Estimates KL divergence with fairness considerations for sub-populations.
problem Fairly estimate KL divergence between distributions considering sub-populations.
method Proposes multi-group attribution for KL divergence estimation, derived from multi-calibration.
result Shows multi-group attribution provides better KL divergence estimates conditioned on sub-populations.
A new model synthesizes population with fewer structural and sampling zeros.
problem Synthesizing a feasible and diverse synthetic population from limited data.
method A deep generative model with two regularizations to minimize structural zeros and preserve sampling zeros.
result The model significantly improves feasibility and diversity of synthetic populations.
Proposes a new approach to improve disease prediction by considering who generates the data.
problem Improving disease prediction across different datasets considering who generates the data.
method Formulates domain adaptation as a multi-source hierarchical Bayesian framework.
result Improves prediction accuracy in target datasets with largely unlabelled data.
New decompositions misattribute differences between populations, even when outcomes are identical.
problem Misattribution of differences between populations using common functional decompositions.
method Extending the Kitagawa-Oaxaca-Blinder decomposition to nonlinear functional decompositions.
result Functional ANOVA and Accumulated Local Effects can misattribute differences even when outcomes are identical in two populations.
This paper proposes deep generative models to predict rare feature combinations in population synthesis.
problem Estimating rare combinations of feature attributes in multivariate distributions.
method Wasserstein Generative Adversarial Network (WGAN) and Variational Autoencoder (VAE) adapted for large-scale population synthesis.
result The models effectively recover sampling zeros while keeping structural zeros low, improving upon existing methods.
Improved local feature attributions using neighbourhood reference distributions.
problem Misleading results from global population in local model behaviour.
method Formulation of neighbourhood reference distributions and self-normalised importance sampling.
result Neighbourhood Shapley values provide meaningful sparse feature attributions.
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact,…
Multi-party machine learning leaks global dataset properties even with black-box access.
problem Leakage of global dataset properties in multi-party machine learning.
method Demonstrated leakage of sensitive attribute distributions in pooled data.
result A curious party can infer sensitive attribute distributions in other parties' data with high accuracy.
Improves flu prediction by blending environment and population info.
problem Challenges in using data from one environment in another due to feature variability and population subgroup differences.
method Population-aware hierarchical Bayesian domain adaptation framework with multiple invariant components.
result Model improves flu prediction in new environments with unlabelled data.
Proposes new attribution methods for trees with regularization.
problem Feature attribution for trees trained with regularization.
method Prediction Decomposition Attribution (PreDecomp) and TreeInner.
result TreeInner shows state-of-the-art feature selection performance.
CTGAN synthesizes population data for travel behavior simulation.
problem Synthesizing population data for agent-based transportation modeling.
method Composite Travel Generative Adversarial Network (CTGAN).
result Consistent and accurate generation of synthetic populations with tabular and sequential mobility data.
Proposes a method to simulate data for testing credit risk scorecard stability.
problem Ensuring credit risk scorecards remain representative of the population over time.
method Specification of bad ratios to generate parameter values for scorecards.
result Simulated data adheres closely to specified bad ratios.
Estimates conversion probabilities from click sequences with privacy constraints.
problem Training models in advertising with limited direct click-conversion links.
method Formalizes learning from attribution sets, constructs unbiased estimator, applies Empirical Risk Minimization.
result Empirical Risk Minimization achieves generalization guarantees and robustness against prior errors.
A framework for hypothesis testing on attributed graphs using sampling.
problem Statistical testing on graph data, especially large attributed graphs.
method Sampling-based framework with PHASE and PHASEopt for accurate and efficient hypothesis testing.
result PHASE and PHASEopt improve accuracy and efficiency of hypothesis testing in attributed graphs.
DFI maps covariates to latent representations for feature importance.
problem Feature importance when predictors are statistically dependent.
method Disentangled Feature Importance (DFI) using entropic optimal transport.
result DFI yields stable, interpretable, uncertainty-quantified attributions of shared predictive signal.
A new CVaR test reduces group performance disparity detection complexity.
problem Detecting performance disparities across multiple sensitive groups in ML models.
method Conditional Value-at-Risk (CVaR) testing to reduce sample complexity.
result Sample complexity reduced exponentially to be at most the square root of the number of groups.
