arXiv research

Paper improves neural ODEs for forecasting non-Markovian processes.

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

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

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

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

Analyzes non-Markovian environments in stochastic approximation.

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

Efficient RL in PRMs with improved regret bound.

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

Deep learning solves non-Markovian FBSDEs for utility maximization.

New method for pricing European options in rough LSV models.

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

Partially observable environments present an important open challenge in the domain of sequential control learning with delayed rewards. Despite numerous attempts during the two last decades, the majority of reinforcement learning algorithms and associated approximate models, applied to this context, still assume Marko…

ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.

A new model predicts price concavity and reversion after metaorder execution.

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.

A new metric detects non-Markovian states in partially observable environments.

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.

This paper studies the equilibrium pricing of asset shares in the presence of dynamic private information. The market consists of a risk-neutral informed agent who observes the firm value, noise traders, and competitive market makers who set share prices using the total order flow as a noisy signal of the insider's inf…

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 tackles non-Markovian control problems with new learning methods.

This paper investigates multi-task reinforcement learning in non-Markovian decision making, showing benefits in sample efficiency.

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…

Introduces alternators for modeling sequences, outperforming baselines.

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…

Extends pricing methods for index options under rough volatility.

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

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

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

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

It is shown how the generating functional method of De Dominicis can be used to solve the dynamics of the original version of the minority game (MG), in which agents observe real as opposed to fake market histories. Here one again finds exact closed equations for correlation and response functions, but now these are de…

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network th…

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…

New method optimizes decision-making in uncertain environments.

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.

We establish a foundation for multivariate counterfactual identification using dynamic optimal transport.

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.

We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes. The multiresolution GP hierarchically couples a collection of smooth GPs, each defined over an element of a random nested partition. Long-range dependencies are captured by the top-level GP…

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

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…

Next-gen reservoir computers fail to predict complex processes, highlighting need for better architectures.