Paper establishes a formula linking model performance to insurance loss ratio.
problem Improving model performance does not always lead to proportional improvements in loss ratio.
method Derives a closed-form formula connecting Pearson correlation to expected loss ratio.
result Model improvements have diminishing marginal returns in reducing loss ratio.
New binary loss functions improve density ratio estimation accuracy.
problem Improving accuracy of density ratio estimators using binary classifiers.
method Characterized loss functions based on prescribed error measures in Bregman divergences.
result Novel loss functions prioritize accurate estimation of large density ratio values.
We develop a new loss function for estimating quasiprobabilistic density ratios.
problem Discontinuous or non-surjective relationships between optimal classifiers and target densities.
method Introduce a convex loss function compatible with both probabilistic and quasiprobabilistic densities.
result Achieve state-of-the-art results in estimating di-Higgs production in particle physics.
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.
This paper explores ratio-based loss functions for machine learning.
problem Margin-based and distance-based loss functions for classification and regression.
method Investigation of ratio-based loss functions' properties.
result Proposed new ratio-based loss functions for regression.
The gain-loss ratio is known to enjoy very good properties from a normative point of view. As a confirmation, we show that the best market gain-loss ratio in the presence of a random endowment is an acceptability index and we provide its dual representation for general probability spaces. However, the gain-loss ratio w…
New α-divergence loss function improves neural density ratio estimation.
problem Optimization challenges in existing DRE methods, especially overfitting and high sample requirements.
method Derived α-divergence loss function (α-Div) for neural density ratio estimation. result The α-divergence loss function (α-Div) offers stable and effective optimization for DRE. Develops a new density ratio estimator for causal inference.
problem Estimation of density ratio functions in statistics.
method Super learning approach with a novel loss function.
result Empirical validation of the density ratio super learner's performance.
Paper develops estimators for unbounded density ratios with applications in error control.
problem Estimating density ratios with unbounded domains and ranges.
method Least squares and logistic regression loss functions for density ratio estimation.
result Established upper bounds on estimation errors with optimal rates for unbounded density ratios.
Study post-hoc Learning to Defer using density-ratio losses.
problem Optimizing decision-making between models and experts.
method Density-ratio losses for post-hoc L2D scorers, derived from class-probability estimation.
result The approach recovers known results and introduces new connections to expert comparison and anomaly detection.
In deep neural network, the cross-entropy loss function is commonly used for classification. Minimizing cross-entropy is equivalent to maximizing likelihood under assumptions of uniform feature and class distributions. It belongs to generative training criteria which does not directly discriminate correct class from co…
The paper defines fair profit sharing ratios in Islamic PL contracts.
problem Determining fair profit sharing ratios in Islamic PL contracts.
method Introduces c-fair profit sharing ratios and uses econometrics models to compute or approximate them. result Elucidates the relation between profit sharing ratios and economic factors.
The study examines how formal index insurance compares to informal risk sharing in managing natural disasters.
problem The challenges of natural disasters and the effectiveness of index insurance in risk management.
method A three-strategy evolutionary game model to analyze the competitive relationship between formal index insurance, informal risk sharing, and non-insurance.
result Basis risk and loss ratio significantly impact the adoption rate of index insurance, with different strategies preferred under varying conditions.
A new approach to model rejection using density ratios.
problem Improving model performance through selective prediction.
method Optimization of a loss's risk with φ-divergence regularization to find an idealized data distribution.
result Model rejection can be made by comparing the density ratio of the idealized distribution to the actual data distribution.
Study asset pricing with reference-dependent preferences, finding matching equity premia.
problem Understanding asset pricing under reference-dependent preferences.
method Discrete-time consumption-based capital asset pricing model with reference-dependent preferences.
result Models can generate equity premia matching empirical estimates, showing procyclical price-dividend ratio and countercyclical equity premium.
Model calculates capital requirements for multi-line insurance companies.
problem Measuring and capitalizing on incurred claims risk for multi-line property and casualty insurers.
method Stochastic model integrating accident semester, development lag effects, autocorrelation, and hierarchical copula.
result Model accurately reproduces empirical loss ratio dynamics and quantifies overall portfolio risk.
