Detects malicious accounts in permissionless blockchains using graph properties and ML.
problem Identifying and classifying malicious accounts in permissionless blockchains.
method Temporal graph properties, ML algorithms (ExtraTreesClassifier, K-Means), cosine similarity, behavior change analysis.
result ExtraTreesClassifier performs best in detecting malicious accounts on Ethereum blockchain.
Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores. We present Sage, a differentially private (DP) ML platform that bounds the cumulative leakage of training data through models. Sage builds upon the ric…
This work tackles ML accountability by quantifying contributions of algorithms and datasets.
problem Determining responsibility for poor performance on subpopulations at test time.
method Proposes Extended Shapley as a principled framework for joint credit assignment.
result Demonstrates Extended Shapley can be used to address ML accountability questions.
Introduces lookahead counterfactual fairness to account for downstream effects of ML predictions.
problem Downstream effects of ML predictions on individuals not considered by counterfactual fairness.
method Introduces lookahead counterfactual fairness (LCF), a new fairness notion that considers future status. Proposes an algorithm based on theoretical conditions.
result Proposes an algorithm to achieve lookahead counterfactual fairness and validates it on synthetic and real data.
This paper examines AI and ML bias and fairness issues.
problem Bias and unfairness in AI and ML algorithms.
method Overview of bias and fairness issues, types and sources of data bias, algorithmic unfairness, fairness metrics, and de-biasing techniques.
result Discussion of the limitations of fairness metrics and de-biasing techniques.
This guide simplifies applying differential privacy to machine learning models.
problem Limited practical guidance for achieving good privacy-utility-computations in ML models.
method Comprehensive self-contained guide covering theory and practical implementation.
result Achieves best possible DP ML model with rigorous privacy guarantees.
Community-based system dynamics improves ML fairness by involving excluded stakeholders.
problem Bias in ML system development during problem formulation.
method Community-based system dynamics (CBSD) for stakeholder participation.
result CBSD facilitates deeper problem understanding and bias mitigation.
AI methods are energy-intensive, but efficiency alone isn't enough for sustainability.
problem AI methods are energy-intensive and contribute to climate change.
method Critically examines the limitations of efficiency in improving environmental sustainability of AI.
result Efficiency alone is insufficient to address the environmental impacts of AI.
Fair ML systems can be safe ML systems by considering uncertainty.
problem Safety of data-driven decision systems is often neglected.
method Viewing ML systems as socio-technical, uncertainty-aware modeling.
result Fair models should be uncertainty-aware, e.g. through distributional regression.
Bayesian framework improves ML classification models' uncertainty estimates.
problem Ensuring trustworthy AI predictions with explicit uncertainty quantification.
method Proposes a Bayesian framework for generative ML classification models that accounts for input measurement uncertainty.
result The BQDA model outperforms other models in terms of interpretability, explicit uncertainty modeling, and computational efficiency.
Develops robust ML systems for predictive uncertainties and adversarial examples.
problem Uncertainties and vulnerabilities in ML predictions and adversarial examples.
method Bayesian structural time series models, SG-MCMC, SVGD, Markov chain samplers, reinforcement learning.
result Improved robustness and efficiency in Bayesian inference and adversarial machine learning.
New method corrects ML for informative sampling in time-series treatment outcomes.
problem Informative sampling in irregularly observed data hinders accurate treatment outcome forecasting.
method Formalized as covariate shift, proposed inverse intensity-weighting framework, TESAR-CDE.
result TESAR-CDE effectively learns treatment outcomes under informative sampling.
Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data. In many real-world applications, however, some potential …
Gaussian DP improves reporting of ML algorithms' differential privacy guarantees.
problem Incomplete and misleading DP guarantees for ML algorithms.
method Using non-asymptotic Gaussian Differential Privacy (GDP) to provide accurate bounds on privacy profiles of ML algorithms.
result GDP captures the entire privacy profile of DP-SGD and related algorithms with virtually no error.
