PAMA learns covariate importance for better matching in observational studies.
problem Poor performance of conventional matching methods when covariates differ in relevance.
method PAMA is a semi-supervised framework that learns covariate importance from paired data and optimizes a weighted quadratic score.
result PAMA outperforms standard methods, particularly in high-dimensional settings and under model misspecification.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
problem Impact of medication non-adherence on adverse outcomes in schizophrenia patients.
method Survival analysis, causal inference methods (T-learner, S-learner, nearest neighbor matching), different amounts of longitudinal information.
result Non-adherence to schizophrenia meds advances adverse events by 1 to 4 months.
The paper clarifies conditions for using benchmark scores in machine learning.
problem Using benchmark scores to draw scientific inferences about learning problems.
method Developing conditions of construct validity inspired by psychological measurement theory.
result Clarifies conditions under which benchmark scores support diverse scientific claims.
Bayesian model for cost-effectiveness analysis with subgroup discovery.
problem Statistical challenges in cost-effectiveness analysis, especially with non-random treatment assignment and censored data.
method Developed a nonparametric Bayesian model using Dirichlet and Gamma processes to estimate cost-survival distributions and identify cost-effectiveness subgroups.
result Identified and estimated policy-relevant causal CEA estimands using a Bayesian nonparametric g-computation procedure.
Study improves U.S. monetary policy forecasting by integrating text and data.
problem Forecasting central bank policy decisions, especially the Fed's rate changes.
method Multi-modal approach combining structured data and unstructured text from Fed communications.
result Hybrid models outperform unimodal baselines, achieving a test AUC of 0.83.
Following the financial crisis of 2007-2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the…
Method estimates treatment effect bounds in sample selection models.
problem Estimating heterogeneous treatment effects in presence of sample selection.
method Debiased/double machine learning approach for non-linear and high-dimensional confounders.
result Substantially tighter effect bounds for younger users.
This paper considers an often forgotten relationship, the time delay between a cause and its effect in economies and finance. We treat the case of Foreign Direct Investment (FDI) and economic growth, - measured through a country Gross Domestic Product (GDP). The pertinent data refers to 43 countries, over 1970-2015, - …
Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unidimensional problems and assuming instantaneous changes. We introduce change surfaces as a multidimensional and highly expressive generalizat…
Study predicts doubling of U.S. maize insurance claims due to climate change.
problem Climate change increases U.S. maize loss probability, impacting insurance claims.
method Neural Network Monte Carlo simulations to predict crop loss metrics.
result Doubling of annual probability of maize Yield Protection insurance claims by mid-century.
Wealth redistribution through Fokker-Planck equation controls preserves Gini coefficient.
problem Preserving Gini coefficient through proportional wealth tax.
method Formulating optimal redistribution as a control problem for Fokker-Planck equation.
result Progressive taxes redistribute within policy-relevant timescales.
Filters on order flow improve short-term market directionality.
problem Improving directional signals from order flow in financial markets.
method Structural filters on order lifetime, modification count, and timing applied to BankNifty index futures.
result Filters on parent orders of executed trades show stronger directional association with returns.
Modeling financial systemic risk with optimal control theory for stability.
problem Analyzing and stabilizing systemic risk in interconnected financial entities.
method Developed a theoretical model using optimal control theory, including steps for synthesizing stabilizing controllers.
result The model ensures that the H∞ norms of the mappings from disturbance to output are less than a predefined constant, stabilizing the system. Study develops a smart contract framework for efficient and fair resource allocation.
problem Lack of rigorous economic foundation in decentralized coordination and smart contract implementations.
method Mechanism design framework with provable convergence guarantees for decentralized price adjustment.
result Proves stability and robustness of the proposed mechanism under various perturbations.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
problem Challenges in evaluating indirect effects due to unmeasured confounding and unethical exposures.
method Developed a unified identification and estimation framework using proximal causal inference.
result Unified identification and estimation of PIIE and causal effect of an intervening variable in settings with pervasive unmeasured confounding.
Unified ML approach predicts ED attendances with high accuracy.
problem Managing hospital demand at emergency departments efficiently.
method Ensemble of time series and machine learning approaches with hyperparameter tuning.
result Predictions with mean absolute error of +/- 14 and +/- 10 patients, MAE of 6.8% and 8.6%.
Paper proposes a method to monitor research topic evolution.
problem Difficulty in tracking research topic diffusion and evolution.
method Deep Non-negative Autoencoder with information divergence measurement.
result Identifies evolution of research topics and discovers topic diffusions.
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.
The appeal of metric evaluation of research impact has attracted considerable interest in recent times. Although the public at large and administrative bodies are much interested in the idea, scientists and other researchers are much more cautious, insisting that metrics are but an auxiliary instrument to the qualitati…
Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large traini…
Paper discusses how financial institutions' model risk management can benefit academic research.
problem Improving academic research process and mitigating limitations.
method Adopting financial institutions' model risk management practices.
result Lessons from financial institutions can enhance academic research reliability.
QRAFTI uses multi-agent framework to improve equity factor research.
problem Replicating and developing new equity factors in large financial datasets.
method Integrates a research toolkit with MCP servers for data access and custom coding operations.
result Improves performance and explainability in multi-step empirical tasks.
