Search Results for author:
Found 101 papers, 59 papers with code
Efficient Hyper-parameter Search for Knowledge Graph Embedding
Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage.
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Sample selection approaches are popular in robust learning from noisy labels.
Superpose Singular Features for Model Merging
Model merging is a critical technique for combining the capabilities of multiple fine-tuned models without requiring additional training.
Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling
To tackle this issue, in this paper, we generalize the hyperedge expansion in hypergraph learning and propose an equivalent transformation for HKG modeling, referred to as TransEQ.
Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction
Subgraph-based methods have proven to be effective and interpretable in predicting drug-drug interactions (DDIs), which are essential for medical practice and drug development.
Benchmarking Graph Learning for Drug-Drug Interaction Prediction
However, evaluating existing methods has several limitations, such as the absence of a unified comparison framework for DDI prediction methods, lack of assessments in meaningful real-world scenarios, and insufficient exploration of side information usage.
Neural Symbolic Regression of Complex Network Dynamics
Complex networks describe important structures in nature and society, composed of nodes and the edges that connect them.
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning
Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans.
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation
Equipped with this tokenizer, UniMoT can unify molecule and text modalities under a shared token representation and an autoregressive training paradigm, enabling it to interpret molecules as a foreign language and generate them as text.
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions
While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions.
Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks
The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models.
HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment
As LLMs are predominantly trained with 1D text data, most existing approaches adopt a graph neural network to represent a graph as a series of node tokens and feed these tokens to LLMs for graph-language alignment.
Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction
Link prediction is a fundamental task in graph learning, inherently shaped by the topology of the graph.
Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph
The task of reasoning over Knowledge Graphs (KGs) poses a significant challenge for Large Language Models (LLMs) due to the complex structure and large amounts of irrelevant information.
Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences.
Graph Unitary Message Passing
Then, unitary adjacency matrix is obtained with a unitary projection algorithm, which is implemented by utilizing the intrinsic structure of unitary adjacency matrix and allows GUMP to be permutation-equivariant.
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs
To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query.
Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nodes and edges.
Ranked #2 on
Node Property Prediction
on ogbn-mag
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models
This paper proposes a novel conceptual prototype for designing versatile graph learning methods with LLMs, with a particular focus on the "where" and "how" perspectives.
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
We propose an active defense strategy, where agents automatically reduce the impact of potentially harmful messages on the final decision.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning
Thus, the lack of DDIs is implicitly compensated by the enriched drug representations and propagated drug similarities.
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development.
Combating Bilateral Edge Noise for Robust Link Prediction
To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse.
Ensemble Learning for Graph Neural Networks
Graph Neural Networks (GNNs) have shown success in various fields for learning from graph-structured data.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
We also propose a regularizer to align the model with graph structure.
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
Ranked #1 on
Link Property Prediction
on ogbl-biokg
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
We then adopt a hierarchical adaptation mechanism to adapt the encoder at task-level and the predictor at query-level by the unified GNN adapter.
A Versatile Graph Learning Approach through LLM-based Agent
In this paper, we propose to explore versatile graph learning approaches with LLM-based agents, and the key insight is customizing the graph learning procedures for diverse graphs and tasks.
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored.
Automated 3D Pre-Training for Molecular Property Prediction
Next, to automatically fuse these three generative tasks, we design a surrogate metric using the \textit{total energy} to search for weight distribution of the three pretext task since total energy corresponding to the quality of 3D conformer. Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT compared to various pre-training baselines.
ColdNAS: Search to Modulate for User Cold-Start Recommendation
Instead of using a fixed modulation function and deciding modulation position by expertise, we propose a modulation framework called ColdNAS for user cold-start problem, where we look for proper modulation structure, including function and position, via neural architecture search.
Understanding Expressivity of GNN in Rule Learning
These results further inspire us to propose a novel labeling strategy to learn more rules in KG reasoning.
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models.
Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task.
