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.
Develops hierarchical reinforcement learning value function approximators.
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for …
Hierarchical geodesic model for analyzing shapes on manifolds.
Previous work in hierarchical reinforcement learning has faced a dilemma: either ignore the values of different possible exit states from a subroutine, thereby risking suboptimal behavior, or represent those values explicitly thereby incurring a possibly large representation cost because exit values refer to nonlocal a…
Develops a hierarchical model for analyzing longitudinal data on manifolds.
Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is n…
The study of hierarchy in networks of the human brain has been of significant interest among the researchers as numerous studies have pointed out towards a functional hierarchical organization of the human brain. This paper provides a novel method for the extraction of hierarchical connectivity components in the human …
Eikonal-Constrained QRL improves goal-reaching in reinforcement learning.
UNTIE learns representations of coupled categorical data.
Paper relaxes set-valued prediction in hierarchical classification by considering representation complexity.
Randomized hierarchical clustering tests for stability and detects clusters.
With globalization, countries are more connected than before by trading flows, which currently amount to at least 36 trillion dollars. Interestingly, approximately 30-60 percent of global exports consist of intermediate products. Therefore, the trade flow network of a particular product with high added values can be re…
Machine learning often needs to model density from a multidimensional data sample, including correlations between coordinates. Additionally, we often have missing data case: that data points can miss values for some of coordinates. This article adapts rapid parametric density estimation approach for this purpose: model…
Proposes a nonparametric tensor factorization for sparse data.
This work introduces a method for visualizing high-dimensional posteriors using hierarchical tree-valued predictions.
The paper develops a decision support system for hierarchical text classification of conference proceedings.
We present a numerical method for the frequent pricing of financial derivatives that depends on a large number of variables. The method is based on the construction of a polynomial basis to interpolate the value function of the problem by means of a hierarchical orthogonalization process that allows to reduce the numbe…
New method optimizes hierarchical multi-label classification results.
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whos…
Bayesian model estimates feature values of premium products.
We use splines and the Sasaki metric to analyze and compare manifold-valued trajectories.
Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different …
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
TreeHFD algorithm explains tree ensemble models through hierarchical orthogonality.
Unified framework for modeling hierarchical spaces in design problems.
Bayesian analysis of financial time series using R-INLA.
HPPCA improves imputation of longitudinal data with missing values.
N-discount optimality was introduced as a hierarchical form of policy- and value-function optimality, with Blackwell optimality lying at the top level of the hierarchy Veinott (1969); Blackwell (1962). We formalize notions of myopic discount factors, value functions and policies in terms of Blackwell optimality in MDPs…
Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse. We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion. Our approach combines universal value functio…
Many real-world optimization problems require significant resources for objective function evaluations. This is a challenge to evolutionary algorithms, as it limits the number of available evaluations. One solution are surrogate models, which replace the expensive objective. A particular issue in this context are hiera…
The stability of money value is an important requisite for a functioning economy, yet it critically depends on the actions of participants in the market themselves. Here we model the value of money as a dynamical variable that results from trading between agents. The basic trading scenario can be recast into an Ising t…
We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary images. The building blocks are binary features defined hierarchically as a composition of some of the features in the layer immediately be…
BOSH optimizes functions with stochastic evaluations more efficiently and precisely.
Locally adaptive clustering for tree delineation.
This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry of the input data. We show that this output structure is equivalent to a finite quasi-ultrametric space and study admissibility with respect…
Proposes a hierarchical curriculum loss to improve model accuracy and interpretability.
This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods that, based on the dissimilarity structure, output hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter. Our construction of hierarchical cl…
Proves deep networks can learn hierarchical structures efficiently.
Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in com…
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small sub…
Deep networks learn hierarchical functions more efficiently than shallow ones.
The paper reviews and extends calibration concepts for classification and regression.
Proposes a revenue function to evaluate dendrograms from comparisons.
HierarchicalForecast provides a Python framework for coherent hierarchical forecasting.
Two new estimators improve VAE training for hierarchical and prior parameters.
New method approximates high-dimensional probability densities efficiently.
VFG model embeds flow-based models with hierarchical structures using variational inference.
Three-layer neural networks learn hierarchical polynomial functions efficiently.