This paper improves bond market making by adjusting hit-ratios for client flow quality.
problem Economic misleading of raw hit-ratios in corporate bond market making.
method Stochastic-control framework with residual-quality-adjusted hit-ratio.
result Optimal quotes decompose into various components, improving service/economics frontier.
The paper proposes using density ratio estimation to evaluate synthetic data quality.
problem Improving the quality and utility of synthetic data for analysis.
method Density ratio estimation to measure synthetic data quality.
result Density ratio estimation yields more accurate global utility estimates than existing methods.
The paper improves QD policy ensembles using distribution ratio estimators.
problem Training diverse and high-quality reinforcement learning agents.
method Using Stein variational gradient descent and distribution ratio estimators.
result The method generates diverse and high-quality reinforcement learning agents.
Seed selection affects Solvency II ratio, impacting insurance company stability.
problem Impact of RNG seed selection on Solvency II ratio stability.
method Detailed explanation of Solvency II ratio, theoretical background, and quality criteria for RNGs.
result True randomness is essential for stable Solvency II ratio results.
Tests Sharpe ratio for skill vs luck in asset management.
problem Accuracy of Sharpe ratio in measuring skill vs luck.
method Statistical tests to assess the significance of Sharpe ratios.
result Tests reveal the statistical significance of Sharpe ratios and their impact of auto-correlation.
Grapevine clusters wine reviews for personalized recommendations.
problem Providing personalized wine recommendations based on user preferences.
method Multi-dimensional clustering and unsupervised learning on wine reviews.
result Optimal wine recommendations based on user preference clusters and price-quality ratio.
Investments with best performance are not associated with best Sharpe ratios.
problem The relationship between performance and risk-adjusted return (Sharpe ratio) is counterintuitive for heavy-tailed distributions.
method Synthetic and real data analysis of returns distributions.
result The best-performing investments are not the best in terms of Sharpe ratio, and vice versa.
This paper improves monaural source enhancement using SDR as an objective function.
problem Maximizing signal-to-distortion ratio (SDR) for better monaural source enhancement.
method Uses signal-to-distortion ratio (SDR) as an objective function to improve monaural source enhancement.
result The proposed method achieved better performance than conventional methods.
We show that the driving force behind the regularizing effect of Laplacian smoothing on surface elements is the popular mean ratio quality measure. We use these insights to provide natural generalizations to polygons and polyhedra. The corresponding functions measuring the quality of meshes are easily seen to be convex…
Two algorithms improve K-means clustering speed without sacrificing quality.
problem Improving clustering quality of K-means while speeding up the process.
method Divisive K-means and Parallel Two-Phase K-means.
result Achieved empirically global optimum clustering results with lower complexity.
A new method improves density ratio estimation efficiency and accuracy.
problem Density ratio estimation trade-off between quality and efficiency.
method One-step Score-based Density Ratio Estimation (OS-DRE) combining analytic and solver-free approach.
result OS-DRE offers a favorable balance between estimation quality and inference efficiency.
Paper detects changes in graph-based data streams using likelihood-ratios.
problem Detecting changes in synchronized graph-based data streams.
method Kernel-based likelihood-ratio estimation over graph nodes.
result Effective detection and localization of change-points.
This work introduces an efficient method to sample high-quality images from conditional GANs.
problem Efficient subsampling of images from conditional GANs (cGANs) is challenging.
method Developed a novel conditional density ratio estimation method (cDRE-F-cSP) and rejection sampling scheme (cDR-RS).
result cDR-RS outperforms state-of-the-art methods in both effectiveness and efficiency.
New algorithm uses imperfect advice to improve online bipartite matching performance.
problem Online bipartite matching with imperfect advice.
method Designing an algorithm that uses external advice to improve performance between advice-free methods and optimal ratio.
result Algorithm achieves competitive ratio interpolating between advice-free methods and optimal ratio of 1.
Novel MCMC method tackles intractable likelihoods using learned ratio estimators.
problem Posterior inference with intractable likelihoods in complex simulations.
method Amortized approximate ratio estimator embedded in MCMC samplers.
result Effective approximation of likelihood-ratios for sampling from intractable posterior.