The paper analyzes the Rashomon ratio for infinite classifier families and shows its importance for choosing good classifiers.
problem Analyzing the Rashomon ratio for infinite classifier families.
method Quantifying the Rashomon ratio in two examples and providing guarantees for estimating it.
result A large Rashomon ratio guarantees choosing a classifier with good empirical accuracy will not significantly increase empirical loss.
The paper examines the unexpected losses and risk ratios for co-monotonic alternatives in large portfolios.
problem Understanding the unexpected losses and risk ratios for large portfolios with co-monotonic alternatives.
method Analyzes the asymptotic behavior of unexpected losses and risk ratios for co-monotonic alternatives using monotone cash-additive risk measures and Choquet insurance premia.
result Unexpected losses of large weighted portfolios are of order o(nλn), where λn is the average weight. Omega ratio, defined as the probability-weighted ratio of gains over losses at a given level of expected return, has been advocated as a better performance indicator compared to Sharpe and Sortino ratio as it depends on the full return distribution and hence encapsulates all information about risk and return. We comput…
This work characterizes the fundamental limit of network pruning using statistical dimension and convex geometry.
problem The fundamental limit of network pruning is still lacking, especially for deep neural networks.
method Directly imposing sparsity constraint on the loss function and using statistical dimension in convex geometry.
result Characterizes the sharp phase transition point as the fundamental limit of pruning ratio.
Filtering out unrealistic images from trained generative adversarial networks (GANs) has attracted considerable attention recently. Two density ratio based subsampling methods---Discriminator Rejection Sampling (DRS) and Metropolis-Hastings GAN (MH-GAN)---were recently proposed, and their effectiveness in improving GAN…
Neural networks approximate likelihood ratios for complex models.
problem Difficulty in computing likelihood ratios for modern models.
method Applying the likelihood ratio trick with neural network classifiers.
result Different neural network setups can approximate likelihood ratios with varying performance.
Adapts RKHS methods to estimate density ratios with optimal error.
problem Estimating density ratios from limited data.
method Minimizes regularized Bregman divergence in RKHS, with Lepskii type parameter choice.
result Adaptive minimax optimal error rate for quadratic loss.
Unified framework for estimating density ratios in causal inference.
problem Estimating density ratios for causal inference is challenging due to instability and curse of dimensionality.
method Bregman-Riesz regression unifies three methods: Bregman divergences, probabilistic classification, and Riesz loss.
result Unified framework improves density ratio estimation in causal inference.
Mirror descent linked to information ratio via Bayesian regret bounds.
problem Understanding stability in mirror descent and its relation to information ratio.
method Developed a connection between mirror descent and information ratio using Bayesian regret bounds.
result Mirror descent with suitable estimators and distributions achieves bounds similar to information-directed sampling.
Proposes a method to prevent overfitting in deep DRE models.
problem Overfitting in deep DRE models using empirical Bregman divergence.
method Introduces a non-negative correction for empirical Bregman divergence.
result The proposed method mitigates train-loss hacking and improves performance.
Novel neural likelihood ratio estimation for negative data in particle physics.
problem Estimating likelihood ratios with negative probability densities and weights.
method Introducing a novel loss function and a new model architecture based on signed mixture models.
result Demonstrated improved estimation on a real-world example from particle physics.
In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,.e.g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions. The traditional back propagation method cannot tr…
The VIX is used to enhance quantitative trading strategies.
problem Improving Sharpe ratio and reducing trading risks in quantitative strategies.
method Postprocessing quantitative strategies with VIX signals.
result Increased Sharpe ratio and reduced trading risks.
Study improves trading decisions by predicting profit and loss outcomes.
problem Inconsistent profitability of machine learning forecasts in financial markets.
method Developed a novel algorithm for forecasting profit and loss outcomes, integrating with market trend predictions.
result Significantly improved performance of trading strategies, including traditional and algorithmic trading.
The study evaluates forecast risk-adjusted performance using various metrics.
problem Evaluating forecast reliability beyond accuracy.
method Risk-adjusted performance measures (Sharpe, Sortino, Omega ratios) and Edge Ratio.
result Machine learning models often offer attractive risk profiles but not necessarily higher reliability.
