
In this paper, we address this key challenge by proposing a semi-supervised multi-task learning framework called \sysname{} which aims to intrinsically search for a latent space suitable for detecting labels of instances from both known and unknown classes.
Cause Identification from Aviation Safety Incident Reports via Weakly Supervised Semantic Lexicon Construction
The Aviation Safety Reporting System collects voluntarily submitted reports on aviation safety incidents to facilitate research work aiming to reduce such incidents.
