arXiv research

This work expands state-action aggregation methods for non-Markovian environments.

Analyzes non-Markovian environments in stochastic approximation.

Non-Markovian point process shows power-law scaling, similar to nonlinear Markovian process.

Paper introduces PRMs to learn non-Markovian stochastic rewards for reinforcement learning.

Deep learning solves non-Markovian FBSDEs for utility maximization.

Paper tackles robust offline RL for non-Markovian processes, improving efficiency and applicability.

Unified analytical tool for non-Markovian jump processes.

Investigates optimal consumption and investment strategies in non-Markovian markets with unbounded parameters.

Two signature-based methods solve optimal stopping in non-Markovian frameworks.

The paper develops a deep signature approach for option pricing under non-Markovian stochastic volatility models.

Study optimal portfolios in a non-Markovian regime-switching model with random time horizon.

Study develops numerical schemes for non-Markovian volatility models with memory.

In this paper, we present a discrete-type approximation scheme to solve continuous-time optimal stopping problems based on fully non-Markovian continuous processes adapted to the Brownian motion filtration. The approximations satisfy suitable variational inequalities which allow us to construct $ε$-optimal stopping tim…

Study analyzes non-Markovian effects in financial markets over multiple years.

Paper improves neural ODEs for forecasting non-Markovian processes.

Paper tackles non-Markovian control problems with new learning methods.

HONEM learns embeddings for higher-order networks, improving performance in various tasks.

We establish existence, uniqueness and regularity of solution results for a class of backward stochastic partial differential equations with singular terminal condition. The equation describes the value function of non-Markovian stochastic optimal control problem in which the terminal state of the controlled process is…

DeepSynth synthesizes automata to guide deep RL agents through sparse, non-Markovian rewards.

Characterizes a general range decreasing group homomorphism.

This paper studies a class of non$-$Markovian singular stochastic control problems, for which we provide a novel probabilistic representation. The solution of such control problem is proved to identify with the solution of a $Z-$constrained BSDE, with dynamics associated to a non singular underlying forward process. Du…

Paper introduces MVS to detect non-Markovian observations in reinforcement learning.

Path signatures improve hedging of exotic derivatives in non-Markovian models.

Extends pricing methods for index options under rough volatility.

The paper develops methods to price options under rough volatility models using BSPDEs.

A new method predicts non-Markovian closure terms for complex systems.

New algorithm prices Bermudan options using Wiener chaos expansion for non-Markovian processes.

HS-FNO models non-Markovian PDEs by learning history and future states.

New model controls memory in seq2seq tasks, revealing learning regimes.

Efficient RL in PRMs with improved regret bound.

Develops RL algorithm for non-Markovian, non-stationary reward streams.

Describes state variables in sequential decision problems, linking them to Markovian and non-Markovian models.

We extend certain homomorphisms defined on the higher Torelli subgroups of the mapping class group to crossed homomorphisms defined on the entire mapping class group. In particular, for every $k\geq 2$, we construct a crossed homomorphism $ε_k$ which extends Morita's homomorphism $\tilde τ_k$ to the entire mapping clas…

By appealing to renewal theory we determine the equations that the mean exit time of a continuous-time random walk with drift satisfies both when the present coincides with a jump instant or when it does not. Particular attention is paid to the corrections ensuing from the non-Markovian nature of the process. We show t…

We study stochastic differential equations (SDEs) whose drift and diffusion coefficients are path-dependent and controlled. We construct a value process on the canonical path space, considered simultaneously under a family of singular measures, rather than the usual family of processes indexed by the controls. This val…

Paper solves Merton's portfolio problem in a non-Markovian, non-semimartingale model.

Two crossing homomorphisms on braid groups are shown to be equivalent.

The study classifies homomorphisms from mapping class groups using finite subgroups.

Study of Chebyshev-Frobenius homomorphism in 3-manifold skein modules.

Study on kinetic Langevin diffusions and their couplings, showing subtle TV bounds and new non-Markovian couplings.

New approach tackles non-Markovian behavior in maternal health programs.

Graph homomorphism numbers embed graphs for classification.

New conditions for weighted composition operators in group homomorphisms.

Two efficient ML techniques compute American option prices in high-dimensional models, including non-Markovian ones.

FLDD improves discrete diffusion models by learning a non-Markovian noising process.

Study homomorphisms from groups to 3-manifold fundamental groups.

In this paper, we present a Longstaff-Schwartz-type algorithm for optimal stopping time problems based on the Brownian motion filtration. The algorithm is based on Leão, Ohashi and Russo and, in contrast to previous works, our methodology applies to optimal stopping problems for fully non-Markovian and non-semimartinga…

New homomorphisms from knot Floer homology help classify knots.