Study detects illegal discrimination by employers using correspondence experiments.
problem Detecting illegal discrimination by individual employers based on protected characteristics.
method Correspondence experiments, bounding higher moments of causal effects, decision rules for investigation.
result 85% of jobs contacting both white and black applicants are likely to discriminate.
Generative classifier derived from any discriminative classifier rejects illegal inputs.
problem Detecting and rejecting illegal inputs like adversarial examples and out-of-distribution samples.
method SDIM-logit: learns generative classifier from logits of any discriminative classifier, imposing statistical constraints.
result SDIM-logit inherits performance of base classifier without loss and can reject illegal inputs.
Predicting illegal fishing on Patagonian Shelf using machine learning.
problem Improving detection of illegal fishing to conserve fish stocks.
method Combining vessel tracking data with oceanographic seascapes and machine learning models.
result Machine learning models predict illegal fishing with 69-96% confidence.
The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detec…
Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock ma…
A multi-task network avoids indirect discrimination in insurance pricing.
problem Indirect discrimination in insurance pricing models based on protected characteristics.
method Multi-task neural network architecture trained with partial protected characteristic information.
result Multi-task network produces discrimination-free insurance prices with comparable accuracy to conventional models.
HW2MP-GAN tackles ancient handwritten text recognition.
problem Automatic text recognition from ancient handwritten records.
method Conditional Generative Adversarial Network (HW2MP-GAN) with Sliced Wasserstein distance and U-Net architectures.
result HW2MP-GAN outperforms state-of-the-art models in image-to-image translation and handwritten recognition.
Detects illegal stock market trading behaviors using graph ranking methods.
problem Detecting irregular trade behaviors in the stock market.
method Three graph Laplacian based semi-supervised ranking methods.
result Un-normalized and symmetric normalized graph Laplacian based methods outperform the random walk Laplacian method.
Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods. A recent new trend in the deep learning community is the application of multi-task models that enable single deep neural networks to perform more than one task at the same ti…
Insider trading is one of the numerous white collar crimes that can contribute to the instability of the economy. Traditionally, the detection of illegal insider trades has been a human-driven process. In this paper, we collect the insider tradings made available by the US Securities and Exchange Commissions (SEC) thro…
We note a simple mechanism that may at least partially resolve several outstanding economic puzzles, including why the cyclically adjusted price to earnings ratio of the S&P 500 index has been oddly high for the past two decades, why gains to capital have outpaced gains to wages, and the persistence of the equity premi…
RL models improve target control in SSGs for security applications.
problem Improving RL algorithms for target control in SSGs.
method Investigates improvements to target representations in RL algorithms.
result Enhanced RL models control targets better in SSGs.
Study identifies key health behaviors linked to adolescent suicide attempts.
problem Understanding health behaviors associated with increased risk of adolescent suicide attempts.
method Cross-sectional data analysis using machine learning algorithms and logistic regression.
result Non-parametric Bayesian tree ensemble model outperforms other models, achieving 80.0% accuracy in goodness-of-fit and 78.2% in predictive accuracy.
Research explores unsupervised methods for detecting vessel behavior changes in real-time data streams.
problem Detecting shifts in vessel behavior for maritime traffic monitoring.
method Investigates unsupervised and semi-supervised change detection methods.
result Identifies shifts in vessel behavior for unusual events detection.
Method debiases alternative data for fair credit underwriting.
problem Bias in alternative data affecting credit underwriting fairness.
method Causal inference applied to machine learning models.
result Improves model accuracy across racial groups without discrimination.
Gold and currency markets form a unique pair with specific interactions and dynamics. We focus on the efficiency ranking of gold markets with respect to the currency of purchase. By utilizing the Efficiency Index (EI) based on fractal dimension, approximate entropy and long-term memory on a wide portfolio of 142 gold p…
Market manipulation is a strategy used by traders to alter the price of financial securities. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools h…
The study quantifies and compares aleatoric and epistemic discrimination in ML models.
problem Sources of discrimination in ML models and their impact on performance.
method Quantifying aleatoric and epistemic discrimination using statistical experiments and model accuracy.
result State-of-the-art fairness interventions are effective at removing epistemic discrimination but not aleatoric discrimination in datasets with missing values.
