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Found 10 papers, 1 papers with code
Uncertainty in Language Models: Assessment through Rank-Calibration
Language Models (LMs) have shown promising performance in natural language generation.
SymmPI: Predictive Inference for Data with Group Symmetries
Methods for predictive inference have been developed under a variety of assumptions, often -- for instance, in standard conformal prediction -- relying on the invariance of the distribution of the data under special groups of transformations such as permutation groups.
Spectral Ranking Inferences based on General Multiway Comparisons
This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a general and more realistic setup.
Uncertainty Quantification of MLE for Entity Ranking with Covariates
This paper concerns with statistical estimation and inference for the ranking problems based on pairwise comparisons with additional covariate information such as the attributes of the compared items.
Ranking Inferences Based on the Top Choice of Multiway Comparisons
The estimated distribution is then used to construct simultaneous confidence intervals for the differences in the preference scores and the ranks of individual items.
Robust High-dimensional Tuning Free Multiple Testing
A stylized feature of high-dimensional data is that many variables have heavy tails, and robust statistical inference is critical for valid large-scale statistical inference.
Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments
We study offline reinforcement learning under a novel model called strategic MDP, which characterizes the strategic interactions between a principal and a sequence of myopic agents with private types.
Are Latent Factor Regression and Sparse Regression Adequate?
To fill in such an important gap, we also leverage our model as the alternative model to test the sufficiency of the latent factor regression and the sparse linear regression models.
Policy Optimization Using Semi-parametric Models for Dynamic Pricing
$F(\cdot)$ with $m$-th order derivative ($m\geq 2$), our policy achieves a regret upper bound of $\tilde{O}_{d}(T^{\frac{2m+1}{4m-1}})$, where $T$ is time horizon and $\tilde{O}_{d}$ is the order that hides logarithmic terms and the dimensionality of feature $d$.
Understanding Implicit Regularization in Over-Parameterized Single Index Model
For both the vector and matrix settings, we construct an over-parameterized least-squares loss function by employing the score function transform and a robust truncation step designed specifically for heavy-tailed data.
