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.
A simpler edge-based discretization method without dual volumes.
Study optimal consumption with relaxed benchmarks and drawdown constraints.
Study describes periodic controls in step 2 sub-Finsler problems on Carnot groups.
Paper approximates free boundary for optimal investment stopping problems.
We propose a numerical recipe for risk evaluation defined by a backward stochastic differential equation. Using dual representation of the risk measure, we convert the risk valuation to a stochastic control problem where the control is a certain Radon-Nikodym derivative process. By exploring the maximum principle, we s…
We consider a class of discrete time stochastic control problems motivated by some financial applications. We use a pathwise stochastic control approach to provide a dual formulation of the problem. This enables us to develop a numerical technique for obtaining an estimate of the value function which improves on purely…
Paper offers a dual formulation for consumption problem with multiplicative habit.
Control of non-episodic, finite-horizon dynamical systems with uncertain dynamics poses a tough and elementary case of the exploration-exploitation trade-off. Bayesian reinforcement learning, reasoning about the effect of actions and future observations, offers a principled solution, but is intractable. We review, then…
We develop a general theory of convex duality for certain singular control problems, taking the abstract results by Kramkov and Schachermayer (1999) for optimal expected utility from nonnegative random variables to the level of optimal expected utility from increasing, adapted controls. The main contributions are the f…
In this paper we prove that there exists a smooth classical solution to the HJB equation for a large class of constrained problems with utility functions that are not necessarily differentiable or strictly concave. The value function is smooth if admissible controls satisfy an integrability condition or if it is contin…
Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the …
The aim of this paper is to study the fast computation of the lower and upper bounds on the value function for utility maximization under the Heston stochastic volatility model with general utility functions. It is well known there is a closed form solution of the HJB equation for power utility due to its homothetic pr…
We develop a technique based on Malliavin-Bismut calculus ideas, for asymptotic expansion of dual control problems arising in connection with exponential indifference valuation of claims, and with minimisation of relative entropy, in incomplete markets. The problems involve optimisation of a functional of Brownian path…
The paper solves a control problem using reflections to track a benchmark process.
We study the stochastic control problem of maximizing expected utility from terminal wealth under a non-bankruptcy constraint. The wealth process is subject to shocks produced by a general marked point process. The problem of the agent is to derive the optimal insurance strategy which allows "lowering" the level of the…
This paper improves robot grasping by integrating meta-control and latent-space imagination.
The paper introduces a method to create more reliable models for robot dynamics.
New duality concept for vector spaces and groupoids.
In this paper, we study a constrained utility maximization problem following the convex duality approach. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of FBSDEs plus additional conditions. Such formulation then allows…
We study an optimal control problem related to swing option pricing in a general non-Markovian setting in continuous time. As a main result we show that the value process solves a first-order non-linear backward stochastic partial differential equation. Based on this result we can characterize the set of optimal contro…
A new framework for generative modeling using value-driven transport.
We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic optimal control problem …
The dual risk model is a popular model in finance and insurance, which is often used to model the wealth process of a venture capital or high tech company. Optimal dividends have been extensively studied in the literature for a dual risk model. It is well known that the value function of this optimal control problem do…
Unified market making controls risk, arbitrage, and volatility surfaces.
Geometric problems are usually formulated by means of (exterior) differential systems. In this theory, one enriches the system by adding algebraic and differential constraints, and then looks for regular solutions. Here we adopt a dual approach, which consists to enrich a plane field, as this is often practised in cont…
Adaptive framework predicts stock prices better during volatile periods.
Two deep learning algorithms solve utility maximisation problems in finance.
Paper tackles utility maximization with job-switching and retirement constraints.
Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow. To accelerate solvers, state-of-the-art approaches consist in reducing the size of the optimization problem at hand. In the context of regression, this can be achiev…
New method for hedging path-dependent options with price impact using probabilistic arguments.
Algorithm learns goals without rewards, controls environments.
Robust -learning for mean-field control under Wasserstein uncertainty
Study of motion control systems on Lie groups with specific geometric constraints.
Optimizes control of hybrid systems with multiple switching processes.
Optimizes wireless power control using graph neural networks and counterfactual optimization.
Paper tackles offline CMDP problems with near-optimal algorithm and sample complexity bound.
Study optimal portfolio strategies with periodic evaluation under short-selling prohibition.
Study efficient convergence of RL algorithm with function approximation.
Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e.g., ExIt from [2], AlphaGo-Zero from [27]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (e.g., a deep neural network) deployed at t…
We geometrize six-dimensional pure Supergravity by means of an exact Courant algebroid, whose Severa class is defined through the Supergravity three-form , equipped with a generalized metric and a compatible, torsion-free, generalized connection. The Supergravity equations of motion follow from the v…
The problem of robust hedging requires to solve the problem of superhedging under a nondominated family of singular measures. Recent progress was achieved by [9,11]. We show that the dual formulation of this problem is valid in a context suitable for martingale optimal transportation or, more generally, for optimal tra…
Paper tackles robust classification and feature selection with a novel primal-dual method.
Trading strategy mimics optimal control with simple heuristic.
Develops a regression approach for solving MDPs with general state and action spaces.
Mixed RL improves RL efficiency with dual representations.
Graph neural networks optimize radio resource management policies for wireless networks.
Unified framework for complex, split-complex, and dual numbers.
The paper explores how AI systems use information geometry to encode semantic structure.