9 citations · 29 across the 18 of their papers we have counts for
7 papers · 1 filter
Confidence Horizons
Chase Mathis, Ian Waudby-Smith
Anytime-valid inference enables analysts to continuously monitor their data and stop experiments early. However, the majority of these methods incur a certain conservativeness by r…
Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
Keegan Harris, Brian W. Lee, Ian Waudby-Smith +3
Reinforcement learning (RL) fine-tuning is widely used in language model training to improve performance on a target task while limiting drift from a reference policy. A standard w…
Gaffke's confidence interval for the mean of bounded data is inadmissible but asymptotically efficient
Jiahao Ming, Aaditya Ramdas, Yi Shen +2
Given observations , Gaffke (2005) defined \[ K_n(\mathbf x)=\mathbb{P}_{\mathbf D}\!\left\{\sum_{i=1}^n x_iD_i\le 1\right\}, \qquad (D_0,D_1,\ldots,D_n)…
Post-Hoc Large-Sample Statistical Inference
Ben Chugg, Etienne Gauthier, Michael I. Jordan +2
We derive inferential procedures for large sample sizes that remain valid under data-dependent significance levels (so-called "post-hoc valid inference"). Classical statistical too…
Multi-Armed Sequential Hypothesis Testing by Betting
Ricardo J. Sandoval, Ian Waudby-Smith, Michael I. Jordan
We consider a variant of sequential testing by betting where, at each time step, the statistician is presented with multiple data sources (arms) and obtains data by choosing one of…
Combining e-values using demi-supermartingales
Jiahao Ming, Aaditya Ramdas, Yi Shen +2
We present a new method for combining e-variables through demi-supermartingales, which settles an old conjecture in the literature on nonparametric mean testing. It also provides a…
On Nonasymptotic Confidence Intervals for Treatment Effects in Randomized Experiments
Ricardo J. Sandoval, Sivaraman Balakrishnan, Avi Feller +2
We study nonasymptotic (finite-sample) confidence intervals for treatment effects in randomized experiments. In the existing literature, the effective sample sizes of nonasymptotic…