PEF identifies the best subgroup performance balance for fairness.
problem Fairness constraints can degrade performance in skewed datasets.
method PEF identifies the closest operating point on the Pareto curve of subgroup performances.
result PEF achieves Pareto levels in accuracy for all subgroups.
Proposes a method to learn fair predictors for multiple subgroups with limited data.
problem Fairness and accuracy issues in learning from multiple subgroups with limited data.
method Formulates a bilevel objective to learn subgroup-specific predictors and a fair predictor that is close to all of them.
result The method effectively controls group sufficiency and generalization error, improving fairness and accuracy.
Empirical study on rich subgroup fairness for machine learning.
problem Ensuring fairness across large subgroups in machine learning.
method An algorithm that learns subject to rich subgroup fairness constraints.
result Rich subgroup fairness leads to large gains in fairness with mild accuracy costs.
Framework improves fairness in predictions across subgroups.
problem Systematic biases in machine learning predictions.
method Multiaccuracy auditing and post-processing for black-box models.
result Improves accuracy for minority subgroups in diverse applications.
Improves fairness in machine learning by adding underrepresented group data.
problem Machine learning biases across subgroups due to under-representation or societal biases.
method Data augmentation via pairwise mixup across subgroups to balance subpopulations.
result Achieves fair outcomes with robust if not improved accuracy.
The paper studies and mitigates accuracy disparity in regression models.
problem Accuracy disparity between different demographic subgroups in high-stakes domains.
method Error decomposition theorem and distribution alignment algorithm.
result The proposed algorithm effectively mitigates accuracy disparity while maintaining predictive power.
New method identifies subgroups in censored data.
problem Identifying meaningful patterns in heterogeneous populations.
method Combining inverse probability weighting, M-estimation, and concave pairwise fusion penalization.
result Robust approach for censored data under heterogeneous AFT models.
ISP improves DNN uncertainty for better subgroup accuracy.
problem Improving accuracy-group robustness in deep neural networks.
method Introspective Self-play (ISP) adds an introspection task to improve bias-awareness and uncertainty.
result ISP improves the accuracy-group robustness trade-off of AL methods.
Differential privacy reduces model accuracy more for underrepresented groups.
problem Differential privacy impacts model accuracy differently across groups.
method Training neural networks with differential privacy (DP-SGD).
result DP-SGD reduces accuracy more for underrepresented groups.
Proposes a new method for subgroup analysis using optimal trees with parameter fusion.
problem Challenges of greedy heuristics and overfitting in tree-based recursive partitioning methods.
method Fused optimal causal tree method leveraging mixed integer optimization (MIO) for globally optimal partitions and parameter fusion.
result Substantial improvement in subgroup discovery accuracy and statistical efficiency.
LSGD improves deep learning training efficiency by synchronizing and decentralizing SGD.
problem Asynchronous SGD's accuracy issues and synchronous SGD's communication inefficiency.
method LSGD divides nodes into subgroups with centralized communication and decentralized computation.
result LSGD achieves better accuracy and efficiency than synchronous and asynchronous SGD.
GAME improves matrix completion by considering subgroup-specific latent structures.
problem Heterogeneous data with overlapping categories, smoothing away subgroup-specific variation.
method Group-Aware Matrix Estimation (GAME) with overlapping nuclear-norm penalties.
result GAME outperforms global low-rank estimators in structured missingness regimes.
New technique reduces gender discrimination in credit lending models.
problem Bias and unfairness in credit lending predictions.
method Subgroup Threshold Optimizer (STO) technique.
result Reduces gender discrimination by over 90%.
We discover subgroups for Cox model survival analysis, improving model accuracy.
problem Finding interpretable subsets of data where Cox model is highly accurate.
method Developed new metrics (EPE, CRS) and algorithms to solve subgroup discovery problem.
result Our methods improve model fit and recover known nonlinearities in data.
This work improves model estimation efficiency and subgroup identification in networked systems.
problem Improving model estimation efficiency and subgroup identification in networked systems.
method A tree-based l1 penalty and decentralized ADMM algorithm are used to solve the objective function in parallel. result The approach outperforms in estimation accuracy, computation speed, and communication cost.
The paper explores fairness in machine learning by setting subgroup sample complexity bounds and advocating for human intervention.
problem Machine learning models often show different performance metrics for different subgroups, due to various factors.
method The paper presents lower bounds of subgroup sample complexity for metric-fair learning and proposes an approach using individual fairness definitions for cases where subgroup samples are insufficient.
result For a classifier to be fair, adequate subgroup population samples are necessary, and model dimensionality must align with subgroup population distributions.