New machine learning methods for inference from simulated data.
problem Modeling score and likelihood ratio functions from sampled data.
method InferoStatic Networks (ISN), Kernel Score Estimation (KSE), Kernel Likelihood Ratio Estimation (KLRE).
result Improved inference methods for complex models.
SIGTRON improves classification accuracy for imbalanced datasets.
problem Improving classification accuracy for imbalanced datasets.
method SIGTRON is a new sigmoid function with a convex loss function for imbalanced classification.
result SIGTRON models outperform existing methods in balanced and imbalanced datasets.
Gradient descent converges linearly in finite-width networks with positive NTK and compatible conditions.
problem Local convergence of gradient descent in finite-width networks.
method Positive Neural Tangent Kernel (NTK), local Polyak-Łojasiewicz inequality, fixed-step containment in Locally Quasi-Convex Region (LQCR).
result Linear convergence achieved under specific conditions.
New method for valid prediction sets in high-dimensional covariate shifts.
problem Valid prediction sets in high-dimensional covariate shifts.
method Likelihood-ratio regularized quantile regression (LR-QR) algorithm.
result LR-QR constructs valid prediction sets with desired coverage in target domain.
This work simplifies SVM parameter selection using S&S ratio.
problem SVM parameter tuning for optimal performance.
method S&S ratio to model SVM performance; automatic RP, kernel, and parameter selection.
result Optimized SVM parameters with reduced computational complexity.
Forecastability measures predictive information across horizons.
problem How much predictive information is available at each prediction horizon?
method Develops the consequences of mutual information between future observations and information set.
result Forecastability is a profile reflecting process dependence structure, with properties like compression and truncation error.
The paper optimizes stock portfolios with constraints based on performance attribution.
problem Optimizing stock portfolios with performance attribution constraints.
method Minimizes expected tail loss, constrains asset allocation and selection effect, tests on Dow Jones stocks.
result Imposing constraints on asset allocation and selection effect improves portfolio performance.
Unified framework for estimating density ratios across multiple distributions.
problem Binary density ratio estimation for multiple distributions.
method Unified framework based on Bregman divergence minimization.
result Generalization of binary DRE methods to multiple distributions.
Neural marked point processes show saturation with complexity, leading to new simple architectures.
problem Performance saturation in neural marked point processes with complex architectures.
method Proposed GCHP with graph convolutional layers and likelihood ratio loss.
result GCHP reduces training time and improves model performance.
Edgeworth Accountant calculates privacy loss under differential privacy compositions efficiently.
problem Efficiently computing overall privacy loss under composition of private algorithms.
method Analytical approach using f-differential privacy framework and Edgeworth expansion. result Non-asymptotic (ε,δ)-differential privacy bounds with reduced computational cost. A new loss function boosts AI's stock trading performance.
problem Improving AI's ability to predict stock prices and make profitable trades.
method Introducing a return-weighted loss function for deep learning models.
result Best models achieve high annual returns and Sharpe Ratios.
Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.
problem Determining which machine learning approach (classification vs. regression) is more effective for portfolio construction.
method Used stacking ensemble of gradient boosted tree, random forest, and neural network models.
result Classification yields higher Sharpe ratios and economically significant alphas compared to regression.
The paper optimizes forecasting for risk-adjusted decisions under trading frictions.
problem Optimizing forecasting accuracy for investment decisions in the presence of transaction costs.
method Develops a utility-weighted calibration criterion to minimize decision loss net of costs.
result Utility-weighted calibration reduces decision loss by over 30% and improves Sharpe ratio.
Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network compression through a variety of techniques such as pruning and quantization have been proposed to r…
In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting. A deep autoencoder network is trained to compress, quantize and reconstruct the bit log-likelihood ratios cor…
Optimizes PnL using linear signals in quantitative finance.
problem Maximizing profit and loss in financial trading.
method Unsupervised machine learning approach that maximizes Sharpe Ratio through linear relationships and parameter optimization.
result Empirical validation and effectiveness of the model on U.S. Treasury ETF.
Proposes a robust method for predicting missing outcomes in covariate shift adaptation.
problem Predicting missing outcomes in test data with covariate shift.
method Doubly robust estimator for covariate shift adaptation via importance weighting, incorporating an additional estimator for the regression function.
result Shows robustness against density-ratio estimation errors, maintaining consistency if either estimator is consistent.