Statistical Learning
Adaptive Linear Estimating Equations
Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes. Despite its advantages, such data collection mechanism often introduces complexities to the statistical inference procedure.
Promises and Pitfalls of Threshold-based Auto-labeling
TBAL is emerging as a widely-used solution in practice.
Evaluating the Moral Beliefs Encoded in LLMs
This paper presents a case study on the design, administration, post-processing, and evaluation of surveys on large language models (LLMs). It comprises two components: (1) A statistical method for eliciting beliefs encoded in LLMs.
Estimating Propensity for Causality-based Recommendation without Exposure Data
They aim to recommend an item based on the uplift, also called the causal effect, in the user's behavior (e.g., clicks or purchases) caused by different treatments (i.e., recommending/exposing the item or not) [
Analyzing and Visualizing the Results of the Reconstruction Optimization
This observation is an interesting research direction for future works.
A Unified Framework for Rank-based Loss Minimization
The empirical risk function is the cornerstone of machine learning.
The geometry of hidden representations of large transformer models
By analyzing the intrinsic dimension (ID) and neighbor composition, we find that the representations evolve similarly in transformers trained on protein language tasks and image reconstruction tasks.
Unified lower bounds for interactive high-dimensional estimation under information constraints
We consider the problem of parameter estimation under local information constraints, where the estimation algorithm has access to only limited information about each sample.