Search Results for author:
Found 63 papers, 22 papers with code
Harnessing Privileged Information for Hyperbole Detection
The detection of hyperbole is an important stepping stone to understanding the intentions of a hyperbolic utterance.
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
We argue that these factual reasoning-based explanations cannot answer critical what-if questions: What would happen to the GNN's decision if we were to alter the code graph into alternative structures?
CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction
Granularity and accuracy are two crucial factors for crime event prediction.
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare.
Challenging Low Homophily in Social Recommendation
To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models.
APLe: Token-Wise Adaptive for Multi-Modal Prompt Learning
Pre-trained Vision-Language (V-L) models set the benchmark for generalization to downstream tasks among the noteworthy contenders.
Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability.
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification
Although some approaches have attempted to address this problem through single-stage clustering as an intermediate training step coupled with a pre-trained language model, which generates pseudo-labels to improve classification, these methods are often error-prone due to the limitations of the clustering algorithms.
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.
An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance
Emotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis.
Ranked #1 on
Speech Emotion Recognition
on EMODB
(using extra training data)
Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition
We attempt to leverage the Mel spectrogram by decomposing distinguishable acoustic features for exploitation in our proposed architecture, which includes a novel feature map generator algorithm, a CNN-based network feature extractor and a multi-layer perceptron (MLP) classifier.
Predicting Financial Literacy via Semi-supervised Learning
We propose the SMOGN-COREG model for semi-supervised regression, applying SMOGN to deal with unbalanced datasets and a nonparametric multi-learner co-regression (COREG) algorithm for labeling.
Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data
Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91. 2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models.
GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation
The objective of spatiotemporal imputation is to estimate these missing values by understanding the inherent spatial and temporal relationships in the observed multivariate time series.
Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation
On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy.
LLaMA-E: Empowering E-commerce Authoring with Multi-Aspect Instruction Following
E-commerce authoring involves creating attractive, abundant, and targeted promotional content to drive product sales.
Neural Causal Graph Collaborative Filtering
In this work, we propose to integrate causal modeling with the learning process of GCN-based GCF models, leveraging causality-aware graph embeddings to capture complex causal relations in recommendations.
Counterfactual Explanation for Fairness in Recommendation
The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes.
Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions
Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period.
Causal Analysis of Customer Churn Using Deep Learning
Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives.
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e. g., dimension \& number of hidden layers).
Achieving Counterfactual Fairness with Imperfect Structural Causal Model
Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i. e., what if the individual belongs to other sensitive groups).
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome.
COV19IR : COVID-19 Domain Literature Information Retrieval
Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval.
Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks
In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations.
Improved Churn Causal Analysis Through Restrained High‑Dimensional Feature Space Efects in Financial Institutions
We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems.
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data.
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
We also deploy the explanation policy to a recommendation model to enhance the recommendation.
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies.
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).
DialMed: A Dataset for Dialogue-based Medication Recommendation
Therefore, we make the first attempt to recommend medications with the conversations between doctors and patients.
Graph Masked Autoencoders with Transformers
In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations.
What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code
In this paper, we conduct a thorough structural analysis aiming to provide an interpretation of pre-trained language models for source code (e. g., CodeBERT, and GraphCodeBERT) from three distinctive perspectives: (1) attention analysis, (2) probing on the word embedding, and (3) syntax tree induction.
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' true intent and thus deteriorate the recommendation effectiveness.
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction.
Dual Space Graph Contrastive Learning
In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Cross-Language Binary-Source Code Matching with Intermediate Representations
Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment.
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.
Hilbert Sinkhorn Divergence for Optimal Transport
Sinkhorn divergence has become a very popular metric to compare probability distributions in optimal transport.
Stochastic Intervention for Causal Inference via Reinforcement Learning
In our study, we advance the causal inference research by proposing a new effective framework to estimate the treatment effect on stochastic intervention.
Stochastic Intervention for Causal Effect Estimation
Central to these applications is the treatment effect estimation of intervention strategies.
DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media
Twitter is currently a popular online social media platform which allows users to share their user-generated content.
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems.
Be Causal: De-biasing Social Network Confounding in Recommendation
A common practice to address MNAR is to treat missing entries from the so-called "exposure" perspective, i. e., modeling how an item is exposed (provided) to a user.
Causality-based Counterfactual Explanation for Classification Models
Accordingly, the gradient-free methods are proposed to handle the categorical variables, which however have several major limitations: 1) causal relationships among features are typically ignored when generating the counterfactuals, possibly resulting in impractical guidelines for decision-makers; 2) the counterfactual explanation algorithm requires a great deal of effort into parameter tuning for dertermining the optimal weight for each loss functions which must be conducted repeatedly for different datasets and settings.
TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers
In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers).
Hyperbolic Neural Collaborative Recommender
In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation.
A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics
Learning and analyzing rap lyrics is a significant basis for many web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web.
Temporal Meta-path Guided Explainable Recommendation
This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.
Social and Information Networks
FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint
In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints.
Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection
Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy.
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction.
Causality Learning: A New Perspective for Interpretable Machine Learning
Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc.
BIG-bench Machine Learning
Interpretable Machine Learning
+2
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.
Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation
To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation, namely SIAGR (short for "Social Influence-based Attentive Group Recommendation"), which takes attention mechanisms and the popular method (BERT) as the aggregation representation for group profile modeling.
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.
Target-Independent Active Learning via Distribution-Splitting
By discovering the connections between hypothesis and input distribution, we map the volume of version space into the number density and propose a target-independent distribution-splitting strategy with the following advantages: 1) provide theoretical guarantees on reducing label complexity and error rate as volume-splitting; 2) break the curse of initial hypothesis; 3) provide model guidance for a target-independent AL algorithm in real AL tasks.
A Structured Perspective of Volumes on Active Learning
In this paper, we approximate the version space to a structured {hypersphere} that covers most of the hypotheses, and then divide the available AL sampling approaches into two kinds of strategies: Outer Volume Sampling and Inner Volume Sampling.
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering
Then, instead of merging the sequence into a single vector with pooling operation, soft alignments between words from the question and the relation are learned.
CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data
In this paper, we propose an innovative and effective clustering framework based on self-adaptive labeling (CSAL) which integrates clustering and classification on unlabeled data.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
Coupled Item-based Matrix Factorization
The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations.