New fairness measures account for prediction uncertainties to detect bias.
problem Fairness of ML models is not well-defined and measures are limited.
method Introduce new fairness measures based on aleatoric and epistemic uncertainties.
result Uncertainty-based measures reveal bias not captured by existing measures.
Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often excessively reduces accuracy. We propose Bayesian differential…
Proposes VEESA pipeline for interpreting ML models with functional data.
problem Interpreting ML models with functional data inputs for high-consequence applications.
method VEESA pipeline using efPCA and PFI.
result Provides explanation of model's use of functional data variability for prediction.
Proposes HEX for human-in-the-loop explainability in ML models.
problem Ensuring accountability and reliability in ML models used for high-stakes decisions.
method Human-in-the-loop deep reinforcement learning approach to MLX.
result Synthesizes decision-specific policies from any classification model.
Underspecified ML models can behave unpredictably in real-world use.
problem ML models can fail in real-world deployment due to ambiguous predictors.
method Identified underspecification as the cause, showing it affects various ML domains.
result Underspecified models can behave differently in deployment domains.
Due to recent technological developments, Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been successfully used to process and extract knowledge from a variety of complex problems. However, a thorough ML approach is complex and highly dependent on the problem at hand. Additionally, implementing …
A human-in-the-loop ML framework for precision dosing reduces expert workload and removes bias.
problem High cost of data annotation and lack of appropriate data for ML models.
method Incorporates human experts into the model learning loop to improve interpretability and reduce bias.
result The approach learns interpretable rules from data and potentially lowers expert workload.
ML system reduces overdraft fees for Mint users.
problem Overdraft fees burden Americans, leading to financial hardship.
method ML-driven overdraft early warning system (ODEWS).
result Saved $3 million in overdraft fees for Mint customers.
Develops statistical inference for ML-discovered heterogeneous treatment effects.
problem ML algorithms may fail to accurately ascertain heterogeneous treatment effects in practical settings.
method Neyman's repeated sampling framework, dividing sample into groups, estimating average treatment effects, constructing confidence intervals.
result Valid methodology for estimating and testing heterogeneous treatment effects without relying on ML algorithm properties.
The workshop focuses on AI principles for structured data.
problem Using AI on structured data for decision-making.
method Addressing principles of privacy, accountability, interpretability, robustness, and reasoning.
result Designing approaches to use structured data for reliable decisions.
Systematic review of ML models for detecting social media deception.
problem Detecting fake news, spam, and fake accounts on social media.
method 36 studies evaluated using PROBAST tool, identifying biases and limitations.
result Over-reliance on accuracy in imbalanced data settings is a flaw.
Fog learning distributes ML model training across heterogeneous devices and networks.
problem Challenges with conventional federated learning in heterogeneous networks.
method Intelligent distribution of ML model training across nodes from edge devices to cloud servers.
result Enhanced federated learning with multi-layer hybrid framework considering network, heterogeneity, and proximity.
The study uses ML and AI to forecast pension fund mortality, outperforming traditional methods.
problem Incorporating longevity risk into pension fund financial assessments.
method Employed actuarial learning with ML/AI techniques (regression trees, random forest, boosting, XGBoost, CatBoost, neural networks) on actuarial data.
result ML/AI algorithms outperform the Lee-Carter model in mortality forecasting for pension funds.
Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets. The discovery of a number of well-known attacks such as dataset poisoning, adversar…
Algorithm reduces long-term policy regret in ML decision-making.
problem Capturing long-term impacts of ML decisions in communities.
method Modeling communities as arms in a multi-armed bandit problem, defining policy regret as a stronger metric than external regret.
result Algorithm achieves provably sub-linear policy regret for long time horizons.
The study assesses ML model robustness under worst-case subpopulations.
problem ML model performance degradation under non-training population.
method Two-stage estimation procedure for evaluating worst-case robustness over subpopulations.
result The method certifies model robustness and prevents unreliable deployments.
