Survey of Monte Carlo methods for noisy, costly densities in reinforcement learning and ABC.
problem Dealing with intractable, costly, and noisy densities in real-world scenarios.
method Classification and description of Monte Carlo methodologies using surrogate models.
result Unified scheme and numerical comparisons of different methodologies.
SaML guides ML models to avoid survey biases.
problem ML models trained on survey data often ignore survey design metadata.
method Nine-step guideline for incorporating survey design metadata in ML lifecycle.
result SaML provides valid population inference from survey data.
Machine learning improves official statistics but needs rigorous validation.
problem Lack of methodological robustness in machine learning for official statistics.
method Total Machine Learning Error (TMLE) framework to validate ML models.
result TMLE addresses representativeness and measurement errors in ML models.
Paper reviews and compares methods for handling imbalanced data.
problem Handling imbalanced data sets in financial industry.
method Reviewed and compared under-sampling/over-sampling methodologies.
result Performance analysis of class-imbalance methods, modeling algorithms, and grid search criteria.
Survey analyzes economic research on cryptocurrencies using hybrid methods.
problem Lack of comprehensive literature review in cryptocurrencies economic research.
method Dual analysis combining bibliometric and close literature review.
result Updated state of cryptocurrency economic research literature.
Paper extends conformal prediction to complex survey data.
problem Applying distribution-free prediction intervals to complex survey data.
method Design-based conformal prediction for non-exchangeable data.
result Empirical guarantees of finite-sample coverage for complex survey data.
The implied volatility surface (IVS) is a fundamental building block in computational finance. We provide a survey of methodologies for constructing such surfaces. We also discuss various topics which can influence the successful construction of IVS in practice: arbitrage-free conditions in both strike and time, how to…
Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order …
Survey on making machine learning models more understandable.
problem Making predictions from machine learning models transparent and understandable for humans.
method Conducts a survey on explainable supervised machine learning approaches.
result Classifies explainable SML approaches and discusses future directions.
Survey on deep learning robust training methods for noisy labels.
problem Dealing with noisy labels in deep learning models.
method Comprehensive review of 62 robust training methods categorized by their approach.
result Analysis of noise rate estimation and evaluation methodologies.
AA extracts archetypes from data for clear feature extraction.
problem Non-convex optimization problem in AA.
method Computational procedure extracting archetypes as convex combinations of data.
result AA offers interpretable representations for high-dimensional data.
In many countries information on expectations collected through consumer confidence surveys are used in macroeconomic policy formulation. Unfortunately, before doing so, the consistency of responses is often not taken into account, leading to biases creeping in and affecting the reliability of the indices hence created…
Survey on automating geometry problem solving with large models.
problem Automating geometric problem solving with spatial understanding and logical reasoning.
method Synthesizes GPS advancements through benchmark construction, parsing, and reasoning paradigms.
result Unified analytical paradigm and emerging opportunities identified.
Survey examines distillation methods for large language models.
problem Efficiently compress large language models while preserving their capabilities.
method Knowledge Distillation and Dataset Distillation techniques.
result Integrating KD and DD can produce more effective and scalable compression strategies.
Survey on Allen-Cahn equations and systems, focusing on multiplicity results and geometric interpretation.
problem Multiplicity results for Allen-Cahn equations and systems in singular perturbation regime.
method Photography method, variational-topological approach based on localized approximate solutions and barycenter maps.
result Encoding of topology into multiplicity results through variational-topological approach.
Rubin LSST DESC uses AI/ML for dark energy research.
problem Challenges in uncertainty quantification and model robustness for AI/ML in DESC.
method Bayesian inference, physics-informed methods, validation frameworks, active learning.
result AI/ML methods are essential but require rigorous evaluation and governance.
Unified RL survey for healthcare AI interventions.
problem Limited real-life application of RL in healthcare.
method Unified technical survey and case studies.
result Bridge between dynamic treatment regimes and mobile health.
Survey on causal interpretability models for machine learning.
problem Understanding and explaining the decisions made by machine learning models.
method Comprehensive survey on causal interpretable models.
result Evaluation metrics for interpretability are discussed.
Discretization of curves is an ancient topic. Even discretization of curves with an eye toward differential geometry is over a century old. However there is no general theory or methodology in the literature, despite the ubiquitous use of discrete curves in mathematics and science. There are conflicting definitions of …
Recently, a number of statistical problems have found an unexpected solution by inspecting them through a "modal point of view". These include classical tasks such as clustering or regression. This has led to a renewed interest in estimation and inference for the mode. This paper offers an extensive survey of the tradi…
This review article surveys data augmentation MCMC algorithms.
problem Sampling from intractable probability distributions.
method Comprehensive study of DA MCMC algorithms, their convergence properties, and acceleration strategies.
result Synthesizes recent developments and provides insights for researchers.
Survey explores how transfer learning improves deep reinforcement learning.
problem Challenges in reinforcement learning efficiency and effectiveness.
method Categorizes and analyzes transfer learning approaches.
result Transfer learning enhances reinforcement learning performance.
