arXiv research

Automates organizing diverse web data into a hierarchical topic model.

Neural model with parameterized algorithms improves graph CO problem solving.

This paper reviews R packages for automating data analysis tasks.

Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling and hyper-parameter optimization. Existing solutions attempt to adaptiv…

grangersearch tests causal relationships in time series data.

This paper introduces a new system for discovering patterns in morphogenetic systems using modular architecture and unsupervised learning.

In our previous study, we introduced stable specification search for cross-sectional data (S3C). It is an exploratory causal method that combines stability selection concept and multi-objective optimization to search for stable and parsimonious causal structures across the entire range of model complexities. In this st…

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in st…

ECO-DQN uses RL to improve combinatorial optimization solutions by continuous exploration.

New greedy algorithms improve Bayesian optimisation performance.

ACA identifies and explains anomalies in data.

This paper improves self-play learning in games by manipulating experience distributions.

Efficiently decomposes tensors with Boolean factors using BMP.

Study speculative trading using RL with exploratory framework.

This paper quantifies privacy loss in exploratory data analysis.

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

The paper proposes a new policy training objective to reduce exploration in self-play.

Study of entropy-regularized LQG MFGs with exploratory actions.

Study finds key investing characteristics for success in equity markets.

DE is a new exploration method that limits resource usage based on expected improvement and surprise.

ETGL-DDPG improves DDPG for sparse reward control with new exploration and replay techniques.

Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.

Causal modeling has long been an attractive topic for many researchers and in recent decades there has seen a surge in theoretical development and discovery algorithms. Generally discovery algorithms can be divided into two approaches: constraint-based and score-based. The constraint-based approach is able to detect co…

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…

The paper supports task classification from eye movements, achieving 95.4% accuracy.

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.

Paper defines a new pseudometric for measuring consensus among subsets of relations.

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 $K$ 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…

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…

RL solves large-scale MV portfolio allocation with high returns.

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

New method for portfolio management learns from past wealth evolution.

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…

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

The abstract warns against flawed empirical research in machine learning.

The paper tackles confidence calibration for exploratory machine learning problems.

TRGPPO improves PPO's exploration, leading to better performance.

Deep learning speeds up IFA estimation for large datasets.

A new method speeds up factor analysis for high-dimensional data.

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

Archetypal analysis helps understand binary data sets.

Paper introduces a new method for risk-sensitive investment management using RL.

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly availab…

This work improves adaptive sampling for multi-fidelity Gaussian processes by considering cost and uncertainty.