The paper improves marine buoy placement to detect ships robustly against disruptions.
problem Detecting fishing vessels in the presence of natural and man-made disruptions.
method Formulated as a clustering problem, used dropout k-means and k-median to improve buoy placement robustness.
result Improved ship detection probability with dropout k-means compared to classic methods.
Blockchain disrupts corporate finance, but challenges remain.
problem Challenges in adopting blockchain for corporate finance.
method Exploring the impact of blockchain on corporate finance valuation and capital allocation.
result Blockchain offers new perspectives but faces regulatory, environmental, and legal challenges.
Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee f…
Discriminator guidance improves autoregressive diffusion models for generating molecular graphs.
problem Improving the accuracy of autoregressive diffusion models for generating molecular graphs.
method Deriving ways to use a discriminator with a pretrained generative model in the discrete case, including optimal and sub-optimal scenarios.
result Using a discriminator can correct pretrained models and improve exact sampling from the data distribution.
Solves GAN mode collapse by assigning minibatches to multiple discriminators.
problem Mode collapse in GANs where models generate similar samples.
method Multiple discriminators, microbatching, and changing tasks.
result Promotes sample diversity in generated sets.
Kernel discriminant analysis uses nonlinear embeddings to improve classification.
problem Limited effectiveness of linear discriminant analysis in capturing nonlinear features.
method Study of nonlinear embeddings in kernel discriminant analysis using polynomial and Gaussian kernels, solving generalized eigenvalue problems.
result Polynomial and Gaussian discriminants capture class differences through population moments and randomized projections.
Machine learning predicts criminal networks' missing partnerships and future behavior.
problem Predicting and understanding criminal networks' properties and future behavior.
method Combining graph representation learning and machine learning methods.
result Outstanding accuracy in predicting missing criminal partnerships and future behavior.
Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor one's heart activity at any time in any place is a crucial advantage of such techno…
Lower-dimensional video discriminators improve GAN performance.
problem High curvature in unconstrained video discriminator loss surfaces.
method Proposed Lower-Dimensional Video Discriminators (LDVD GANs).
result LDVD GANs double Temporal-GAN performance and achieve state-of-the-art performance.
Discriminative clustering uses mutual information to cluster data.
problem Clustering data into cohesive groups.
method Discriminative clustering using mutual information.
result Mutual information has been a cornerstone of discriminative clustering.
Pairwise discriminators stabilize adversarial training by ensuring the generator's alignment is preserved.
problem Stability issues in adversarial training when using sub-optimal discriminators.
method Introducing a family of objectives using pairwise discriminators, ensuring the generator's alignment is preserved.
result Only the generator needs to converge, and the alignment is preserved with any discriminator.
PBN combines generative and discriminative capabilities in a neural network.
problem Combining generative and discriminative capabilities in neural networks.
method Convolutional PBN, sharing FF-NN embodiment, combining generative and discriminative qualities.
result PBN shows excellent qualities from either generative or discriminative viewpoint.
Study on GANs under and overfitting using discriminator unseen data.
problem Understanding and mitigating under and overfitting in GANs.
method Using discriminators unseen by the generator to measure generalization.
result Model capacity of the discriminator affects generator quality; large discriminator capacities do not prevent overfitting.
Study infinite Euclidean distance discriminants of algebraic varieties.
problem Understanding the structure of data points with infinitely many critical points in Euclidean distance correspondence.
method Developed computer code to compute discriminants and proved properties of fibers.
result Infinite Euclidean distance discriminants contain all data points with infinitely many critical points for the nearest-point problem.
