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06/08/2023
Conservative Prediction via Data-Driven Confidence Minimization
Errors of machine learning models are costly, especially in safety-criti...
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04/08/2023
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
As machine learning has been deployed ubiquitously across applications i...
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11/25/2022
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Distribution shift occurs when the test distribution differs from the tr...
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11/16/2022
Holistic Evaluation of Language Models
Language models (LMs) are becoming the foundation for almost all major l...
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10/25/2022
Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations
There is an inescapable long-tailed class-imbalance issue in many real-w...
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10/20/2022
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
A common approach to transfer learning under distribution shift is to fi...
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10/11/2022
C-Mixup: Improving Generalization in Regression
Improving the generalization of deep networks is an important open chall...
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10/11/2022
Knowledge-Driven New Drug Recommendation
Drug recommendation assists doctors in prescribing personalized medicati...
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05/27/2022
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
Spatio-temporal graph learning is a key method for urban computing tasks...
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03/22/2022
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Building models of natural language processing (NLP) is challenging in l...
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02/07/2022
Diversify and Disambiguate: Learning From Underspecified Data
Many datasets are underspecified, which means there are several equally ...
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01/02/2022
Improving Out-of-Distribution Robustness via Selective Augmentation
Machine learning algorithms typically assume that training and test exam...
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10/26/2021
Meta-learning with an Adaptive Task Scheduler
To benefit the learning of a new task, meta-learning has been proposed t...
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09/10/2021
Knowledge-Aware Meta-learning for Low-Resource Text Classification
Meta-learning has achieved great success in leveraging the historical le...
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06/04/2021
Meta-Learning with Fewer Tasks through Task Interpolation
Meta-learning enables algorithms to quickly learn a newly encountered ta...
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10/22/2020
Online Structured Meta-learning
Learning quickly is of great importance for machine intelligence deploye...
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08/01/2020
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records
Recently, E-commerce platforms have extensive impacts on our human life....
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07/26/2020
Don't Overlook the Support Set: Towards Improving Generalization in Meta-learning
Meta-learning has proven to be a powerful paradigm for transferring the ...
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06/28/2020
Graph Convolutional Networks against Degree-Related Biases
In recent years, Graph Convolutional Networks (GCNs) show competitive pe...
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01/03/2020
Automated Relational Meta-learning
In order to efficiently learn with small amount of data on new tasks, me...
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11/26/2019
Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) serve as useful resources for various natural lan...
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11/22/2019
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Multivariate time series (MTS) forecasting is widely used in various dom...
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10/07/2019
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, ...
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09/07/2019
Transferable Neural Processes for Hyperparameter Optimization
Automated machine learning aims to automate the whole process of machine...
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08/29/2019
Targeted Source Detection for Environmental Data
In the face of growing needs for water and energy, a fundamental underst...
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08/20/2019
Robust Graph Neural Network Against Poisoning Attacks via Transfer Learning
Graph neural networks (GNNs) are widely used in many applications. Howev...
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05/13/2019
Hierarchically Structured Meta-learning
In order to learn quickly with few samples, meta-learning utilizes prior...
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03/15/2019
Predicting Academic Performance for College Students: A Campus Behavior Perspective
Detecting abnormal behaviors of students in time and providing personali...
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02/25/2019
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
Real-time traffic volume inference is key to an intelligent city. It is ...
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01/24/2019
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Spatial-temporal prediction is a fundamental problem for constructing sm...
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03/03/2018
Modeling Spatial-Temporal Dynamics for Traffic Prediction
Spatial-temporal prediction has many applications such as climate foreca...
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02/23/2018