arXiv research

Study speculative trading using RL with exploratory framework.

A framework for robust exploration in reinforcement learning under ambiguity.

New method for portfolio management learns from past wealth evolution.

Study uses RL to optimize investment with financial constraints, showing exploration benefits.

This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to deve…

Study on utility maximization with Tsallis entropy in reinforcement learning.

Neural model with parameterized algorithms improves graph CO problem solving.

This paper quantifies privacy loss in exploratory data analysis.

Study of entropy-regularized LQG MFGs with exploratory actions.

Study finds key investing characteristics for success in equity markets.

Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaus…

Accounting frameworks follow stipulations of existing Accounting Theories. This exploratory research sets out to trace the evolution of accounting theories of Charge and Discharge Syndrome and the Corollary of Double Entry. Furthermore, it dives into the theories of Income Determination, garnishing it with areas of div…

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data clean…

In this paper, we propose a new algorithm for exploratory projection pursuit. The basis of the algorithm is the insight that previous approaches used fairly narrow definitions of interestingness / non interestingness. We argue that allowing these definitions to depend on the problem / data at hand is a more natural app…

Study uses big data to analyze quantum invariants.

Action chunking and data exploration improve behavior cloning in robotics.

Agent learns directed exploration policies to improve performance in hard games.

We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into $K$ clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, $P$, is chosen by the user and optimally distributed am…

Many real-world problems can be reduced to combinatorial optimization on a graph, where the subset or ordering of vertices that maximize some objective function must be found. With such tasks often NP-hard and analytically intractable, reinforcement learning (RL) has shown promise as a framework with which efficient he…

Breaks the hardness conjecture for batch RL with a novel tournament-based approach.

Factor analysis aims to determine latent factors, or traits, which summarize a given data set. Inter-battery factor analysis extends this notion to multiple views of the data. In this paper we show how a nonlinear, nonparametric version of these models can be recovered through the Gaussian process latent variable model…

New method explains high-dimensional sphere data with latent factors.

Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models an…

Proposes a virtual bidding strategy for electricity markets using stochastic control.

The abstract warns against flawed empirical research in machine learning.

We present a new application and covering number bound for the framework of "Machine Learning with Operational Costs (MLOC)," which is an exploratory form of decision theory. The MLOC framework incorporates knowledge about how a predictive model will be used for a subsequent task, thus combining machine learning with t…

The paper tackles confidence calibration for exploratory machine learning problems.

Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings,…

Interpretable model for Granger causality using neural networks.

Deep learning speeds up IFA estimation for large datasets.

cGAP visualizes high-dimensional categorical data with interpretable geometric structure.

cGAP visualizes high-dimensional categorical data with interpretable geometric structure.

Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test. This is an adaptive process, where the choice of analysis to be performed next dep…

The paper tackles optimal stopping problems using reinforcement learning and singular control.

Archetypal analysis helps understand binary data sets.

ACA identifies and explains anomalies in data.

Extends model-x framework to handle missing data.

Since time immemorial, people have been looking for ways to organize scientific knowledge into some systems to facilitate search and discovery of new ideas. The problem was partially solved in the pre-Internet era using library classifications, but nowadays it is nearly impossible to classify all scientific and popular…

A-DOGE embeds attributed graphs efficiently using density of states.

We introduce an exploratory study on Mutation Validation (MV), a model validation method using mutated training labels for supervised learning. MV mutates training data labels, retrains the model against the mutated data, then uses the metamorphic relation that captures the consequent training performance changes to as…

This describes a statistical technique called "tonsuring" for exploratory data analysis in finance. Instead of rejecting "outlier" data that conflicts with the model, this strips out "inlier" data to get a clearer picture of how the market changes for larger moves.

Study optimal stopping in random exploration, deriving HJB and designing a reinforcement learning algorithm.

Proposes a new theoretical framework for PbRL that requires less human feedback.

We explore a new method for discrete-time control problems using randomization and entropy.

As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary …

As data collections become larger, exploratory regression analysis becomes more important but more challenging. When observations are hierarchically clustered the problem is even more challenging because model selection with mixed effect models can produce misleading results when nonlinear effects are not included into…

This work shows how to use simulators to learn efficient exploration in real-world RL.