3 citations · 3 across the 9 of their papers we have counts for
9 papers
Empirical Bayes Estimation of the Mean of a Function of the Latent Variable with Applications to the Treatment of Nonresponse
Eitan Greenshtein, Ya'acov Ritov
We consider the estimation of linear functionals of the mixing distribution in a nonparametric empirical Bayes framework. Our main interest is in situations in which the mixing dis…
The Root Finding Problem Revisited: Beyond the Robbins-Monro procedure
Yue Yu, Moulinath Banerjee, Ya'acov Ritov
We introduce Sequential Probability Ratio Bisection (SPRB), a novel stochastic approximation algorithm that adapts to the local behavior of the (regression) function of interest ar…
Limitations of refinement methods for weak to strong generalization
Seamus Somerstep, Ya'acov Ritov, Mikhail Yurochkin +2
Standard techniques for aligning large language models (LLMs) utilize human-produced data, which could limit the capability of any aligned LLM to human level. Label refinement and…
Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model
Kevin Christian Wibisono, Debarghya Mukherjee, Moulinath Banerjee +1
In many practical situations, randomly assigning treatments to subjects is uncommon due to feasibility constraints. For example, economic aid programs and merit-based scholarships…
A mixture of a normal distribution with random mean and variance -- Examples of inconsistency of maximum likelihood estimates
Ya'acov Ritov
We consider the estimation of the mixing distribution of a normal distribution where both the shift and scale are unobserved random variables. We argue that in general, the model i…
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
Seamus Somerstep, Ya'acov Ritov, Yuekai Sun
In many prediction problems, the predictive model affects the distribution of the prediction target. This phenomenon is known as performativity and is often caused by the behavior…
A transfer learning framework for weak-to-strong generalization
Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee +3
Modern large language model (LLM) alignment techniques rely on human feedback, but it is unclear whether these techniques fundamentally limit the capabilities of aligned LLMs. In p…
No need for an oracle: the nonparametric maximum likelihood decision in the compound decision problem is minimax
Ya'acov Ritov
We discuss the asymptotics of the nonparametric maximum likelihood estimator (NPMLE) in the normal mixture model. We then prove the convergence rate of the NPMLE decision in the em…
Asymptotic normality of a linear threshold estimator in fixed dimension with near-optimal rate
Debarghya Mukherjee, Moulinath Banerjee, Debasri Mukherjee +1
Linear thresholding models postulate that the conditional distribution of a response variable in terms of covariates differs on the two sides of a (typically unknown) hyperplane in…