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08/29/2023
Heterogeneous Multi-Task Gaussian Cox Processes
This paper presents a novel extension of multi-task Gaussian Cox process...
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08/21/2023
Towards Accelerated Model Training via Bayesian Data Selection
Mislabeled, duplicated, or biased data in real-world scenarios can lead ...
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07/11/2023
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
Different distribution shifts require different algorithmic and operatio...
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07/08/2023
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Heterogeneous Information Networks (HINs) are information networks with ...
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06/04/2023
Adaptive and Personalized Exercise Generation for Online Language Learning
Adaptive learning aims to provide customized educational activities (e.g...
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05/30/2023
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
Competitions for shareable and limited resources have long been studied ...
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05/25/2023
Meta Adaptive Task Sampling for Few-Domain Generalization
To ensure the out-of-distribution (OOD) generalization performance, trad...
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05/24/2023
Rethinking the Evaluation Protocol of Domain Generalization
Domain generalization aims to solve the challenge of Out-of-Distribution...
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05/22/2023
RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
The fixed-size context of Transformer makes GPT models incapable of gene...
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05/21/2023
Exploring and Exploiting Data Heterogeneity in Recommendation
Massive amounts of data are the foundation of data-driven recommendation...
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04/20/2023
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Large-scale pre-trained models have achieved remarkable success in a var...
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04/01/2023
Predictive Heterogeneity: Measures and Applications
As an intrinsic and fundamental property of big data, data heterogeneity...
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03/03/2023
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
Recently, flat minima are proven to be effective for improving generaliz...
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02/10/2023
Confidence-based Reliable Learning under Dual Noises
Deep neural networks (DNNs) have achieved remarkable success in a variet...
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12/02/2022
Stable Learning via Sparse Variable Independence
The problem of covariate-shift generalization has attracted intensive re...
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11/03/2022
PointSee: Image Enhances Point Cloud
There is a trend to fuse multi-modal information for 3D object detection...
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10/28/2022
Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing
Image dehazing is fundamental yet not well-solved in computer vision. Mo...
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10/15/2022
Product Ranking for Revenue Maximization with Multiple Purchases
Product ranking is the core problem for revenue-maximizing online retail...
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06/07/2022
Distributionally Invariant Learning: Rationalization and Practical Algorithms
The invariance property across environments is at the heart of invariant...
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05/20/2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business ...
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04/17/2022
NICO++: Towards Better Benchmarking for Domain Generalization
Despite the remarkable performance that modern deep neural networks have...
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03/27/2022
Towards Domain Generalization in Object Detection
Despite the striking performance achieved by modern detectors when train...
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03/11/2022
ZIN: When and How to Learn Invariance by Environment Inference?
It is commonplace to encounter heterogeneous data, of which some aspects...
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02/09/2022
Regulatory Instruments for Fair Personalized Pricing
Personalized pricing is a business strategy to charge different prices t...
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02/08/2022
CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
In spite of the tremendous development of recommender system owing to th...
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12/23/2021
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?
Geometric deep learning, i.e., designing neural networks to handle the u...
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11/20/2021
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Graph Neural Networks (GNNs) are proposed without considering the agnost...
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11/03/2021
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Knowledge graph is generally incorporated into recommender systems to im...
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10/24/2021
Kernelized Heterogeneous Risk Minimization
The ability to generalize under distributional shifts is essential to re...
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10/22/2021
ListReader: Extracting List-form Answers for Opinion Questions
Question answering (QA) is a high-level ability of natural language proc...
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10/21/2021
Topic-Guided Abstractive Multi-Document Summarization
A critical point of multi-document summarization (MDS) is to learn the r...
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08/31/2021
Towards Out-Of-Distribution Generalization: A Survey
Classic machine learning methods are built on the i.i.d. assumption that...
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07/13/2021
Domain-Irrelevant Representation Learning for Unsupervised Domain Generalization
Domain generalization (DG) aims to help models trained on a set of sourc...
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06/30/2021
Distributionally Robust Learning with Stable Adversarial Training
Machine learning algorithms with empirical risk minimization are vulnera...
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05/26/2021
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
With the success of the graph embedding model in both academic and indus...
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05/09/2021
Heterogeneous Risk Minimization
Machine learning algorithms with empirical risk minimization usually suf...
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04/16/2021
Deep Stable Learning for Out-Of-Distribution Generalization
Approaches based on deep neural networks have achieved striking performa...
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04/07/2021
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
In recent years, there are great interests as well as challenges in appl...
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03/28/2021
Accurate and Reliable Forecasting using Stochastic Differential Equations
It is critical yet challenging for deep learning models to properly char...
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02/07/2021
Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
Recently, real-world recommendation systems need to deal with millions o...
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01/28/2021
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Graph Neural Networks (GNNs) have received considerable attention on gra...
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10/23/2020
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
The original design of Graph Convolution Network (GCN) couples feature t...
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10/15/2020
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching
Sentence matching is a fundamental task of natural language processing w...
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10/13/2020
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks
Text summarization aims to compress a textual document to a short summar...
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09/05/2020
A Simple and General Graph Neural Network with Stochastic Message Passing
Graph neural networks (GNNs) are emerging machine learning models on gra...
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08/27/2020
Adversarial Eigen Attack on Black-Box Models
Black-box adversarial attack has attracted a lot of research interests f...
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07/05/2020
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have gained great popularity in tack...
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06/18/2020
Algorithmic Decision Making with Conditional Fairness
Nowadays fairness issues have raised great concerns in decision-making s...
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06/18/2020
Calibrated Reliable Regression using Maximum Mean Discrepancy
Accurate quantification of uncertainty is crucial for real-world applica...
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06/09/2020