Search Results for author: Nitesh Chawla

Found 18 papers, 4 papers with code

Conformalized Selective Regression

no code implementations26 Feb 2024 Anna Sokol, Nuno Moniz, Nitesh Chawla

However, this focus neglects the significant influence of model-specific biases on a model's performance.

Conformal Prediction Fairness +1

Boosting Graph Neural Networks via Adaptive Knowledge Distillation

no code implementations12 Oct 2022 Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh Chawla

In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN.

Graph Classification Graph Mining +3

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

no code implementations18 Jul 2022 Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh Chawla, Jane Cleland-Huang

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.

Anomaly Detection Time Series +1

Efficient Augmentation for Imbalanced Deep Learning

1 code implementation13 Jul 2022 Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh Chawla

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.

Data Augmentation

Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning

1 code implementation13 Jul 2022 Damien Dablain, Bartosz Krawczyk, Nitesh Chawla

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.

Fairness

Compositional Training for End-to-End Deep AUC Maximization

no code implementations ICLR 2022 Zhuoning Yuan, Zhishuai Guo, Nitesh Chawla, Tianbao Yang

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.

Image Classification Medical Image Classification +1

Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning

no code implementations12 Mar 2020 Mandana Saebi, Steven Krieg, Chuxu Zhang, Meng Jiang, Nitesh Chawla

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.

Knowledge Graphs Question Answering +4

Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series

no code implementations10 Feb 2020 Xian Wu, Chao Huang, Pablo Roblesgranda, Nitesh Chawla

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.

Representation Learning Time Series +1

Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text

no code implementations IJCNLP 2019 Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh Chawla, Meng Jiang

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.

TAG valid

Neural Tensor Factorization

no code implementations13 Feb 2018 Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh Chawla

Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data.

Collaborative Filtering Link Prediction +1

Influence of Personal Preferences on Link Dynamics in Social Networks

no code implementations21 Sep 2017 Ashwin Bahulkar, Boleslaw K. Szymanski, Nitesh Chawla, Omar Lizardo, Kevin Chan

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.

Attribute

Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)

no code implementations28 Jul 2017 Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao

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.

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