Flexible models predict US Census survey response rates.
problem Predicting survey response rates in the US Census Bureau.
method Nonparametric additive models with structured interactions using ℓ0-based penalization.
result Models lead to predictions comparable to black-box methods but are interpretable.
Balance corrects biased survey data for more accurate insights.
problem Bias in survey data leads to inaccurate insights and underperforming models.
method Three steps: bias understanding, weight adjustment, and evaluation.
result Corrected data leads to more accurate ML model training and insights.
Bayesian nonparametrics adapt model complexity to diverse datasets.
problem Complex challenges across statistics, computer science, and engineering.
method Flexible Bayesian nonparametric models that adapt model complexity.
result Bayesian nonparametrics offer innovative solutions to multi-object tracking.
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are other…
Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation, …
Fuzzy Forests reduces feature space in high-dimensional survey data.
problem High-dimensional and highly correlated datasets in social science.
method Fuzzy Forests algorithm, a variant of Random Forests.
result Partisan polarization was the strongest factor in the 2020 presidential election.
Quantum computing offers financial industry new optimization and risk management tools.
problem Traditional computing limits financial industry's problem-solving capabilities.
method Structured review of quantum computing platforms, algorithms, and use cases.
result Quantum computing can enhance financial industry applications like optimization and risk management.
PICZL improves photometric redshifts for AGN in all-sky surveys.
problem Challenges in accurately computing photo-z for AGN due to interplay of SMBH and host galaxy emissions.
method PICZL uses an ensemble of CNNs with cross-channel integration of image and catalog data, leveraging Gaussian mixture models.
result PICZL achieves a photo-z variance of 4.5% and outlier fraction of 5.6% on a validation sample of 8098 AGN, outperforming previous methods.
To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, com…
Survey of financial foundation models for diverse applications.
problem Challenges in applying general-purpose FMs to financial tasks.
method Review of financial foundation models (FFMs) in three modalities.
result Emergence of FFMs designed specifically for finance.
Survey on concept factorization methods for better feature learning.
problem Understanding the quality of learned features in representation learning.
method Categorizing and summarizing recent advances in concept factorization methods.
result Provides insights into theoretical basis and current developments in concept factorization.
This paper surveys RL for combinatorial optimization, focusing on TSP.
problem Optimizing solutions for combinatorial optimization problems.
method Reinforcement learning applied to combinatorial optimization problems, specifically the TSP.
result Deep learning mechanisms enhance RL algorithms for near-optimal solutions.
Survey of LLMs in finance tasks, highlighting progress and challenges.
problem Transforming financial practices with advanced LLMs.
method Exploration of various financial tasks, categorization, and analysis of methodologies.
result Unlocking novel opportunities for financial applications with LLMs.
This survey analyzes knowledge discovery in cryptocurrency transactions.
problem Understanding user behaviors and collective actions in cryptocurrency transactions.
method Data mining techniques and literature review.
result Classified existing research into three aspects and discussed major findings.
Survey of deep causal models for industrial applications.
problem Estimating causal effects using deep learning.
method Deep causal models map covariates to a representation space and use objective functions for unbiased counterfactual data estimation.
result Comprehensive overview of deep causal models with industry applications.
Survey on combining causal models with deep generative models for improved explainability and fairness.
problem Deep generative models lack explainability, induce spurious correlations, and poor out-of-distribution extrapolation.
method Structural causal models (SCMs) combined with deep generative models to address shortcomings.
result Causal generative models offer robustness, fairness, and interpretability.
Companies may be achieving only a third of the value they could be getting from data science in industry applications. In this paper, we propose a methodology for categorizing and answering 'The Big Three' questions (what is going on, what is causing it, and what actions can I take that will optimize what I care about)…
Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy require…
This paper surveys UQ methods for deep learning.
problem Overconfident predictions in deep learning models.
method Categorizes UQ methods by uncertainty sources.
result Identifies strengths and limitations of each category.
AI improves precision health through adaptive interventions.
problem Improving healthcare through personalized and dynamic treatments.
method Reinforcement learning (RL) for adaptive interventions in digital health.
result RL shows promise in dynamic healthcare problems.
The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecast…
Survey examines LLMs for financial data analysis.
problem Challenges in processing multifaceted financial data.
method Synthesizes recent LLM developments.
result Promises new avenues for financial data analysis.
Bayesian methods improve learning from related domains.
problem Finding optimal information to transfer between domains.
method Bayesian approaches to transfer learning.
result Bayesian methods outperform frequentist competitors in simulations.
With a growing interest in using non-representative samples to train prediction models for numerous outcomes it is necessary to account for the sampling design that gives rise to the data in order to assess the generalized predictive utility of a proposed prediction rule. After learning a prediction rule based on a non…
We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populatio…
Image classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequately trained. Gathering a correctly annotated dataset is not always feasible due to several factors, such as the expensiveness of the label…