Linking output sensitivity to deep learning generalization.
problem Understanding and comparing the generalization properties of deep neural networks.
method Linking the loss function to output sensitivity and analyzing its relation to bias-variance decomposition.
result Output sensitivity is a strong metric for comparing generalization performance of deep networks.
Paper proposes a new FRL algorithm for continuous sensitive attributes using EIPM.
problem Existing FRL algorithms cannot handle continuous sensitive attributes.
method Introduces EIPM to assess fairness in representation space for continuous attributes and proposes FREM algorithm.
result FREM outperforms other methods in fairness evaluation for continuous sensitive attributes.
Proposes a method to select fair performance metrics through metric elicitation.
problem Choosing fair performance metrics in multiclass classification with multiple sensitive groups.
method Metric elicitation strategy that requires only relative preference feedback and is robust to noise.
result Elicits group-fair performance metrics for multiclass classification problems.
Proposes ICE-based metric for better understanding interactions in black-box models.
problem Misleading global sensitivity metrics in black-box models due to interaction effects.
method Individual Conditional Expectation (ICE) curves to compute feature importance and interactions.
result ICE-based metric provides richer insights into feature importance and interactions.
Bayesian approach improves AdaLoRA's performance and efficiency.
problem Improving the efficiency and performance of adaptive low-rank adaptation.
method Utilized Bayesian metrics and the Improved Variational Online Newton (IVON) optimizer for adaptive parameter budget allocation.
result Bayesian counterpart outperforms sensitivity-based importance metric and is faster than AdaLoRA.
New method makes quality metrics scale-invariant for high-dimensional data.
problem Scale sensitivity in quality metrics affects the accuracy of data projections.
method Analytical and empirical investigation of stress and KL divergence; introduction of a scale-invariant technique.
result The proposed technique accurately captures expected behavior and makes metrics scale-invariant.
Worst-Case Sensitivity measures model sensitivity to uncertainty set size.
problem Model sensitivity to uncertainty set size in Distributionally Robust Optimization.
method Introducing Worst-Case Sensitivity as a measure of model sensitivity, and deriving closed-form expressions for various uncertainty sets.
result DRO solutions can be sensitive to the family and size of the uncertainty set, and worst-case sensitivity reflects these properties.
Generatability in metric spaces studied with novel novelty parameters.
problem Understanding generatability in metric spaces with asymmetric novelty parameters.
method Introducing (ε,ε′)-closure dimension to characterize uniform and non-uniform generatability. result Generatability is stable across novelty scales in doubling spaces but can be highly scale-sensitive in general metric spaces.
Efficiently computes optimal policies for Entropic Risk Measures.
problem Optimizing risk-sensitive metrics in MDPs is computationally expensive.
method Uses Entropic Risk Measures and novel structural analysis for efficient computation.
result Achieves strong performance in various decision-making scenarios.
This paper tackles cost-sensitive portfolio optimization under ambiguous return distributions.
problem Tackles cost-sensitive distributionally robust log-optimal portfolio problem with ambiguous return distributions.
method Uses Wasserstein metric for distributional ambiguity, incorporates convex transaction costs, and approximates infinite-dimensional problem with finite convex program.
result Establishes conditions for robustly survivable trades and validates theoretical framework with empirical studies.
New algorithm tackles subgroup fairness in AI with multiple sensitive attributes.
problem Heavy computational burdens and data sparsity in subgroup fairness for multiple sensitive attributes.
method Doubly Regressing Adversarial learning (DRAF) for subgroup fairness, focusing on subgroups with sufficient sample sizes and marginal fairness.
result DRAF algorithm reduces a surrogate fairness gap for supIPM with less computation than directly reducing supIPM.
SenSeI ensures fair models by enforcing invariance on sensitive groups.
problem Ensuring fair machine learning models that respect sensitive groups.
method Designing a transport-based regularizer to enforce invariance on sensitive sets.
result Certifiably fair ML models trained using SenSeI achieve improved fairness metrics.
