arXiv research

Optimal estimator derived for partially observable LTI systems.

PAC-Bayesian bounds for stochastic LTI systems derived.

HiPPO-Prophecy models can learn dynamical systems without fine-tuning.

Novel method for shape optimization of non-smooth PDEs.

The paper introduces a new method to detect rough volatility and market states using fractional derivatives.

Derives RL framework for systems without velocity or acceleration measurements.

Using stochastic gradient search and the optimal filter derivative, it is possible to perform recursive (i.e., online) maximum likelihood estimation in a non-linear state-space model. As the optimal filter and its derivative are analytically intractable for such a model, they need to be approximated numerically. In [Po…

Stochastic model prices weather derivatives for Indian states, highlighting temperature volatility impacts.

New formulas derived for lattice crossing coefficients, improving computation efficiency.

In this paper we investigate a link between state- space models and Gaussian Processes (GP) for time series modeling and forecasting. In particular, several widely used state- space models are transformed into continuous time form and corresponding Gaussian Process kernels are derived. Experimen- tal results demonstrat…

Study models interest rates as CTMC, pricing and replicating derivatives.

The paper analyzes multivariate payments in multi-state life insurance using Markovian state processes.

Paper solves tracking control for $(x,u)$-flat systems using classical states.

This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information. In MNNs, information is propagated between neurons throughout a state transition function. State and error gradients are then directly computed from state updat…

We define invariants for a framed link equipped with a SL2 local system in its complement and additional combinatorial data based on the theory of representations of stated skein algebras at roots of unity of punctured bigons and the geometric interpretation of their centers. The gauge invariance of the link invariant …

Paper derives explicit formulas for AJ-bracket of tied links.

A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can be solved for analytically, yielding a novel parametric density function involving a ratio of Riema…

Paper derives an error bound for stochastic LTI systems.

The p-adic theory of the stock market is presented. It is shown that the price dynamics is very naturally described by the adelic function. The procedure of derivation of the functional integral formulation of adelic type is derived from microscopic models using generalized supercoherent states.

We derive the general state sum construction for 2D topological quantum field theories (TQFTs) with source defects on oriented curves, extending the state-sum construction from special symmetric Frobenius algebra for 2-D TQFTs without defects (cf. Lauda \& Pfeiffer \cite{LP}). From the extended Pachner moves (Crane \& …

Researchers derive the chemical potential equation for ideal agent systems.

Quantum Signal Processing reduces derivative pricing quantum resource requirements.

Let G be a discrete group and C be an additive spherical G-fusion category. We prove that the state sum 3-dimensional HQFT derived from C is isomorphic to the surgery 3-dimensional HQFT derived from the G-center of C.

New RL method reduces sample complexity for large state-action spaces.

We combine general equilibrium theory and theorie generale of stochastic processes to derive structural results about equilibrium state prices.

Deep learning detects sleep state fluctuations in neonates from single EEG channel.

We tackle the problem of acting in an unknown finite and discrete Markov Decision Process (MDP) for which the expected shortest path from any state to any other state is bounded by a finite number $D$. An MDP consists of $S$ states and $A$ possible actions per state. Upon choosing an action $a_t$ at state $s_t$, one re…

Let C be a spherical fusion category. We prove that the Turaev-Viro-Barrett-Westbury state sum invariant of 3-manifolds derived from C is equal to the Reshetikhin-Turaev surgery invariant of 3-manifolds derived from Z(C), where Z(C) is the Drinfeld-Joyal-Street center of C.

A new method for state estimation in state-space models using incomplete data.

Reinforcement learning (RL) in Markov decision processes (MDPs) with large state spaces is a challenging problem. The performance of standard RL algorithms degrades drastically with the dimensionality of state space. However, in practice, these large MDPs typically incorporate a latent or hidden low-dimensional structu…

Paper introduces multitask neural networks for efficient stochastic control problems.

The virtual Betti number conjecture states that any hyperbolic three-manifold has a finite cover with positive first Betti number. We show that this would follow if it were known that the derived series of the fundamental group $G$ of a hyperbolic three-manifold satisfies a certain stability property. The stability pro…

Develops a new reinforcement learning framework for complex control problems.

We derive, for the square operator of Yau, an analogue of the Omori-Yau maximum principle for the Laplacian. We then apply it to obtain nonexistence results concerning complete spacelike hypersurfaces with constant higher order mean curvature in the Steady State space.

Method estimates parameters of complex nonlinear systems.

Deep Bellman Hedging uses reinforcement learning to optimize financial portfolio hedging.

TERA method speeds up derivative Gaussian processes in high dimensions.

The paper explores geometric calculations on probability manifolds derived from master equations.

We study in this paper a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered…

In this paper we consider a reduced-form intensity-based credit risk model with a hidden Markov state process. A filtering method is proposed for extracting the underlying state given the observation processes. The method may be applied to a wide range of problems. Based on this model, we derive the joint distribution …

Imitation learning targets deriving a mapping from states to actions, a.k.a. policy, from expert demonstrations. Existing methods for imitation learning typically require any actions in the demonstrations to be fully available, which is hard to ensure in real applications. Though algorithms for learning with unobservab…

Paper derives constraints for Bayesian Knowledge Tracing parameters.

New model for insurance states using Markov jump processes with non-countable state space.

This paper develops a method to derive optimal portfolios and risk premia explicitly in a general diffusion model for an investor with power utility and a long horizon. The market has several risky assets and is potentially incomplete. Investment opportunities are driven by, and partially correlated with, state variabl…

Paper derives analytical formulas for NLD-CEV moments with regime switching.

New risk measures incorporate economic states to assess crude oil derivatives.

We consider a Hidden Markov Model (HMM) where the integrated continuous-time Markov chain can be observed at discrete time points perturbed by a Brownian motion. The aim is to derive a filter for the underlying continuous-time Markov chain. The recursion formula for the discrete-time filter is easy to derive, however i…

Researchers derived Kauffman bracket polynomial for Celtic link shadows using two methods.