Search Results for author:
Found 22 papers, 4 papers with code
Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
We utilize graph attention network and Wasserstein distance to address the limitations of LightGCN and Kullback-Leibler divergence (KL) divergence to learn Gaussian embedding for each user and item.
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.
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.
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.
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.
