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.
This paper applies AMP theory to improve learning tasks.
Model reduction of Markov processes is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a discrete-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study…
The study analyzes a model for aggregate losses with dependent and overdispersed inter-losses times.
This paper optimizes MDP policies for efficient state aggregation.
Algorithm for online decision making with unknown dynamics and aggregate feedback.
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our propose…
New framework handles dynamic contexts in reinforcement learning.
We propose dynamical systems trees (DSTs) as a flexible class of models for describing multiple processes that interact via a hierarchy of aggregating parent chains. DSTs extend Kalman filters, hidden Markov models and nonlinear dynamical systems to an interactive group scenario. Various individual processes interact a…
We study minority games in efficient regime. By incorporating the utility function and aggregating agents with similar strategies we develop an effective mesoscale notion of state of the game. Using this approach, the game can be represented as a Markov process with substantially reduced number of states with explicitl…
New algorithm for aggregate inference in HMMs with continuous observations.
State aggregation is a popular model reduction method rooted in optimal control. It reduces the complexity of engineering systems by mapping the system's states into a small number of meta-states. The choice of aggregation map often depends on the data analysts' knowledge and is largely ad hoc. In this paper, we propos…
Study online learning in MDPs with aggregate bandit feedback, achieving low regret in both stochastic and adversarial settings.
New method forecasts time series with changing variances.
Most real-world problems have huge state and/or action spaces. Therefore, a naive application of existing tabular solution methods is not tractable on such problems. Nonetheless, these solution methods are quite useful if an agent has access to a relatively small state-action space homomorphism of the true environment …
New algorithm for collective Gaussian hidden Markov models inference.
This paper presents a way of solving Markov Decision Processes that combines state abstraction and temporal abstraction. Specifically, we combine state aggregation with the options framework and demonstrate that they work well together and indeed it is only after one combines the two that the full benefit of each is re…
We establish that an optimistic variant of Q-learning applied to a fixed-horizon episodic Markov decision process with an aggregated state representation incurs regret , where is the horizon, is the number of aggregate states, is the number of episodes, and is …
We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes, and the transition function is not known to the learner. We show regret bound, where is the number of episodes, is the state space, $A…
We address the problem of estimating the parameters of a time-homogeneous Markov chain given only noisy, aggregate data. This arises when a population of individuals behave independently according to a Markov chain, but individual sample paths cannot be observed due to limitations of the observation process or the need…
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate …
Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We introduce a Markov-switching modeling approach for price change, where the latent Markov proc…
We introduce a simple approach for testing the reliability of homogeneous generators and the Markov property of the stochastic processes underlying empirical time series of credit ratings. We analyze open access data provided by Moody's and show that the validity of these assumptions - existence of a homogeneous genera…
Agents collaboratively learn optimal policies in MDPs with limited capabilities.
This work extends HiP-MDPs to robust state abstractions for multi-task and meta-reinforcement learning.
This paper tackles hidden state inference for HMMs using particle filtering.
This paper develops a low-nonnegative-rank approximation method to identify the state aggregation structure of a finite-state Markov chain under an assumption that the state space can be mapped into a handful of meta-states. The number of meta-states is characterized by the nonnegative rank of the Markov transition mat…
A new algorithm estimates aggregate marginals from noisy data in an online manner.
Paper introduces a new model for cyber insurance pricing.
Develops a method to model multivariate count processes with Cox processes and shot noise intensities.
Method learns CTMC models from steady-state data, predicting unseen states.
The paper proposes a time-dependent Markov model for a limit order book.
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregatio…
Study optimal policy regret in partially observable Markov games with adaptive opponents.
This work tackles large action spaces in RL by binarizing actions.
New neural processes use stacked Markov operators to improve flexibility.
New algorithm for recommending best arms with aggregated feedback.
Proposes proactive bed requests to reduce ED boarding and patient wait times.
The method approximates stationary distributions of Markov models by truncating irrelevant states.
Copula models for sovereign ratings improved by incorporating climate risk.
Revises GNN neighborhood aggregation for more accurate node classification.
Robust algorithm for distributed optimization resistant to Byzantine failures.
Model infers functions for attributes using multi-aggregate datasets with knowledge transfer.
We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rules. The polynomial property allows us to study such rules using …
Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggregation algorithms, one basic question is how to efficiently collect a large amount of high-quality pairwise comparisons for the ranking pur…
Numerous kinds of uncertainties may affect an economy, e.g. economic, political, and environmental ones. We model the aggregate impact by the uncertainties on an economy and its associated financial market by randomised mixtures of Lévy processes. We assume that market participants observe the randomised mixtures only …
A new method for faster prediction in distributed Gaussian processes.
New insights on offline RL with state aggregation and trajectory data.
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction. Existing representation learning methods …