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.
Neural model with parameterized algorithms improves graph CO problem solving.
Study speculative trading using RL with exploratory framework.
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.
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…
A framework for robust exploration in reinforcement learning under ambiguity.
Study uses big data to analyze quantum invariants.
Action chunking and data exploration improve behavior cloning in robotics.
We study the problem of nonparametric dependence detection. Many existing methods may suffer severe power loss due to non-uniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat…
Study on utility maximization with Tsallis entropy in reinforcement learning.
We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, , is chosen by the user and optimally distributed am…
This work explores efficient reinforcement learning with density features in low-rank MDPs.
New method for portfolio management learns from past wealth evolution.
Breaks the hardness conjecture for batch RL with a novel tournament-based approach.
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…
Extends model-x framework to handle missing data.
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…
The abstract warns against flawed empirical research in machine learning.
In exploratory data analysis, we are often interested in identifying promising pairwise associations for further analysis while filtering out weaker, less interesting ones. This can be accomplished by computing a measure of dependence on all variable pairs and examining the highest-scoring pairs, provided the measure o…
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,…
The paper tackles optimal stopping problems using reinforcement learning and singular control.
ACA identifies and explains anomalies in data.
The paper extends mean field results to three-layer neural networks using SGD.
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…
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…
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…
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…
We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to…
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.
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…
In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to known labels. This paper studies a new problem setting in which there are unknown classes in the training data misperceived as other labels, and thus …
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.
Interpretable model for Granger causality using neural networks.
PEARL uses reinforcement learning to improve matrix preconditioners.
This study analyzes data science vocabulary changes over 13 years.
Sampling strategies significantly affect feature approximations in ELA, impacting classifier accuracy.
NeuralRBMLE tackles explore-exploit trade-offs in contextual bandits with neural networks.
Paper uses RL to optimize multi-asset portfolios in fluctuating markets.
Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex and enables one to solve the easier sub task at hand first. Generating a curriculum for such guided learning involves subjecting the agent to…