Hidden Markov models and their variants are the predominant sequential classification method in such domains as speech recognition, bioinformatics and natural language processing. Being generative rather than discriminative models, however, their classification performance is a drawback. In this paper we apply ideas fr…
EnhanceNet improves image quality through automated texture synthesis.
problem Traditional image super-resolution methods produce over-smoothed images.
method Automated texture synthesis in adversarial training setting.
result Achieves state-of-the-art results in image quality benchmarks.
A new method improves text generation quality and diversity.
problem Exposure bias in Maximum Likelihood Estimation for text generation.
method ψ-MLE, a new training scheme based on density ratio estimation.
result ψ-MLE outperforms Maximum Likelihood Estimation and other models in text generation quality and diversity.
New GAN training framework explains and improves model diversity.
problem Poor sample diversity in GANs.
method Interprets GAN training as density ratio estimation and divergence minimization. Derives new generator objectives targeting arbitrary f-divergences.
result Trained models achieve improved sample diversity.
Single model corrects JPEG artifacts for various compression settings.
problem JPEG compression artifacts due to aggressive quantization.
method Parameterized architecture using quantization matrix.
result State-of-the-art performance across different quality settings.
Optimizes audio codec selection with statistical guarantees.
problem Selecting the best audio encoding scheme for various data types.
method Supervised learning with uniform convergence theory.
result Rigorous statistical guarantees for codec selection.
REP-GAN improves GANs by reparameterizing proposals for better sample quality and efficiency.
problem Poor sample efficiency in GANs due to independent proposal sampling.
method REParameterizing Markov chains into the latent space of the generator to create dependent proposals.
result Empirically shows significant improvement in sample efficiency and quality.
Estimates density ratio for two-sample comparison using tree models.
problem Comparing two distributions given i.i.d. observations.
method Additive tree models with balancing loss for density ratio estimation.
result Bayesian inference provides uncertainty quantification for density ratio.
Neural network improves K-factor estimation for OFDM systems.
problem Estimating Ricean K factor for link quality in OFDM systems.
method Classified as a classification problem, neural network estimates K factor at transmitter side.
result High accuracy in K factor estimation with reduced feedback bandwidth.
Revises precision-recall curves for generative models.
problem Improves evaluation of generative models by distinguishing mode-collapse and quality issues.
method Generalizes PR curve formulation to arbitrary measures, exposes a bridge to error rates, proposes a new algorithm to approximate precision-recall curves.
result Demonstrates the interest of the new formulation over the original approach on multi-modal datasets.
The signal-noise ratio of a portfolio of p assets, its expected return divided by its risk, is couched as an estimation problem on the sphere. When the portfolio is built using noisy data, the expected value of the signal-noise ratio is bounded from above via a Cramer-Rao bound, for the case of Gaussian returns. The bo…
A new hybrid VAE-GAN framework improves mode coverage and quality.
problem Mode collapse and poor sample quality in GANs and VAEs.
method Integrates a 'Best-of-Many-Samples' reconstruction cost and a stable synthetic likelihood estimate.
result Significant improvement in mode coverage and quality compared to hybrid VAE-GANs and plain GANs.
GRAM improves generative models without saddlepoint optimization.
problem Training deep generative models with saddlepoint optimization is challenging.
method GRAM uses a fixed kernel as an adversary instead of a critic network.
result GRAM networks match and outperform adversarial training in generative quality.
QA-Token improves tokenization for noisy data, boosting model performance.
problem Tokenization ignores data quality, limiting model effectiveness on noisy corpora.
method QA-Token combines signal quality with vocabulary construction through bilevel optimization and reinforcement learning.
result QA-Token achieves state-of-the-art performance on genomic and financial datasets.
Spectrum selectively trains LLMs based on SNR to save resources.
problem Efficiently training large language models with limited resources.
method Targeting layer modules based on SNR for selective training.
result Spectrum achieves similar performance to full fine-tuning but with reduced VRAM usage.
FF algorithm uses goodness as a measure of input quality, derived from likelihood-ratio tests.
problem Training each layer locally with a goodness measure.
method FF algorithm uses a likelihood-ratio test to define goodness, which is the sum of squared activations normalized between layers.
result The goodness measure is a sufficient statistic for a likelihood-ratio test, explaining the FF algorithm's performance.
