It is shown that the alternating COMB control attains the limiting Hamiltonian maximum exactly when the two highest scores coincide and the third- and fourth-highest scores coincide, and that the alternating COMB control attains the limiting Hamiltonian maximum exactly when the two highest scores coincide.
Abstract
We present a solution of the limiting five-expert prediction problem with geometric stopping, based on stochastic calculus and partial differential equations. If $\delta$ is the stopping probability, the minimax expected regret from tied initial scores is $45\pi^2/(512\sqrt{2\delta})+o(\delta^{-1/2})$ as $\delta\downarrow0$. The adversarial rank control selecting the best and third-best experts maximizes the limiting Hamiltonian at every state. Following the four-expert construction of Bayraktar, Ekren, and Zhang (2020), we represent the value correction as a discounted boundary-local-time expectation for a degenerate obliquely reflected Brownian motion. The hyperbolic systems governing its boundary traces are derived and solved explicitly. To verify the nonlinear Hamilton--Jacobi--Bellman equation, we combine equality directions, hyperbolic comparison principles, and averaging across controls. These arguments reduce the 48 regional control inequalities to lower-dimensional boundary problems, with the remaining signs established by explicit monotonicity arguments. We prove global $C^2$ regularity across both regional interfaces and changes in rank ordering. We also show that the alternating COMB control attains the limiting Hamiltonian maximum exactly when the two highest scores coincide and the third- and fourth-highest scores coincide.
It is proved that an unbounded gap between the update maps can coexist with vanishing predictive KL for every fixed finite $K\ge2$ in a stationary symmetric Gaussian HMM, and isolates two missing links between internal update gaps and predictive cost.
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This work derives a finite-dimensional dual formulation of PrO inference that separates sampling fluctuation, approximation under a divergence budget, regularization, and numerical optimization error and uses an exactly solvable categorical example to show that predictive-risk convergence can imply convergence to a uni...
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We give an explicit solution to the five expert prediction with expert advice partial differential equation (PDE) in the finite-time horizon setting. The solution formula establishes that the adversary's rank strategy $(1,0,1,0,0)$ is globally optimal, and the COMB strategy $(1,0,1,0,1)$ is optimal exactly on the set w...
This work develops a general framework that separates score discretization from Brownian-motion simulation and allows multiple geodesic random-walk steps per score evaluation and provides a sharper characterization of the convergence and sampling complexity of Riemannian diffusion models.
A variant of stochastic gradient descent with initial regularization with initial regularization is analyzed and dimension-free upper bounds on its expected excess risk for the squared loss are derived.
Deep-Control BSDE is proposed, a layerwise control-regression method for Markovian backward stochastic differential equations that establishes backward stability through a contraction property of the Brownian projection and derives conditional consistency when discretization, local learning, numerical, and finite-branc...
Mingchang Wang, Xiangjun Wang· 0 citations
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