Paper introduces Functional Effects Models to account for individual heterogeneity in panel data.
problem Accounting for preference heterogeneity in panel data with machine learning.
method Functional Effects Models using gradient boosting decision trees and deep neural networks to learn individual-specific preference parameters.
result Functional Effects Models outperform traditional models in learning inter-individual heterogeneity and predictive performance.
In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods rely on machine learning (ML) algorithms to approximate important aspects of the brittle fracture problem. In addition to the ML algorithms…
Predictive models can be used for causal inference with feature selection.
problem Limitations of predictive models in interpreting causal relationships.
method Constrained learning process by selecting features according to Pearl's backdoor adjustment criterion.
result Causal models provide near unbiased effect estimates and better generalization.
Framework for deferring decisions to experts in sequential medical settings.
problem Myopic and non-adaptive decision-making by ML models in sequential medical contexts.
method Sequential Learning-to-Defer (SLTD) framework using model-based reinforcement learning.
result Adaptive deferral policy improves trade-off between long-term outcomes and deferral frequency.
This paper highlights the overlooked role of preprocessing hyperparameters in machine learning model performance.
problem The neglect of preprocessing hyperparameters in machine learning model evaluation.
method Empirical review and illustration of different procedures for generating and evaluating prediction models.
result Users may fail to report or account for preprocessing hyperparameter optimization, leading to exaggerated performance claims.
Paper compares ML methods for credit scoring, highlighting feature selection and scaling impacts.
problem Determining default risk in credit scoring models.
method Eight ML methods (SVM, Naive Bayes, DT, RF, XGBoost, KNN, MLP, LR) with feature selection and scaling.
result Feature selection and scaling improve model performance in credit scoring.
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.
Federated learning technique improves convergence speed with communication delays.
problem Communication delays between edge nodes and aggregator in federated learning.
method Developed FedDelAvg, a technique that generalizes federated averaging to incorporate a weighting between current local model and delayed global model.
result FedDelAvg achieves a significant improvement in convergence speed, especially when optimizing the weighting scheme to account for delays.
Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired …
A fundamental object in a hyperbolic 3-manifold M is its convex core C(M), defined as the smallest closed non-empty convex subset of M. We investigate the way the geometry of the boundary S of C(M) varies as we vary the hyperbolic metric of M. Thurston observed that the intrinsic metric of S is hyperbolic, and that its…
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal…
Decision trees perform well in complex interactions, even when interactions are not fully accounted for.
problem Interpreting complex interactions in machine learning models.
method Experiments on datasets and two methods for robust GLMs.
result Tree depth compensates for model misspecification, enhancing performance in complex scenarios.
The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive a…
The paper studies calibration in ML models for wireless networks, showing key theoretical and practical insights.
problem Ensuring ML models in wireless networks deliver well-calibrated confidence scores for reliable decision-making.
method Theoretical analysis and simulation-based experiments using Platt scaling and isotonic regression.
result Well-calibrated models improve the system's minimum achievable OP and are part of a broader class of predictors.
Methodology creates holdout and test/train sets for ML studies, preserving data for future research.
problem Preserving data for future research studies that are analysis-naive.
method Modification of k-fold cross-validation, randomization, and three-way split (holdout, test, training).
result Efficiently creates holdout and test/train sets without forcing.
Sparse Gaussian process hyperparameters optimized using MCMC.
problem Hyperparameter uncertainty leads to biased estimates and underestimation of predictive uncertainty.
method Proposes an MCMC algorithm to sample from the hyperparameter posterior in sparse Gaussian process regression.
result Significantly improves sampling efficiency in the Gaussian likelihood case.
A novel approach for augmenting histopathological images by blending Gaussian-Laplacian pyramids.
problem Data imbalance and inter-patient variability in histopathological images.
method Image blending using Gaussian-Laplacian pyramids to distribute inter-patient variability.
result Promising gains in performance compared to existing data augmentation techniques.
Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engineering. A plethora of ML applications transform human lives a…