Improves diffusion models by controlling total variance and signal-to-noise-ratio.
problem Long sampling time in diffusion models.
method Total-Variance/Signal-to-Noise-Ratio (TV/SNR) disentangled framework.
result Improves generation performance by controlling TV and SNR independently.
BWS selects best window subsets for efficient data pruning.
problem Challenges in selecting subsets of large datasets for neural network training.
method Best Window Selection (BWS) by choosing optimal window intervals from ordered sample scores.
result BWS outperforms other methods across various selection ratios and datasets.
A method detects changes in heterogeneous data streams over graph nodes.
problem Detecting changes in data streams from nodes of a graph.
method Online non-parametric method using likelihood-ratio estimation.
result The method accurately identifies change-points in real-world applications.
Improved GAN training stability through clipping and reweighting.
problem Inconsistent GAN training leading to inferior performance.
method Proposes a variational GAN framework with probability ratio clipping and sample reweighting.
result Significantly improved performance across various GAN tasks.
Estimates treatment effect using ratio of potential outcomes in MS patients.
problem Estimating treatment-covariate interactions in observational studies.
method Proposes a doubly robust estimator for the ratio of expected potential outcomes.
result Validates the proposed estimator on an independent sample.
A new ML-based framework improves variational inference efficiency.
problem Efficient and accurate gradient estimation in variational inference.
method Multilevel Monte Carlo (MLMC) with reparameterized gradient estimators and adaptive learning rate.
result Our method achieves faster convergence and reduces gradient variance.
New Fourier-based diffusion model improves high-frequency generation quality.
problem Diffusion models struggle with high-frequency details.
method Analyzed and modified the forward process in Fourier space to equalize noise corruption across frequencies.
result Improved generation quality for high-frequency components.
Network analysis reveals regional banking clusters during financial crisis.
problem Understanding how financial institutions react to systemic crises.
method Extracting Accounting Network from financial statements, applying quality checks, community detection, PCA.
result Regional banking clusters emerge, with US and Japanese banks dominating, reflecting global practices.
Enhances GNNs by improving input data quality from topology and labels.
problem Poor quality of graph data limits GNN performance.
method Improves graph data quality using model outputs for better semi-supervised node classification.
result SEG consistently improves GNN performance across various datasets.
Generative AI improves stock selection by synthesizing features from diverse data sources.
problem Automating feature discovery in stock market data.
method Used large language models with retrieval-augmented generation and structured prompting to synthesize features from various data sources.
result AI-generated features consistently outperform baselines, with Sharpe improvements ranging from 14% to 91%.
This paper improves OMP-based sparse subspace clustering with data-adaptive capability.
problem Existing OMP-based approaches lack data adaptiveness, leading to inaccurate data representation.
method Develops a parameter selection process to adjust OMP parameters based on data distribution and introduces a new SEA ratio metric.
result Proposed approach achieves better clustering accuracy, SEA ratio, and representation quality compared to other OMP-based methods.
This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity Synthetic Apertur…
A multi-stage reinforcement learning method for object detection.
problem Efficiently detecting objects within images with high accuracy.
method Hierarchical tree-like region candidates, zoom and refinement stages, aspect ratio modification, multiple reward metrics.
result The multi-stage approach leads to more correct detections compared to single-stage methods.
Alignment of neural network representations is influenced by SNR and sample size.
problem Understanding how neural network representations align across different conditions.
method Controlled training of neural networks on perturbed datasets, analyzing alignment and generalization.
result Alignment varies monotonically with SNR but non-monotonically with sample size, with minimal alignment near the interpolation threshold.
New method compresses GANs by 1669:1 ratio on MNIST, 58:1 on CIFAR-10, 87:1 on Celeb-A.
problem Expensive GANs for low SWAP hardware and real-time applications.
method Knowledge distillation between a student and teacher GAN.
result Compressed GANs outperform standard training methods with a fixed parameter budget.
Corrects bias in learned generative models using likelihood-free importance weighting.
problem Bias in learned generative models relative to true data distribution.
method Estimate likelihood ratio using a classifier, apply importance weighting.
result Consistently improves goodness-of-fit metrics for deep generative models.
Deep neural network improves ultrasound imaging quality.
problem Rapid performance degradation of traditional beamformers in poor data acquisition conditions.
method Developed a deep learning-based beamformer.
result Significant performance gain in contrast-to-noise ratio and structural similarity for focused and planar imaging.