Dynamic meta-learning improves multi-agent communication with natural language.
problem Static population-based approaches struggle with adapting to human partners using natural language.
method Dynamic population-based meta-learning approach.
result Agents outperform all prior work in communicating with seen partners and humans.
Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for ho…
Proposes adversarial learning for counterfactual fairness in machine learning.
problem Ensuring fairness at the individual level by simulating counterfactual samples.
method Adversarial neural learning approach to infer counterfactual samples.
result Significant improvements in counterfactual fairness for both discrete and continuous settings.
This research improves transparency in RNN predictions of ICU mortality risk.
problem Lack of transparency in RNNs' healthcare predictions.
method Introduced Learned Binary Masks (LBM) and KernelSHAP for identifying EMR variables' contributions to RNN risk of mortality predictions.
result Attribution matrices show each input feature's contribution to RNN predictions, facilitating analysis of the model and its predictions.
New research shows fairness in machine learning can sometimes make disadvantaged groups worse off.
problem The impact of fairness constraints in machine learning on different groups.
method Unified, population-level (Bayes) framework for binary classification under prevalent group fairness notions.
result Fairness in machine learning can lead to leveling down, making one or both groups worse off.
Automates RL with sample-efficient hyperparameter optimization.
problem Challenges in applying deep RL due to hyperparameter sensitivity and inefficiency.
method Population-based AutoRL framework for meta-optimizing RL algorithms and architectures.
result Reduces the number of environment interactions needed for meta-optimization by up to an order of magnitude.
The study tests and optimizes fairness in credit scoring models.
problem Discrimination in credit scoring models based on protected attributes.
method Formal testing and variable identification to optimize fairness and performance.
result Guidance on monitoring and improving algorithmic fairness in credit scoring.
A game environment simulates competition among many agents for resources.
problem Understanding large-scale multiagent interactions and resource competition.
method Developed a persistent, massively multiplayer AI environment.
result Population size affects the development of skillful behaviors and niche differentiation.
Proposes SGShift to identify shifted features causing model performance degradation under concept shift.
problem Concept shift leading to miscalibration in ML models across domains.
method SGShift method for identifying sparse set of shifted features using feature selection and statistical tools.
result SGShift identifies shifted features more accurately than baseline methods, requires few samples in the shifted domain, and is robust to complex cases.
Study causal inference under specific sampling methods with monotonicity assumptions.
problem Causal inference under biased sampling methods.
method Binary-outcome and binary-treatment case study with monotonicity assumptions.
result Monotonicity assumptions yield comparable results to random sampling.
Introduces MPR to measure and optimize representation across intersectional groups in retrieval.
problem Harmful stereotypes, cultural erasure, and social disparities in image search and retrieval.
method Develops MPR metric, practical estimation methods, theoretical guarantees, and optimization algorithms.
result Optimizing MPR yields more proportional representation across multiple intersectional groups, often with minimal retrieval accuracy compromise.
This study improves audit sampling by using sequential procedures with statistical guarantees.
problem Improving audit efficiency and reliability with statistical methods.
method Formulated as a sequential testing problem, defining null and alternative hypotheses, stopping and decision rules, and exact boundary conditions.
result Exact design yields ex ante control of decision error probabilities, and simulation-based implementation approximates this design.
Paper proposes a method to identify and treat latent discriminating features in machine learning models.
problem Fairness issues in machine learning models trained on historical data containing sensitive attributes.
method A novel algorithm that identifies and treats latent discriminating features, agnostic of the learning algorithm.
result Experimental results show near-ideal fairness measurement compared to other methods.
Consistent spectral clustering with fairness constraints on representation graphs.
problem Finding balanced clusters in similarity graphs with fairness constraints.
method Developed variants of unnormalized and normalized spectral clustering for fair planted partitions.
result Consistency results for constrained spectral clustering under fair planted partitions.