Teaches reproducible research to medical students and postgrads.
problem Lack of reproducibility in medical research practices.
method Designed and delivered a lecture series on reproducible research.
result Encountered practical obstacles in reproducing a published analysis.
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and …
We discuss here researches on econophysics done from India in the last two decades. The term `econophysics' was formally coined in India (Kolkata) in 1995. Since then many research papers, books, reviews, etc. have been written by scientists. Many institutions are now involved in this research field and many conference…
This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
FedML aims to improve FL research by providing a library and benchmark.
problem Inconsistent FL algorithm development and performance comparison.
method FedML offers an open research library and benchmark supporting diverse computing paradigms and flexible API design.
result FedML facilitates fair algorithm comparison and development in federated learning.
Peer-reviewed research and mined data predict stock returns similarly.
problem Predicting stock returns using research quality.
method Cross-sectional analysis of 29,000 accounting ratios with t-statistics > 2.0.
result Post-sample performance is largely independent of whether the predictor is peer-reviewed or mined.
AI tested on 10 math questions from research.
problem Assessing AI's ability to solve research-level math problems.
method Shared 10 math questions not previously publicly available.
result Answers to questions are known to authors but encrypted.
The study examines dataset usage patterns in machine learning research.
problem Lack of attention to dataset dynamics in machine learning research.
method Analysis of dataset usage patterns across machine learning subcommunities and time periods (2015-2020).
result Increasing concentration on fewer and fewer datasets, significant adoption from other tasks, and concentration across the field on datasets introduced by elite institutions.
This workshop about triangulations of manifolds in computational geometry and topology was held at the 2014 CG-Week in Kyoto, Japan. It focussed on computational and combinatorial questions regarding triangulations, with the goal of bringing together researchers working on various aspects of triangulations and of foste…
learn2learn simplifies meta-learning research by providing a library and standardized interfaces.
problem Prototyping and reproducibility issues in meta-learning.
method Developed a library (learn2learn) with common routines and standardized interfaces.
result Fosters a community around standardized software for meta-learning research.
This study analyzes EU ETS literature trends using bibliometric methods.
problem Understanding the evolving research landscape of EU ETS.
method Bibliometric analysis of Scopus database, focusing on publication trends, themes, influential authors, and journals.
result Notable increase in research activity over two decades, particularly during policy changes and economic events.
This review explores ChatGPT in accounting and finance.
problem Understanding the current state of research on ChatGPT in accounting and finance.
method A scoping review of recent publications and working papers.
result Identifies three themes: applications, research tools, and implications.
Cryptocurrencies use blockchain tech for secure transactions, offering new research opportunities.
problem Misunderstanding of cryptocurrency technology and lack of empirical data.
method Analyzing detailed transaction data and summarizing statistics.
result Opportunity for academic research in financial economics.
Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional…
This paper presents the philosophy, design and feature-set of Neural Network Distiller, an open-source Python package for DNN compression research. Distiller is a library of DNN compression algorithms implementations, with tools, tutorials and sample applications for various learning tasks. Its target users are both en…
Automates research and development process by evaluating model capabilities.
problem Expanding experimental burden due to reading and verifying research directions.
method Proposes RD2Bench, a benchmark for evaluating data-centric automatic R&D.
result Demonstrates promising potential of LLMs in automating R&D process.
Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. Various scholarly search websites, citation recommendation engines, and research databases have been created to simplify the text search tasks. However, it is still d…
FinRobot AI agent for equity research provides comprehensive insights.
problem Narrow focus and limited discretion in AI solutions for equity research.
method Multi-agent Chain of Thought system integrating quantitative and qualitative analyses.
result FinRobot delivers insights comparable to major brokerage firms.
PEHRT harmonizes EHR data for translational research.
problem Barriers in using EHR data for translational research.
method Common pipeline including open-source code, visualization tools, and detailed documentation.
result PEHRT harmonizes EHR data to standardized ontologies and generates robust embeddings.
This article presents a summary of a keynote lecture at the Deep Learning Security workshop at IEEE Security and Privacy 2018. This lecture summarizes the state of the art in defenses against adversarial examples and provides recommendations for future research directions on this topic.
New framework aims to make neural network explanations more reliable.
problem Current interpretability methods rely on intuition and lack falsifiability.
method Proposes a framework for strongly falsifiable interpretability research.
result Falsifiable interpretability methods can generate meaningful advances in understanding DNNs.
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL. However, research in model-based RL has not been very standardized. It is fairly common for authors to experiment with self-designed environments, and there are several separate…
Survival analysis models research reproducibility, offering new insights.
problem Reproducibility crisis in machine learning research.
method Survival analysis to model reproducibility as a continuous process.
result Survival analysis provides deeper insights into research reproducibility.
Current XAI research lacks solid foundations and clear goals.
problem Inadequate conceptual, ethical, and methodological foundations in XAI research.
method Discussion of misconceptions and suggestions for improvement.
result Current XAI research needs to address conceptual, ethical, and methodological issues.
Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2…
Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definitio…