Study finds key investing characteristics for success in equity markets.
problem Understanding what traits lead to financial success in equity markets.
method Exploratory factor analysis and multiple linear regression on 403 respondents' data.
result Investing characteristics significantly impact individual investors' excess return.
metboost improves prediction performance in hierarchically clustered data.
problem Challenges in exploratory regression analysis with hierarchically clustered data.
method metboost extends boosted decision trees to hierarchically clustered data, constraining tree structure while allowing terminal node means to differ.
result metboost improves prediction performance by up to 15% compared to boosted decision trees.
Study predicts adverse events in Afghanistan using time series data.
problem Predicting the number of negative events in Afghanistan's theater of war.
method Regression analysis on time series data, non-conventional aggregation of districts, machine learning models.
result Predictive models show reasonable performance on historical data, but other variables do not improve prediction quality.
Study speculative trading using RL with exploratory framework.
problem Sequential optimal stopping problem over entry and exit times with general utility function and price process.
method Formulated as a sequential optimal stopping problem, solved using Cox processes driven by bounded, non-randomized intensity controls. Characterized randomized control via probability measure over jump intensities and regularized objective function by Shannon's entropy. Established error estimates and convergence of RL objective to value function.
result Closed-form solutions for optimal policy and value function are derived.
We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity consideration in quantile regression enables the explorati…
This paper quantifies privacy loss in exploratory data analysis.
problem Privacy loss in exploratory data analysis is often overlooked in privacy budgets.
method Quantitative analysis of privacy loss for statistical functions.
result Privacy loss must be considered in calculating machine learning privacy budgets.
The 2016 US election results are inferred from census microdata.
problem Estimating vote shares for specific demographic groups from aggregated election data.
method Distribution regression with multinomial-logit model, exploratory data analysis.
result Estimates vote shares for specific demographic groups (e.g., white women, Trump supporters, etc.).
Study of entropy-regularized LQG MFGs with exploratory actions.
problem Optimizing multi-population mean field games with entropy regularization.
method Introduced exploratory actions and derived optimal action distributions.
result Optimal action distributions lead to ε-Nash equilibria in finite-population MFGs.
Paper tackles expected predictions computation for arbitrary generative models.
problem Hard to compute expected predictions for arbitrary generative models.
method Identifies tractable generative and discriminative models for expected predictions.
result Tractable computation of high-order moments and expectations for classification.
This work shows how to use simulators to learn efficient exploration in real-world RL.
problem Sample complexity of real-world reinforcement learning.
method Coupling exploratory policies learned in simulators with practical approaches.
result Polynomial sample complexity in real world, exponential improvement over direct sim2real transfer.
This paper reviews R packages for automating data analysis tasks.
problem Time-consuming Exploratory Data Analysis in large, noisy data sets.
method Systematic review of 12 R packages for autoEDA.
result Identifies automated tasks and areas for future development.
Study analyzes Disney stock market performance using machine learning.
problem Forecasting stock market performance of Disney.
method Exploratory data analysis, feature engineering, model selection (linear regression).
result Linear regression model performed best.
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…
New method learns dynamical systems efficiently using active learning.
problem Efficiently learning dynamical systems from data.
method Active learning strategies leveraging Gaussian process regression.
result Data-efficient training of the model through exploratory sampling.
The paper analyzes Lending Club's loan applicants to predict default risk.
problem Predicting default risk in loan applicants of Lending Club.
method Exploratory data analysis and machine learning (Logistic Regression, Random Forest) were used.
result A credit derivative based on Credit Default Swap was designed to hedge default risk.
ABDA automatically analyzes data without expert supervision.
problem Automatic exploratory data analysis for mixed data types.
method Automatic Bayesian Density Analysis (ABDA) for missing value estimation, data type and likelihood discovery, anomaly detection, and dependency structure mining.
result ABDA provides accurate density estimation and is suitable for mixed data types.
A framework for robust exploration in reinforcement learning under ambiguity.
problem Optimal stopping under ambiguity in reinforcement learning.
method Continuous-time robust reinforcement learning framework using g-expectation and backward stochastic differential equations. result Constructs a robust exploratory stopping time approximating the optimal stopping time under ambiguity.
Action chunking and data exploration improve behavior cloning in robotics.
problem Exponential errors in learning from demonstrations for continuous control tasks.
method Action chunking and exploratory data collection.
result Control-theoretic stability is key to improving imitation learning.
Study uses big data to analyze quantum invariants.
problem Investigate structural properties of Jones polynomial.
method Exploratory and topological data analysis, including coloring, rank increase, categorification.
result Contrasts behavior of Jones polynomial under various enhancements.
Study on utility maximization with Tsallis entropy in reinforcement learning.
problem Exploring utility maximization with Tsallis entropy in reinforcement learning.
method Introducing Tsallis entropy regularizer to induce exploration, investigating specific examples, characterizing well-posedness, designing reinforcement learning algorithm.
result Characterized well-posedness and provided semi-closed-form solutions for specific examples, found distinct optimal strategies.
Proposes a new method to analyze the distributional effects of treatments.
problem Analyzing the full distributional impact of treatments beyond just the mean.
method Uses kernel conditional mean embeddings and U-statistic regression to investigate the CoDiTE.
result Demonstrates the effectiveness of the proposed method through experiments.
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…
RL solves large-scale MV portfolio allocation with high returns.
problem Large-scale mean-variance portfolio optimization.
method Continuous-time reinforcement learning with a multivariate Gaussian policy.
result Our method outperforms econometric and deep RL methods by significant margins.
