A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimal reinsurance and investment strategies are derived under mean-variance criteria with partial information.
problem Optimal reinsurance and investment strategies for an insurance firm under mean-variance criteria with partially observable market dynamics.
method Formulated as a stochastic LQ control problem, solved using separation principle and stochastic filtering theory for partial information, and viscosity solution for full information.
result Efficient strategies and efficient frontier presented in closed forms via solutions to extended stochastic Riccati equations.
New algorithms ensure privacy in online learning with optimal regret bounds.
problem Privacy in online learning with optimal regret bounds.
method Differentially private algorithms for online linear optimization in full information and bandit settings.
result Optimal regret bounds of $O(\sqrt{T})+ ilde{O}\left(\frac{1}{\epsilon}
ight)$ in full information and $ ilde{O}\left(\frac{1}{\epsilon}\sqrt{T}
ight)$ in bandit settings.
This work improves knowledge distillation by transferring full kernel matrices efficiently.
problem Efficiently transferring full pairwise similarity matrices for model compression in deep learning.
method The authors propose a method to transfer the full similarity matrix effectively using the Nyström method, decomposing it into partial matrices.
result The difference between the full kernel matrices of teacher and student can be well bounded by partial matrices, improving optimization efficiency.
In this note, we present a version of the Thompson sampling algorithm for the problem of online linear generalization with full information (i.e., the experts setting), studied by Kalai and Vempala, 2005. The algorithm uses a Gaussian prior and time-varying Gaussian likelihoods, and we show that it essentially reduces …
We study the power of different types of adaptive (nonoblivious) adversaries in the setting of prediction with expert advice, under both full-information and bandit feedback. We measure the player's performance using a new notion of regret, also known as policy regret, which better captures the adversary's adaptiveness…
In this paper, we consider a financial market with assets exposed to some risks inducing jumps in the asset prices, and which can still be traded after default times. We use a default-intensity modeling approach, and address in this incomplete market context the problem of maximization of expected utility from terminal…
We consider the mean-variance hedging problem under partial information in the case where the flow of observable events does not contain the full information on the underlying asset price process. We introduce a martingale equation of a new type and characterize the optimal strategy in terms of the solution of this equ…
We address online linear optimization problems when the possible actions of the decision maker are represented by binary vectors. The regret of the decision maker is the difference between her realized loss and the best loss she would have achieved by picking, in hindsight, the best possible action. Our goal is to unde…
Given a geodesic space (E, d), we show that full ordinal knowledge on the metric d-i.e. knowledge of the function D d : (w, x, y, z) → 1 d(w,x)≤d(y,z) , determines uniquely-up to a constant factor-the metric d. For a subspace En of n points of E, converging in Hausdorff distance to E, we construct a met…
This paper concerns the recursive utility maximization problem under partial information. We first transform our problem under partial information into the one under full information. When the generator of the recursive utility is concave, we adopt the variational formulation of the recursive utility which leads to a s…
Study pairs trading strategy with uncertain drift and penalized risk.
problem Optimizing pairs trading strategy with uncertain drift and risk penalty.
method Model pairs trading as a Gaussian mean-reverting process with a Markov chain, use stochastic filtering theory, and solve for logarithmic utility function.
result Characterize optimal strategies and value functions under full and partial information, showing certainty equivalence principle.