Contributions to the Theory of Statistical Estimation and Testing Hypotheses

By A. Wald. 1939

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Citations in the corpus

Privacy Aware Learning (2012, background): “First, the use of loss functions and risk functions provides a compelling formal foundation for deﬁning “learning,” one that dates back to Wald [28] in the 1930’s, and which has seen continued development in the context of research on machine learning over the past two decades.”
Local Privacy and Statistical Minimax Rates (2013, background): “The notion of minimizing a risk functional lies at the heart of decision-theoretic statistics, dating back to the seminal work of Wald [24].”
Local Privacy, Data Processing Inequalities, and Statistical Minimax Rates (2013, background): “ sharper results. 5 Convex risk minimization under local privacy The notion of minimizing a risk functional lies at the heart of decision-theoretic statistics, dating back to the seminal work of Wald [34]. In practice, it is most attractive to minimize convex functions, and thus, convex risk minimiz”
Trial Sequential Analysis in systematic reviews with meta-analysis (2017, background): “tributions to the theory of statistical estimation and testing hypotheses [25].”
Toward Idealized Decision Theory (2015, background): “r quite some time, under the name of decision theory. The investigation of what is now called decision theory stretches back to Pascal and Bernoulli; more recently decision theory has been studied by Wald (1939), Lehmann (1950), Jeﬀrey (1965), Lewis (1981a), Joyce (1999), Pearl (2000) and many other”
Aligning Superintelligence with Human Interests: A Technical Research Agenda (2015, background): “The study goes back to Pascal, and has been picked up in modern times by Lehmann (1950), Wald (1939), Jeﬀrey (1983), Joyce (1999), Lewis (1981), Pearl (2000), and many others.”
Estimation with Quadratic Loss (1992, background): “Wald [18], also in 1939, in what may be considered the first paper on statistical decision theory, did the same for location parameters alone, and tried to show in his theorem 5 that the estimator obtained in this way is admissible, that is, that there is no estimator whose risk is no greater at any”
Minimax Bounds on Stochastic Batched Convex Optimization (2018, methodology): “We evaluate the performance of an algorithm A in a uniform sense (Wald, 1939) by considering its maximum risk for a collection of functions F .”
Towards Deep Learning Models Resistant to Adversarial Attacks (2017, background): “Formulations of this type (and their finite-sample counterparts) have a long history in robust optimization, going back to Wald (Wald, 1939; 1945; 1992).”
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle (2019, background): “To effectively defend the adversarial attacks, [25] proposed adversarial training which can be formulated as a robust optimization [35]: min θ E(x,y)∼D max ‖η‖≤ `(θ;x+ η, y), (1)”
When did Bayesian inference become "Bayesian"? (2006, background): “Wald (175) considered “hypothetical a priori distributions” of the parameter of interest, and in 1947 (176) he derived the “Bayes solution,” but then evaluated its performance with respect to the sample space.”
You Only Propagate Once: Painless Adversarial Training Using Maximal Principle (2019, background): “To defense the adversarial attack, [5] proposed adversarial training which can be formulated as a robust optimization problem [6]:”