Discriminators can be good feature extractors despite their task focus.
problem Discriminators' features are often considered useless for downstream tasks.
method Theoretical analysis and feature space examination to understand discriminator's role.
result Discriminator features are robust and can prevent mode collapse, making them useful for transfer learning.
Optimized GAN discriminator using polyharmonic interpolation.
problem Optimizing the discriminator in GANs with higher-order gradient regularization.
method Polyharmonic interpolation and variational calculus.
result The optimal discriminator is a polyharmonic radial basis function.
Generalized dual discriminator GANs improve upon traditional GANs by using two discriminators and a flexible loss function.
problem Mode collapse in GANs.
method Introducing dual discriminator α-GANs and extending the approach to arbitrary functions. result The approach reduces the optimization problem to a linear combination of an f-divergence and a reverse f-divergence. GNNs improve graph signal discrimination by adding nonlinearities.
problem Improving graph signal discrimination in physical networks.
method Analyzing the discriminability of GNNs and their relation to graph filter banks.
result GNNs are at least as discriminative as linear graph filter banks.
We study two important concepts in adversarial deep learning---adversarial training and generative adversarial network (GAN). Adversarial training is the technique used to improve the robustness of discriminator by combining adversarial attacker and discriminator in the training phase. GAN is commonly used for image ge…
Paper proposes distributed sparse multicategory discriminant analysis for classification.
problem Sparse multicategory classification with distributed data.
method Convex formulation, distributed setting, invariant discriminant subspace recovery.
result Distributed sparse multicategory linear discriminant analysis performs as good as centralized version after a few rounds of communications.
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for trainin…
New method computes discriminative classifiers from generative models.
problem Discriminative vs generative classifiers are often seen as distinct, but this work shows they can be equivalent.
method General theoretical result showing generative classifiers can be computed discriminatively.
result Bayesian Maximum Posterior classifier from generative models matches discriminative classifier definition.
Proposes a curriculum-based dropout discriminator for domain adaptation.
problem Improving domain adaptation using deep learning networks trained on large labeled datasets.
method Introduces a curriculum-based dropout discriminator that gradually increases sample variance and uses reverse gradients to align source and target feature representations.
result The proposed model outperforms state-of-the-art results in domain adaptation tasks.
Proposes a sparse classifier for discriminative Gaussian Mixture Models.
problem Softmax-based discriminative models assume unimodality, leading to parameter redundancy.
method Sparse Bayesian learning for GMM-based discriminative model, reducing parameters and complexity.
result The SDGM outperforms existing softmax-based discriminative models.
Study detects spoofing in high-frequency trading using micro-structural analysis.
problem Challenges in detecting spoofing due to complex electronic platforms and high-frequency trading.
method Micro-structural study in a simplified setting, optimization of spoofing strategy, monitoring with Wasserstein distance.
result Optimal spoofing strategy and its impact on market imbalance quantified.
New findings show fixed-kernel discriminators are weaker than feature-learning ones.
problem Comparing performance of fixed-kernel and feature-learning discriminators.
method Using function classes F2 and F1, constructing pairs of distributions, and linking IPMs with sliced Wasserstein distances. result Fixed-kernel IPM and SD cannot discriminate certain distributions that feature-learning IPM and SD can.
Paper formalizes anti-discrimination law in automated systems.
problem Algorithmic discrimination in legal contexts.
method Decision-theoretic framework grounded in UK anti-discrimination law.
result Introduced 'conditional estimation parity' metric for ML fairness.
Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…
The paper addresses instability in KL divergence estimation using a neural network discriminator.
problem Unstable estimation of KL divergence due to discriminator complexity.
method Using a Reproducing Kernel Hilbert Space (RKHS) to control discriminator complexity.
result Theoretical bound on error probability of KL estimates based on discriminator complexity in RKHS.
We consider a pseudo-Riemannian metric that changes signature along a smooth curve on a surface, called the discriminant curve. The discriminant curve separates the surface locally into a Riemannian and a Lorentzian domain. We study the local behaviour and properties of geodesics at a point on the discriminant where th…