Search Results for author:
Found 37 papers, 13 papers with code
Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence
To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item's exposure to the user's neighbors; and (2) adjusting the exposure of a target item to target users' neighbors to trade off steering performance and the damage to the neighbors' experience.
From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents
Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption.
MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models
We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models.
Leveraging Weak Cross-Modal Guidance for Coherence Modelling via Iterative Learning
The iterative cross-modal boosting also functions in inference to further enhance coherence prediction in each modality.
Causal Inference with Complex Treatments: A Survey
Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education.
Proximity Matters: Local Proximity Preserved Balancing for Treatment Effect Estimation
Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias.
Debiased Recommendation with Noisy Feedback
Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate.
Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow
After that, we prove that the imputation procedure of KnewImp can be derived from another cost functional related to the joint distribution, eliminating the need for the mask matrix and hence naturally addressing issue (2).
Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation
Current methods mainly leverage aggregation functions invented by federated vision community to aggregate parameters from similar clients, e. g., clustering aggregation.
On the Maximal Local Disparity of Fairness-Aware Classifiers
Fairness has become a crucial aspect in the development of trustworthy machine learning algorithms.
Attaining Human`s Desirable Outcomes in Human-AI Interaction via Structural Causal Games
In human-AI interaction, a prominent goal is to attain human`s desirable outcome with the assistance of AI agents, which can be ideally delineated as a problem of seeking the optimal Nash Equilibrium that matches the human`s desirable outcome.
Explainable Few-shot Knowledge Tracing
Knowledge tracing (KT), aiming to mine students' mastery of knowledge by their exercise records and predict their performance on future test questions, is a critical task in educational assessment.
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation
Out-of-distribution (OOD) problems in few-shot classification (FSC) occur when novel classes sampled from testing distributions differ from base classes drawn from training distributions, which considerably degrades the performance of deep learning models deployed in real-world applications.
Debiased Collaborative Filtering with Kernel-Based Causal Balancing
Inspired by these gaps, we propose to approximate the balancing functions in reproducing kernel Hilbert space and demonstrate that, based on the universal property and representer theorem of kernel functions, the causal balancing constraints can be better satisfied.
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
On this basis, we propose a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect.
Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions.
Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science.
Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other.
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness.
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction.
FreDF: Learning to Forecast in Frequency Domain
Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences.
TransFR: Transferable Federated Recommendation with Pre-trained Language Models
To this end, we propose a transferable federated recommendation model with universal textual representations, TransFR, which delicately incorporates the general capabilities empowered by pre-trained language models and the personalized abilities by fine-tuning local private data.
SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors
We propose SceneTex, a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors.
Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series
Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges.
Pareto Invariant Representation Learning for Multimedia Recommendation
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder.
Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval
Specifically, on two key tasks, \textit{i. e.}, image-to-text and text-to-image retrieval, HAT achieves 7. 6\% and 16. 7\% relative score improvement of Recall@1 on MSCOCO, and 4. 4\% and 11. 6\% on Flickr30k respectively.
Your Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination
Most existing image-text matching methods adopt triplet loss as the optimization objective, and choosing a proper negative sample for the triplet of <anchor, positive, negative> is important for effectively training the model, e. g., hard negatives make the model learn efficiently and effectively.
ConvFormer: Revisiting Transformer for Sequential User Modeling
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences.
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety.
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance.
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction.
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.
Model Predictive Robustness of Signal Temporal Logic Predicates
We evaluate our approach for the use case of autonomous driving with predicates used in formalized traffic rules on a recorded dataset, which highlights the advantage of our approach compared to traditional approaches in terms of precision.
Multiple Robust Learning for Recommendation
Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities.
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.