Quantum method calculates risk contributions in credit portfolios efficiently.
problem Quantifying risk concentration in subgroups of a credit portfolio.
method Quantum algorithm for simultaneous estimation of multiple expected values.
result Quantum method scales better than classical methods for finely divided subgroups.
Randomized predictions ensure fair and accurate individual calibration in machine learning.
problem Systematic bias in typical calibration methods leads to unfair predictions for certain subgroups.
method Randomization of predictions to enforce individual calibration, trading off bias with variance.
result Randomized regression functions are more calibrated for arbitrary subgroups and achieve higher utility.
Estimates statistical power for cluster analysis in biomedical research.
problem Lack of established methods to compute a priori statistical power for cluster analysis.
method Simulation studies varying subgroup size, number, separation, and covariance structure.
result Sufficient statistical power achieved with small samples (N=20-30) for large effect sizes.
Machine learning predicts arithmetic curve invariants with high accuracy.
problem Classifying arithmetic curves based on their invariants.
method Training machine learning algorithms on datasets of elliptic and genus 2 curves.
result High accuracy in classifying curves, including rank, torsion, and integral points.
SkewSize detects model biases by analyzing mistakes across subgroups.
problem Benchmarking model performance in the presence of spurious correlations.
method Introducing SkewSize, a metric that captures bias from model mistakes.
result SkewSize highlights biases not captured by other metrics.
The paper calculates subgroup distortions in 3-manifold groups.
problem Understanding subgroup distortions in 3-manifold groups.
method Computed all finitely generated subgroups of finitely generated 3-manifold groups and analyzed their distortions.
result Subgroup distortions in 3-manifold groups are linear, quadratic, exponential, or double exponential.
Algorithm learns fair representations without sacrificing accuracy across groups.
problem Mitigating disparity among different demographic subgroups in classification.
method Balanced error rate and conditional alignment of representations.
result Improves utility-fairness trade-off on balanced datasets.
Stable and Morse subgroups coincide in mapping class groups.
problem Understanding subgroup properties in mapping class groups.
method Analyzing stability and Morse properties in mapping class groups.
result Stability and Morse properties coincide for subgroups of infinite index in mapping class groups.
Improved sampling for Bayesian neural networks reduces vanishing acceptance rates and increases predictive accuracy.
problem Sampling inefficiency in Bayesian neural networks, especially with deep architectures and large datasets.
method Approximate blocked Gibbs sampling to partition and sample subgroups of parameters.
result Increased predictive accuracy and quantification of predictive uncertainty in classification tasks.
The paper introduces a model comparison framework for identifying fairness differences between machine learning models.
problem Comparing fairness in machine learning models across different subgroups.
method Develops a framework to automatically identify subgroups where models differ in fairness metrics.
result Identifies subgroups where machine learning models disagree on fairness-related quantities.
Regular subgroups of SL3(R) are identified and ruled out.
problem Identifying and characterizing regular subgroups of SL3(R).
method Using Kapovich–Leeb–Porti and Guichard–Wienhard divergent subgroups criteria, and Oh's results.
result Regular subgroups of SL3(R) are precisely lattices in minimal horospherical subgroups.
Study shows lower central subgroups of a subgroup don't contain those of the free group.
problem Whether lower central subgroups of a subgroup contain those of the free group.
method Analyzes the relationship between lower central subgroups of a free group and its subgroup.
result Lower central subgroups of a subgroup do not contain those of the free group if the subgroup does not normally generate the free group.
The paper investigates how data imbalance affects fairness and accuracy in differentially private deep learning.
problem Impact of data imbalance on fairness and accuracy in differentially private deep learning.
method Study the effects of different levels of imbalance in the data on the accuracy and fairness of decisions made by a model trained with differential privacy.
result Small imbalances and loose privacy guarantees can cause disparate impacts on model accuracy and fairness.
The paper explores geometric finiteness in mapping class groups and constructs new examples of these subgroups.
problem Understanding geometric finiteness in mapping class groups and constructing new examples.
method Examined several constructions of subgroups and determined conditions for geometric finiteness.
result Provides new examples of parabolically geometrically finite and reducibly geometrically finite subgroups.
Study on braid group quotients by congruence subgroups.
problem Understanding the image of congruence subgroups in GL(n,Z).
method Characterization through symplectic congruence subgroups.
result Open problem solved: image of congruence subgroups in GL(n,Z).
Proposes a new method for finding non-redundant, standout subgroups in numeric datasets.
problem Mining large numbers of redundant subgroups in numeric datasets.
method Dispersion-aware problem formulation based on MDL principle for subgroup set discovery.
result Empirically demonstrates SSD++ returns outstanding subgroup lists.