Breaks the hardness conjecture for batch RL with a novel tournament-based approach.
problem Sample-efficient reinforcement learning from exploratory data.
method BVFT algorithm using pairwise comparison and state-action partition.
result Solves the learning problem in a setting previously thought impossible.
New method for portfolio management learns from past wealth evolution.
problem Optimizing portfolio selection based on past performance.
method Simulated annealing clustering for asset selection, considering past wealth evolution.
result Strategy effectively learns from past performance and performs well in practice.
Automates organizing diverse web data into a hierarchical topic model.
problem Manual classification of all scientific and popular scientific knowledge is impractical.
method Proposes an algorithm to aggregate multiple collections into a single hierarchical topic model.
result Demonstrates a web service for topical exploratory search.
New method explains high-dimensional sphere data with latent factors.
problem Understanding intricate dependence structure in high-dimensional sphere data.
method Exploratory factor analysis of the projected normal distribution with a fast alternating expectation profile conditional maximization algorithm.
result Uniformly excellent results on various data types, including tweets, brain imaging, and cancer gene expression.
New algorithm uncovers hierarchical block structure in large matrices.
problem Uncovering hierarchical block structure in symmetric matrices.
method Incremental multiresolution matrix factorization.
result Algorithm scales well to large matrices and uncovers structure one feature at a time.
Study uses RL to optimize investment with financial constraints, showing exploration benefits.
problem Optimal investment with financial constraints in continuous time.
method Reinforcement learning framework, focusing on Gaussian and truncated Gaussian distributions.
result Exploration leads to more dispersed wealth distribution with heavier tails, especially with smaller exploration parameters.
Model-based clustering approaches concern the paradigm of exploratory data analysis relying on the finite mixture model to automatically find a latent structure governing observed data. They are one of the most popular and successful approaches in cluster analysis. The mixture density estimation is generally performed …
The abstract warns against flawed empirical research in machine learning.
problem Flawed empirical research in machine learning leading to unreliable results.
method Call for more awareness of experimental knowledge plurality and epistemic limitations.
result Current empirical machine learning research should be exploratory, not confirmatory.
Proposes ALRL for single shot active learning with pseudo annotators.
problem Real-world applications where human experts are not always available.
method Substitutes human annotators with pseudo annotators providing random labels.
result ALRL outperforms state-of-the-art approaches in real-world datasets.
The paper tackles confidence calibration for exploratory machine learning problems.
problem Difficulty in curating datasets and confusion about category validity.
method Introduces four new algorithms for category-specific confidence estimation, including kernel density ratios.
result Kernel density ratios provide a novel approach to confidence calibration, especially for exploratory problems.
Deep learning speeds up IFA estimation for large datasets.
problem Slow MML estimation for large-scale IFA models.
method Importance-weighted autoencoder (IWAE) for fast VI.
result IWAE yields accurate estimates faster than MH-RM.
Uncharted Forest visualizes data associations for classification and provenance studies.
problem Exploratory data analysis in high-dimensional datasets.
method Unsupervised tree ensemble (uncharted forest) for partitioning and visualizing data.
result Visualizes class associations, sample associations, and class heterogeneity.
Machine learning predicts Shafarevich-Tate group orders of elliptic curves.
problem Predicting the order of the Shafarevich-Tate group of elliptic curves.
method Train feed-forward neural network and regression models on elliptic curve invariants.
result Models achieve high accuracy (>0.9) and predict orders not seen during training. Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemp…
A new method speeds up factor analysis for high-dimensional data.
problem Estimating covariance parameters in high-dimensional Gaussian data with limited observations.
method Matrix-free likelihood method using implicitly restarted Lanczos and limited-memory quasi-Newton algorithms.
result Our method is faster than EM without sacrificing accuracy.
The paper tackles optimal stopping problems using reinforcement learning and singular control.
problem Continuous-time and state-space optimal stopping problems.
method Formulated as a singular control problem with randomized stopping times and penalized cumulative residual entropy.
result Identified unique optimal exploratory strategy through dynamic programming.
Archetypal analysis helps understand binary data sets.
problem Explaining binary questionnaire data.
method Using archetypal analysis for binary observations.
result The approach contributes to understanding binary data sets.
ACA identifies and explains anomalies in data.
problem Explaining anomalies in non-supervised data analysis.
method Abnormal Component Analysis (ACA) using data depth.
result ACA provides a linear explanation for anomalies.
Study uses social media analytics to identify exercise-related topics.
problem Understanding exercise-related discussions on social media.
method Data collection, topic modeling, and data annotation.
result 86% of detected topics were meaningful after annotation.
A-DOGE embeds attributed graphs efficiently using density of states.
problem Efficiently represent node-attributed graphs with few numerical features.
method A-DOGE uses density of states to blend topology and attributes, leveraging efficient approximation algorithms.
result A-DOGE achieves competitive performance with modern supervised GNNs while being significantly faster.
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…
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.
Deep learning improves analysis of complex natural processes.
problem Simplistic dynamics in regression analyses of complex natural processes.
method Flexible function approximation using deep learning, relaxing standard assumptions.
result Substantial improvements in behavioral and neuroimaging data.
We explore a new method for discrete-time control problems using randomization and entropy.
problem Discrete-time linear-exponential quadratic Gaussian (LEQG) control problem.
method Introduce exploration through randomization and apply duality between free energy and relative entropy.
result Reduced LEQG problem to equivalent risk-neutral LQG control problem with entropy regularization.
Sampling strategies significantly affect feature approximations in ELA, impacting classifier accuracy.
problem The impact of sampling strategies on feature approximations in ELA.
method Analysis of feature approximations from different sampling strategies and sample sizes.
result Feature approximations from different sampling strategies do not converge, affecting classifier accuracy.