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Found 9 papers, 2 papers with code
(Empirical) Bayes Approaches to Parallel Trends
We consider Bayes and Empirical Bayes (EB) approaches for dealing with violations of parallel trends.
Testing Mechanisms
Economists are often interested in the mechanisms by which a particular treatment affects an outcome.
Denoising Task Difficulty-based Curriculum for Training Diffusion Models
By organizing timesteps or noise levels into clusters and training models with descending orders of difficulty, we facilitate an order-aware training regime, progressing from easier to harder denoising tasks, thereby deviating from the conventional approach of training diffusion models simultaneously across all timesteps.
Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models
Shrinkage methods are frequently used to estimate fixed effects to reduce the noisiness of the least squares estimators.
Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach
Indeed, when naively training the diagnostic model using CAT response data, we observe that item profiles deviate significantly from the ground-truth.
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function
Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing that of negative ones.
Towards an Efficient ML System: Unveiling a Trade-off between Task Accuracy and Engineering Efficiency in a Large-scale Car Sharing Platform
Under various image recognition tasks in the real world car-sharing platform, our study illustrates how we established the proposed system and lessons learned from this journey as follows.
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction
Knowledge tracing and dropout prediction are crucial for online education to estimate students' knowledge states or to prevent dropout rates.
Bias-Aware Inference in Regularized Regression Models
We consider inference on a scalar regression coefficient under a constraint on the magnitude of the control coefficients.
