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08/08/2023
Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation
Multi-modal recommendation systems, which integrate diverse types of inf...
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08/07/2023
Redundancy-aware Transformer for Video Question Answering
This paper identifies two kinds of redundancy in the current VideoQA par...
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06/05/2023
Discovering Dynamic Causal Space for DAG Structure Learning
Discovering causal structure from purely observational data (i.e., causa...
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03/06/2023
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
Under stringent model type and variable distribution assumptions, differ...
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02/10/2023
Invariant Collaborative Filtering to Popularity Distribution Shift
Collaborative Filtering (CF) models, despite their great success, suffer...
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11/05/2022
Adversarial Causal Augmentation for Graph Covariate Shift
Out-of-distribution (OOD) generalization on graphs is drawing widespread...
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10/20/2022
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
Collaborative filtering (CF) models easily suffer from popularity bias, ...
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06/17/2022
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Traditional biological and pharmaceutical manufacturing plants are contr...
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06/16/2022
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Leading graph contrastive learning (GCL) methods perform graph augmentat...
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06/01/2022
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Bundle recommendation aims to recommend a bundle of related items to use...
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05/31/2022
Differentiable Invariant Causal Discovery
Learning causal structure from observational data is a fundamental chall...
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04/23/2022
Reinforced Causal Explainer for Graph Neural Networks
Explainability is crucial for probing graph neural networks (GNNs), answ...
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01/30/2022
Discovering Invariant Rationales for Graph Neural Networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a ...
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01/21/2022
Deconfounding to Explanation Evaluation in Graph Neural Networks
Explainability of graph neural networks (GNNs) aims to answer “Why the G...
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07/09/2021
Approximation algorithms for the directed path partition problems
Given a directed graph G = (V, E), the k-path partition problem is to fi...
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04/12/2021
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
Gradient-based attribution methods can aid in the understanding of convo...
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07/03/2020
Disentangled Graph Collaborative Filtering
Learning informative representations of users and items from the interac...
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10/06/2019
Approximation algorithms for maximally balanced connected graph partition
Given a simple connected graph G = (V, E), we seek to partition the vert...
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12/21/2018
A local search 4/3-approximation algorithm for the minimum 3-path partition problem
Given a graph G = (V, E), the 3-path partition problem is to find a mini...
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11/03/2018
Improved approximation algorithms for path vertex covers in regular graphs
Given a simple graph G = (V, E) and a constant integer k > 2, the k-path...
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09/15/2018
Approximation algorithms for the three-machine proportionate mixed shop scheduling
A mixed shop is a manufacturing infrastructure designed to process a mix...
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03/07/2018