Proves Congruence Subgroup Property for two types of groups.
problem Proving Congruence Subgroup Property for specific groups.
method Elementary proof of Johnson filtration and geometric subsurface inclusions.
result Proves Congruence Subgroup Property for nilpotent quotients and subsurface subgroups.
New method constructs non-quasiconvex subgroups in hyperbolic groups.
problem Creating non-quasiconvex subgroups in hyperbolic groups.
method Using Stallings-like techniques on right-angled Coxeter groups (RACGs).
result Explicit examples of non-quasiconvex subgroups constructed.
New techniques reveal subgroup properties in Coxeter groups.
problem Characterizing and understanding subgroups of right-angled Coxeter groups.
method Using cube complexes and Stallings-like techniques to study subgroups.
result Reflection and one-ended subgroups are quasiconvex.
Machine learning classifies complex geometric patterns with high accuracy.
problem Classifying extension degree of dessins d'enfants over the rationals.
method Deep feed-forward neural network trained on machine learning.
result 0.92 accuracy in classification with 0.03 standard error.
Characterizes knotted subgroups of Lie groups and provides examples.
problem Defining and understanding knotted subgroups of Lie groups.
method Geometric equivalence, one-parameter subgroups, infinitesimal elements, canonical forms, spectrum analysis.
result Completely classified knotted subgroups of SL(2,R) and SL(3,R).
New benchmark predicts cardiometabolic risk from accelerometer data, with varying accuracy.
problem Lack of accurate tabular benchmarks for cardiometabolic risk from accelerometer data.
method Tabular learning methods (ridge regression, XGBoost, TabPFN v2) applied to NHANES data.
result TabPFN v2 achieves best performance, but triglycerides remain largely unpredictable.
Study subgroups of pro-p PD^3 groups, finding specific conditions.
problem Characterize subgroups of pro-p PD^3 groups. method Analyzes properties of subnormal and finitely presented subgroups.
result Conditions on subgroups of pro-p PD^3 groups. Torelli subgroup rigidity in Out(F_N) proven for N≥4.
problem Proving rigidity of Torelli subgroup in Out(F_N).
method Injective homomorphisms and conjugation analysis.
result Every injective homomorphism from Torelli subgroup to Out(F_N) is conjugate to inclusion.
Extends Anosov subgroup definitions to more general groups.
problem Characterize subgroups of semisimple Lie groups.
method Relativizes characterizations of Anosov subgroups.
result Proves implications and equivalences between relativized characterizations.
Artin-Tits groups of spherical type have parabolic subgroups with lattice properties.
problem Characterize parabolic subgroups in Artin-Tits groups of spherical type.
method Prove intersection and inclusion properties, show minimal parabolic subgroups, and define a simplicial complex.
result Parabolic subgroups form a lattice and have unique minimal subgroups.
Robust subgroup discovery finds non-redundant, statistically significant subgroups.
problem Finding interpretable, robust subgroups from data.
method Formulated subgroup lists for univariate and multivariate targets, used MDL principle and greedy heuristic SSD++.
result SSD++ outperforms previous methods in quality and size of subgroup lists.
New theorem on subgroup dynamics of Out(F_N).
problem Understanding subgroups of Out(F_N).
method Analogous to Ivanov's and Handel-Mosher's theorems.
result Subgroups either contain atoroidal elements or fix conjugacy classes.
No hyperbolic group can have an infinite chain of free subgroups of fixed rank.
problem Infinite ascending chains of free subgroups in hyperbolic groups.
method Proof by contradiction and properties of hyperbolic groups.
result Hyperbolic groups do not contain strictly ascending chains of free quasiconvex subgroups of constant rank.
Sparse GFA identifies disease factors in FTD subgroups.
problem Heterogeneity in neurological disorders hinders understanding and treatment.
method Sparse Group Factor Analysis (GFA) with regularised horseshoe priors.
result Identified latent disease factors differentially expressed in FTD subgroups.
Study infinite subgroups of higher rank Lie groups, focusing on Anosov subgroups.
problem Understanding properties of Anosov subgroups in higher rank semisimple Lie groups.
method Characterize Anosov subgroups through geometric, coarse geometric, and dynamical viewpoints.
result New equivalent characterizations of Anosov subgroups, capturing rank one behavior.
The paper introduces a new bias measure, infra-marginality, to quantify unfairness in group fairness.
problem The trade-off between group fairness and individual-level bias in decision-making.
method Proposes a new notion of η-infra-marginality, proves its independence from accuracy, and provides practical methods to measure and avoid it. result High accuracy does not lead to high infra-marginality, but maximizing group fairness often increases infra-marginality.