Papers Selection bias
“Selection bias” 태그가 달린 논문 365편 · 필터 해제
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Open-vocabulary Extreme Multi-label Classification (OXMC) extends traditional XMC by allowing prediction beyond an extremely large, predefined label set (typically $10^3$ to $10^{12}$ labels), addressing the dynamic natu…
Extreme Multi-Label ClassificationKeyphrase GenerationMissing LabelsMulti-Label Classification+2GABInsight: Exploring Gender-Activity Binding Bias in Vision-Language Models
Vision-language models (VLMs) are intensively used in many downstream tasks, including those requiring assessments of individuals appearing in the images. While VLMs perform well in simple single-person scenarios, in rea…
Image to textImage-to-Text RetrievalSelection biasText RetrievalOn the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement
Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias are insufficiently handling irrelevant v…
DisentanglementSelection biasLarge Language Models as Co-Pilots for Causal Inference in Medical Studies
The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studi…
Causal InferenceSelection biasAugmented prediction of a true class for Positive Unlabeled data under selection bias
We introduce a new observational setting for Positive Unlabeled (PU) data where the observations at prediction time are also labeled. This occurs commonly in practice -- we argue that the additional information is import…
PredictionSelection biasTowards Systematic Monolingual NLP Surveys: GenA of Greek NLP
Natural Language Processing (NLP) research has traditionally been predominantly focused on English, driven by the availability of resources, the size of the research community, and market demands. Recently, there has bee…
Selection biasSystematic Literature ReviewStable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced b…
counterfactualHeterogeneous Treatment Effect EstimationRepresentation LearningSelection biasProximity 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. Existing methods address this bias by minimizing distribution discrepancies between tr…
counterfactualSelection biasDIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents. But do RAG implementations leave out important information when answering queries that need an integrat…
Information RetrievalRAGRetrievalRetrieval-augmented Generation+1Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study
Utilizing Large Language Models (LLMs) as evaluators to assess the performance of LLMs has garnered attention. However, this kind of evaluation approach is affected by potential biases within LLMs, raising concerns about…
In-Context LearningSelection biasOpen-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena
Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs). Typically, an LLM is given a question and selects the answer deemed most probable after adjustments for factors like length. Unf…
Multiple-choiceSelection biasStrengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
Multiple-Choice Questions (MCQs) constitute a critical area of research in the study of Large Language Models (LLMs). Previous works have investigated the selection bias problem in MCQs within few-shot scenarios, in whic…
Multiple-choiceSelection biasSparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation
This paper introduces a novel framework for reducing variable selection bias by balancing selection frequencies of base-learners in boosting and introduces the sgboost package in R, which implements this framework combin…
Selection biasVariable SelectionJump-teaching: Ultra Efficient and Robust Learning with Noisy Label
Sample selection is the most straightforward technique to combat label noise, aiming to distinguish mislabeled samples during training and avoid the degradation of the robustness of the model. In the workflow, $\textit{s…
Learning with noisy labelsSelection biasRevisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck
As a promising individualized treatment effect (ITE) estimation method, counterfactual regression (CFR) maps individuals' covariates to a latent space and predicts their counterfactual outcomes. However, the selection bi…
counterfactualregressionSelection biasChallenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com
Transformer-based neural networks, empowered by Self-Supervised Learning (SSL), have demonstrated unprecedented performance across various domains. However, related literature suggests that tabular Transformers may strug…
Fraud DetectionSelection biasSelf-Supervised LearningThe MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems
An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices. This paper addresses this issue by introducing a method for collecting user beli…
Recommendation SystemsSelection biasSample Selection Bias in Machine Learning for Healthcare
While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this paper, we focus on sampl…
Selection biasFull Stage Learning to Rank: A Unified Framework for Multi-Stage Systems
The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's returned list of results to be ranked wi…
Information RetrievalLearning-To-RankRe-RankingRetrieval+1A Model-based Multi-Agent Personalized Short-Video Recommender System
Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation session as a Markov decision process and…
Recommendation SystemsReinforcement Learning (RL)Selection bias