Learning Symbolic Models for Graph-structured Physical Mechanism
The essence of our method is to model the formula skeleton with a message-passing flow, which helps transform the discovery of the skeleton into the search for the message-passing flow.
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach
Despite the success, we observe two aspects that can be further improved: (a) enhancing the ego feature information extraction from node itself which is more reliable in extracting the intra-class information; (b) designing node-wise GNNs can better adapt to the nodes with different homophily ratios.
Ranked #4 on
Node Classification
on Actor
Search to Pass Messages for Temporal Knowledge Graph Completion
In particular, we develop a generalized framework to explore topological and temporal information in TKGs.
Ranked #1 on
Link Prediction
on GDELT
Searching a High-Performance Feature Extractor for Text Recognition Network
We design a domain-specific search space by exploring principles for having good feature extractors.
Neural Architecture Search
Vocal Bursts Intensity Prediction
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph.
AutoWeird: Weird Translational Scoring Function Identified by Random Search
This scoring function, called AutoWeird, only uses tail entity and relation in a triplet to compute its plausibility score.
Ranked #2 on
Link Property Prediction
on ogbl-wikikg2
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search
Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search).
AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning
An important design component of GNN-based KG reasoning methods is called the propagation path, which contains a set of involved entities in each propagation step.
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
While the optimization problem is nonconvex and nonsmooth, we show that its critical points still have good statistical performance on the tensor completion problem.
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently.
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning.
Progressive Feature Interaction Search for Deep Sparse Network
Deep sparse networks (DSNs), of which the crux is exploring the high-order feature interactions, have become the state-of-the-art on the prediction task with high-sparsity features.
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Short text classification is a fundamental task in natural language processing.
Heterogeneous Graph-Based Multimodal Brain Network Learning
Other experiments also indicate that our proposed model with a pretraining strategy alleviates the problem of limited labelled data and yields a significant improvement in accuracy.
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
A public instance of Codabench (https://www. codabench. org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats.
Pooling Architecture Search for Graph Classification
To address this problem, we propose to use neural architecture search (NAS) to search for adaptive pooling architectures for graph classification.
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance.
Knowledge Graph Reasoning with Relational Digraph
In this paper, we introduce a novel relational structure, i. e., relational directed graph (r-digraph), which is composed of overlapped relational paths, to capture the KG's local evidence.
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
the target property, such that the limited labels can be effectively propagated among similar molecules.
Bilinear Scoring Function Search for Knowledge Graph Learning
We first set up a search space for AutoBLM by analyzing existing scoring functions.
Ranked #6 on
Link Property Prediction
on ogbl-biokg
Efficient Data-specific Model Search for Collaborative Filtering
In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones.
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature.
Searching to Sparsify Tensor Decomposition for N-ary Relational Data
Recently, tensor decomposition methods have been introduced into N-ary relational data and become state-of-the-art on embedding learning.
Role-Aware Modeling for N-ary Relational Knowledge Bases
N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts.
Search to aggregate neighborhood for graph neural network
In this work, to obtain the data-specific GNN architectures and address the computational challenges facing by NAS approaches, we propose a framework, which tries to Search to Aggregate NEighborhood (SANE), to automatically design data-specific GNN architectures.
Topology-aware Tensor Decomposition for Meta-graph Learning
The proposed topology-aware tensor decomposition is easy to use and simple to implement, and it can be taken as a plug-in part to upgrade many existing works, including node classification and recommendation on heterogeneous graphs.
Differentiable Learning of Graph-like Logical Rules from Knowledge Graphs
The score also helps relax the discrete space into a continuous one and can be uniformly transformed into matrix form by the Einstein summation convention.
TRACE: Tensorizing and Generalizing Supernets from Neural Architecture Search
Recently, a special kind of graph, i. e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching better architectures for computer vision (CV) and natural language processing (NLP) tasks.
Efficient Graph Neural Architecture Search
To obtain state-of-the-art (SOAT) data-specific GNN architectures, researchers turn to the neural architecture search (NAS) methods.