Transformers are less sensitive to input perturbations compared to other models.
problem Understanding the inductive biases of transformers and distinguishing them from other architectures.
method Identified token-wise sensitivity as a metric to explain transformers' inductive biases across different data modalities.
result Transformers have lower sensitivity than MLPs, CNNs, ConvMixers, and LSTMs, across vision and language tasks.
Fairness is becoming a rising concern w.r.t. machine learning model performance. Especially for sensitive fields such as criminal justice and loan decision, eliminating the prediction discrimination towards a certain group of population (characterized by sensitive features like race and gender) is important for enhanci…
Paper introduces differential pairwise privacy for secure metric learning.
problem Securely measuring similarities of individuals given sensitive pairwise data.
method Develops differential pairwise privacy (DPP) to protect sensitive pairwise data.
result Achieves pairwise data privacy without significant performance loss.
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered firs…
New framework improves differential privacy for asymmetric datasets.
problem Improving differential privacy for asymmetric datasets.
method Adapts inverse sensitivity mechanism with sparse vector technique.
result Efficiently estimates general functions with improved privacy.
New algorithms optimize metrics for binary classification with class imbalance.
problem Optimizing metrics like Fβ, AM, Jaccard for imbalanced classes.
method Reformulates metric optimization as cost-sensitive learning, using surrogate loss functions.
result METRO algorithms provide strong theoretical guarantees and outperform baselines.
Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation tim…
Semisupervised methods inevitably invoke some assumption that links the marginal distribution of the features to the regression function of the label. Most commonly, the cluster or manifold assumptions are used which imply that the regression function is smooth over high-density clusters or manifolds supporting the dat…
Study shows data attribution methods are sensitive to hyperparameters, making tuning costly.
problem Hyperparameter sensitivity in data attribution methods makes tuning impractical.
method Theoretical analysis and lightweight procedure for selecting regularization value without retraining.
result Proposes a lightweight procedure for selecting regularization value without model retraining.
Financial markets can be seen as complex systems that are constantly evolving and sensitive to external disturbance, such as systemic risks and economic instabilities. Analysis of resilient market performance, therefore, becomes useful for investors. From a systems perspective, this paper proposes a novel function-base…
Revisits conformal metrics with finite Q-curvature, providing necessary and sufficient conditions.
problem Understanding conformal metrics with finite total Q-curvature.
method Introduces conformal mass and provides necessary and sufficient conditions for normality.
result Derives volume comparison theorems and proves a positive mass type theorem related to Q-curvature.
Proposes a text perturbation method using a Mahalanobis metric to balance privacy and utility.
problem Low utility of text analysis when using spherical noise for privacy-preserving text embedding.
method Regularized Mahalanobis metric to add elliptical noise, accounting for embedding space density.
result Improves privacy statistics while maintaining utility, outperforming Laplace mechanism.
Optimizes portfolios by identifying causal drivers of diversification.
problem Achieving efficient portfolio optimization based on asset and diversification dynamics.
method Commonality Principle, Reichenbach Common Cause Principle, conformal maps, Bayesian networks, correlation-based algorithms, neural networks, SDEs.
result Optimal portfolio diversification achieved through causal methodologies and sensitivity forecasting.
The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes. The value of being sensitive to this geometry has been demo…
A new method for survival analysis models that ensures fairness without using sensitive demographic data.
problem Ensuring fairness in survival analysis models without relying on sensitive demographic information.
method A worst-case error minimization approach using a training loss function that does not know sensitive demographic information.
result The proposed method often scores better on fairness metrics without a significant drop in prediction accuracy compared to baselines.
Framework quantifies financial NLP robustness under regime shifts.
problem Semantic and causal drift in financial news narratives.
method Four metrics: FCAS, PCS, TSV, NLICS.
result Transformer models are more affected by semantic drift.
