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Found 22 papers, 5 papers with code
BenchmarkCards: Large Language Model and Risk Reporting
Large language models (LLMs) offer powerful capabilities but also introduce significant risks.
Fast Explanations via Policy Gradient-Optimized Explainer
The challenge of delivering efficient explanations is a critical barrier that prevents the adoption of model explanations in real-world applications.
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
Federated Learning (FL) is a distributed machine learning framework in which a set of local communities collaboratively learn a shared global model while retaining all training data locally within each community.
A Survey of Large Language Models for Graphs
This survey aims to serve as a valuable resource for researchers and practitioners eager to leverage large language models in graph learning, and to inspire continued progress in this dynamic field.
Conformalized Selective Regression
However, this focus neglects the significant influence of model-specific biases on a model's performance.
Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control
We also show how our method improves time requirements by at least a factor of 9 and is a resource-efficient solution that ensures high performance without specialised hardware.
Understanding CNN Fragility When Learning With Imbalanced Data
To demystify CNN decisions on imbalanced data, we focus on their latent features.
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN.
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers
While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs.
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning
A key element in achieving algorithmic fairness with respect to protected groups is the simultaneous reduction of class and protected group imbalance in the underlying training data, which facilitates increases in both model accuracy and fairness.
Efficient Augmentation for Imbalanced Deep Learning
We empirically study a convolutional neural network's internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider for minority classes.
Compositional Training for End-to-End Deep AUC Maximization
The key idea of compositional training is to minimize a compositional objective function, where the outer function corresponds to an AUC loss and the inner function represents a gradient descent step for minimizing a traditional loss, e. g., the cross-entropy (CE) loss.
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases
Multi-hop relation reasoning over knowledge base is to generate effective and interpretable relation prediction through reasoning paths.
Addressing Class-Imbalance Problem in Personalized Ranking
Pairwise ranking models have been widely used to address recommendation problems.
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning
Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommender systems.
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series
The prevalence of wearable sensors (e. g., smart wristband) is creating unprecedented opportunities to not only inform health and wellness states of individuals, but also assess and infer personal attributes, including demographic and personality attributes.
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text
In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences.
LNEMLC: Label Network Embeddings for Multi-Label Classification
Multi-label classification aims to classify instances with discrete non-exclusive labels.
Neural Tensor Factorization
Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data.
Influence of Personal Preferences on Link Dynamics in Social Networks
We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)
It provides a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning, concept drift, and the combined issues of class imbalance and concept drift.
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Our model can predict social status of individuals with 93% accuracy.