Combining Self-Supervised and Supervised Learning with Noisy Labels
Thus, inspired by the observation that classifier is more robust to noisy labels while representation is much more fragile, and by the recent advances of self-supervised representation learning (SSRL) technologies, we design a new method, i. e., CS$^3$NL, to obtain representation by SSRL without labels and train the classifier directly with noisy labels.
A Survey of Label-noise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios.
Automorphic Equivalence-aware Graph Neural Network
Distinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e. g., computational biologist and social network analysis.
Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding
In this paper, motivated by the observation that negative triplets with large gradients are important but rare, we propose to directly keep track of them with the cache.
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising, compressed HSI reconstruction and inpainting.
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
Specifically, we search for a meta graph, which can capture more complex semantic relations than a meta path, to determine how graph neural networks (GNNs) propagate messages along different types of edges.
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data.
Simplifying Architecture Search for Graph Neural Network
Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios.
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion
Extensive low-rank matrix completion experiments on a number of synthetic and real-world data sets show that the proposed method obtains state-of-the-art recovery performance while being the fastest in comparison to existing low-rank matrix learning methods.
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases
With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a. k. a knowledge graph) with powerful tensor decomposition related methods.
Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network
Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation.
AutoSTR: Efficient Backbone Search for Scene Text Recognition
In this work, inspired by the success of neural architecture search (NAS), which can identify better architectures than human-designed ones, we propose automated STR (AutoSTR) to search data-dependent backbones to boost text recognition performance.
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along the paths.
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Sample selection approaches are popular in robust learning from noisy labels.
Efficient Neural Interaction Function Search for Collaborative Filtering
Motivated by the recent success of automated machine learning (AutoML), we propose in this paper the search for simple neural interaction functions (SIF) in CF.
Efficient Neural Architecture Search via Proximal Iterations
Recently, DARTS, which constructs a differentiable search space and then optimizes it by gradient descent, can obtain high-performance architecture and reduces the search time to several days.
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions.
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses.
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.
Ranked #1 on
Link Prediction
on FB15k
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small.
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Negative sampling, which samples negative triplets from non-observed ones in the training data, is an important step in KG embedding.
Ranked #6 on
Link Prediction
on FB15k
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
This is done by first learning a low-dimensional projection and the related reduced image from the noisy HSI.
Ranked #10 on
Hyperspectral Image Denoising
on ICVL-HSI-Gaussian50
Differential Private Stack Generalization with an Application to Diabetes Prediction
To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms.
Automated Machine Learning: From Principles to Practices
Machine learning (ML) methods have been developing rapidly, but configuring and selecting proper methods to achieve a desired performance is increasingly difficult and tedious.
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.
FasTer: Fast Tensor Completion with Nonconvex Regularization
Due to the easy optimization, the convex overlapping nuclear norm has been popularly used for tensor completion.
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing.
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.
Ranked #9 on
Learning with noisy labels
on CIFAR-10N-Random3
Side Information Fusion for Recommender Systems over Heterogeneous Information Network
Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users' preferences (ratings) based on their past behaviors.
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Based on it, we develop a proximal gradient algorithm (and its accelerated variant) with inexact proximal splitting and prove that a convergence rate of O(1/T) where T is the number of iterations is guaranteed.
Scalable Online Convolutional Sparse Coding
Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data.
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
The proximal gradient algorithm has been popularly used for convex optimization.
Optimization and Control
Loss-aware Binarization of Deep Networks
Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time.
Fast Learning with Nonconvex L1-2 Regularization
In this paper, we show that a closed-form solution can be derived for the proximal step associated with this regularizer.
Learning of Generalized Low-Rank Models: A Greedy Approach
Learning of low-rank matrices is fundamental to many machine learning applications.
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
The nonconvex regularizer is then transformed to a familiar convex regularizer, while the resultant loss function can still be guaranteed to be smooth.
Fast Low-Rank Matrix Learning with Nonconvex Regularization
This allows the use of power method to approximate the SVD efficiently.