The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application. Evaluation metrics must ultimat…
Machine learning algorithms are extensively used to make increasingly more consequential decisions about people, so achieving optimal predictive performance can no longer be the only focus. A particularly important consideration is fairness with respect to race, gender, or any other sensitive attribute. This paper stud…
New metrics CWSA and CWSA+ improve model evaluation under confidence thresholds.
problem Lack of metrics capturing model reliability under confidence thresholds.
method Introducing CWSA and CWSA+ metrics that reward confident accuracy and penalize overconfident mistakes.
result CWSA and CWSA+ outperform classical metrics in trust-sensitive tests.
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose f…
A new robust metric compares distributions more accurately than existing methods.
problem Sensitivity to outliers and sampling discrepancy in Wasserstein distances.
method Introducing k-RPW, a partial p-Wasserstein distance.
result k-RPW converges faster to true distance and is more robust to outliers.
We study consistency of learning algorithms for a multi-class performance metric that is a non-decomposable function of the confusion matrix of a classifier and cannot be expressed as a sum of losses on individual data points; examples of such performance metrics include the macro F-measure popular in information retri…
Paper introduces a new performance metric for class imbalance datasets.
problem Challenges in selecting and comparing models for imbalanced datasets.
method Proposes a new performance measure based on the harmonic mean of Recall and Selectivity normalized in class labels.
result The proposed measure is less sensitive to changes in the majority class and more sensitive to changes in the minority class.
We construct several natural connections and Dirac type operators on a general metric contact manifold which are more sensitive to the geometric background. In the special case of CR manifolds these connections are also compatible with the CR structure and include among them the Webster connection. We also describe sev…
With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accura…
Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the {\em MT-net}, which enables the meta-learner…
Improves classifier evaluation by aligning with Total Classification Cost.
problem Lack of consensus on evaluation metrics and class imbalance issues.
method Introduces Weighted Accuracy (WA) and a reweighting framework for cost-sensitive scenarios.
result WA aligns with Total Classification Cost (TCC) minimization under realistic conditions.
Introduces FairCOCCO for fair learning with multitype, multivariate sensitive attributes.
problem Fairness in machine learning with multiple, complex sensitive attributes.
method FairCOCCO measure based on cross-covariance operators, incorporating a regularisation term.
result Consistent improvements in balancing fairness and predictive power on real-world datasets.
This article deals with the generalization performance of margin multi-category classifiers, when minimal learnability hypotheses are made. In that context, the derivation of a guaranteed risk is based on the handling of capacity measures belonging to three main families: Rademacher/Gaussian complexities, metric entrop…
We investigate the topics of sensitivity and robustness in feedforward and convolutional neural networks. Combining energy landscape techniques developed in computational chemistry with tools drawn from formal methods, we produce empirical evidence indicating that networks corresponding to lower-lying minima in the opt…
New metrics quantify implementation risk in portfolio backtesting, revealing systematic differences in engine implementations.
problem Systematic divergence in backtested portfolio metrics due to differences in engine implementations.
method Formalized implementation risk, proposed four metrics, executed 15 strategies through five engines, analyzed source-code defects.
result Implementation risk introduces measurable ambiguity in performance attribution, but does not alter investment decisions.
GS-WGAN sanitizes sensitive data for machine learning with improved privacy and model quality.
problem Lack of privacy in sensitive data hinders machine learning applications.
method Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN).
result GS-WGAN generates more informative samples and outperforms state-of-the-art approaches.
We are focusing on bound constrained global optimization problems, whose objective functions are computationally expensive black-box functions and have multiple local minima. The recently popular Metric Stochastic Response Surface (MSRS) algorithm proposed by \cite{Regis2007SRBF} based on adaptive or sequential learnin…
We explore an ensembled Σ-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps. The networks in Σ-net are trained in a supervised way, including content and GAN losses, and with various ways of data c…
This research tackles group fairness in predictive process monitoring by ensuring predictions are independent of sensitive group membership.
problem Predictive models using biased historical data can perpetuate unfair behavior in new cases.
method Investigates independence through metrics like ΔDP and a composite loss function balancing predictive performance and fairness.
result Proposes and validates a composite loss function for training models that balance fairness and performance.
Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification algorithms in various social contexts. Several recent works have focused on fairness with respect to a specific metric, modeled the correspo…