Research from a variety of fields including psychology and linguistics have found correlations and patterns in personal attributes and behavior, but efforts to understand the broader heterogeneity in human behavior have not yet integrated these approaches and perspectives with a cohesive methodology. Here we extract pa…
This paper is based on a previous publication [29]. Our work extends exception mining and outlier detection to the case of object-relational data. Object-relational data represent a complex heterogeneous network [12], which comprises objects of different types, links among these objects, also of different types, and at…
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.
The study examines how to assess skill when outcomes are noisy and insufficient.
problem Determining skill when outcomes are unreliable and insufficiently numerous.
method Characterizes decision domains with noise and effective sample size, using population-level validation methods.
result Domains with noisy outcomes are unreliable for individual skill assessment.
Study examines equity in post-Snow Uri recovery, finds disparities.
problem Disproportionate impacts on vulnerable populations during recovery.
method County and census tract level data analysis, satellite imagery, statistical procedures.
result Negative associations between non-Hispanic whites and outages, positive associations with certain demographic variables.
Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions to measure evidence conflict and stability.
problem Modern data analysis lacks mechanisms to show the clarity, conflict, or stability of evidence behind predictions.
method Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions.
result SEF measures conflict and stability, and shows that conflict can improve loss prediction beyond confidence.
This paper debiases KG embeddings by filtering sensitive attributes.
problem Social and cultural biases in KG representations.
method Exploring and filtering popularity and gender biases in KGE.
result Sensitivity of KG embeddings to sensitive attributes like gender.
We study the global probability distribution of energy consumption per capita around the world using data from the U.S. Energy Information Administration (EIA) for 1980-2010. We find that the Lorenz curves have moved up during this time period, and the Gini coefficient G has decreased from 0.66 in 1980 to 0.55 in 2010,…
RUMBoost combines RUMs and deep learning for better choice modelling.
problem Creating interpretable and robust discrete choice models.
method Gradient Boosted Regression Trees for utility functions, with constraints for interpretability and monotonicity.
result RUMBoost outperforms ML and RUM benchmarks in predictive performance and interpretability.
We provide two fundamental results on the population (infinite-sample) likelihood function of Gaussian mixture models with M≥3 components. Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical…
We extend manifold capacity to nonlinear neural representations with contextual information.
problem Efficient processing of information through neural representations.
method Theoretical framework leveraging latent directions in input space related to contextual information.
result Derivation of an exact formula for context-dependent manifold capacity.
While studying response trajectory, often the population of interest may be diverse enough to exist distinct subgroups within it and the longitudinal change in response may not be uniform in these subgroups. That is, the timeslope and/or influence of covariates in longitudinal profile may vary among these different sub…
Study creates a multimodal learning framework for CVD risk prediction.
problem Predicting cardiovascular disease risk in diverse populations.
method Combines cross modal transformers, graph neural networks, and causal representation learning.
result Model predicts personalized CVD risk with causal invariance across subpopulations.
This work addresses local fairness in machine learning models.
problem Ensuring fairness within subregions of feature space, not just global averages.
method Introduces ROAD, a Distributionally Robust Optimization (DRO) approach with adversarial learning.
result Achieves Pareto dominance in local fairness and accuracy across datasets.
This paper analyzes EM algorithm for softmax mixture models in high dimensions.
problem Modeling heterogeneous populations choosing from multiple attributes.
method Comprehensive analysis of the EM algorithm for softmax mixture models (SMMs), proving identifiability and convergence.
result EM algorithm recovers mixture atoms at near-parametric rate under suitable initialization.
This study analyzes LTS in sparse models with finite sample error bounds.
problem Robust regression in high-dimensional sparse models with limited data.
method Non-asymptotic analysis of LTS error bounds.
result Established finite sample error bounds for LTS in sparse models.
Deep neural network improves cancer detection accuracy in breast screening.
problem Improving cancer detection accuracy in breast screening.
method Two-stage training procedure using patch-level and macroscopic labels.
result Neural network achieves AUC of 0.895 in predicting cancer presence.
ZAPs rewards users based on activity, making them less susceptible to bots and sybil attacks.
problem Incentive programs in DeFi are vulnerable to bots and sybil operations.
method ZAPs combines percentile normalization, two-layer weighting, and anomaly detection.
result ZAPs reduces adversarial reward capture by 30-90 percent.