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Creators/Authors contains: "Shivam Gupta, Jasper Lee"

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  1. (, Thirty-seventh Conference on Neural Information Processing Systems)
    Location estimation is one of the most basic questions in parametric statistics. Suppose we have a known distribution density f , and we get n i.i.d. samples from f (x − μ) for some unknown shift μ. The task is to estimate μ to high accuracy with high probability. The maximum likelihood estimator (MLE) is known to be asymptotically optimal as n → ∞, but what is possible for finite n? In this paper, we give two location estimators that are optimal under different criteria: 1) an estimator that has minimax-optimal estimation error subject to succeeding with probability 1 − ¶ and 2) a confidence interval estimator which, subject to its output interval containing μ with probability at least 1 − ¶, has the minimum expected squared interval width among all shift-invariant estimators. The latter construction can be generalized to minimizing the expectation of any loss function on the interval width. 
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    Accepted Manuscript Full Text Available
  2. (, Advances in Neural Information Processing Systems 35 (NeurIPS 2022))
    Accepted Manuscript Full Text Available