Paper improves deep neural networks' generalization by focusing on margin distribution complexity.
problem Improving deep neural networks' generalization performance.
method Proves a generalization upper bound based on margin distribution statistics and optimizes a convex margin distribution loss function.
result Optimizing the ratio of margin standard deviation to expected margin enhances generalization performance.
Paper improves forest representation learning by optimizing margin distribution.
problem Improving generalization gap in forest representation learning.
method Reformulated as an additive model, optimizing margin distribution ratio λ.
result Substantially improved upper bound of generalization gap from O(√(ln m / m)) to O((ln m) / m).
MCD reformulates conditional density estimation into binary classification.
problem Conditional density estimation in statistical and machine learning.
method Marginal Contrastive Discrimination, reformulating into marginal and ratio density functions for binary classification.
result Significantly outperforms existing methods on most density models and regression datasets.
A new MCMC method tackles doubly intractable posterior problems.
problem Sampling from complicated distributions with doubly intractable posterior.
method Multi-armed Bandit MCMC (MABMC) algorithm.
result MABMC achieves higher average acceptance probability than existing methods.
New estimator reduces variance in off-policy evaluation for contextual bandits.
problem High variance in current OPE methods for contextual bandits.
method Marginal Density Ratio (MR) estimator focusing on marginal distribution shift.
result MR estimator reduces variance compared to IPW and DR methods.
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
problem Estimating ATT marginal hazard-ratio in externally controlled survival trials
method Machine-learning-assisted generalized entropy calibration for IPW Cox regression
result Reduces bias, increases efficiency, and improves coverage
Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty…
Proposes a model for clearing prices in financial markets due to margin calls.
problem Determining prices in financial markets following margin calls and short squeezes.
method Developed an explicit formulation for clearing prices after margin calls and short squeezes.
result Identified a threshold short interest ratio leading to discontinuity in clearing prices.
In order to protect brokers from customer defaults in a volatile market, an active margin system is proposed for the transactions of margin lending in China. The probability of negative return under the condition that collaterals are liquidated in a falling market is used to measure the risk associated with margin loan…
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 algorithm learns halfspaces with large margins efficiently.
problem Learning halfspaces with large margins in noisy data.
method Proper learning algorithm with tight complexity bounds.
result Nearly tight complexity characterization for α=1.01-approximate learning. Study introduces new financial ratios for better predicting company performance.
problem Lack of progress in predicting company performance and assessing financial risks.
method Developed new financial and macroeconomic ratios, supervised learning models, and Bayesian models.
result New proposed variables improve model accuracy and FNN performs best across multiple tasks.
Study shows how margin loan interest rates converge to a choke price, limiting long-term advantage in the broker call money market.
problem Long-term dynamics of margin loan interest rates and their impact on retail clients' advantage in the broker call money market.
method Analyzes the broker call money market dynamics, assuming perfect inelastic supply and continuous reinvestment, to show convergence of relative size and margin loan interest rates.
result Margin loan interest rates converge to a choke price, limiting the long-term advantage of retail clients over the market.
Paper connects rejection learning to Bhattacharyya divergence.
problem Learning models to abstain from predictions.
method Developed a link between rejection and thresholding different statistical divergences, focusing on Bhattacharyya divergence.
result Rejector obtained by joint ideal distribution corresponds to thresholding of skewed Bhattacharyya divergence.
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taki…
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.
Max-margin classifiers' behavior is studied in high dimensions with non-Gaussian features.
problem Understanding the role of featurization maps and high-dimensional misclassification error.
method High-dimensional asymptotics, Gaussian model, support vector representation.
result Asymptotic behavior of max-margin classifiers is determined by feature covariance and label covariance.
New method uses geometric mean to avoid non-collapsibility in case-control studies.
problem Non-collapsibility of odds ratio under outcome-dependent sampling.
method Proposes geometric mean aggregation to avoid non-collapsibility and provides estimation and inference methods.
result Geometric odds ratio is collapsible under outcome-dependent sampling.
Deep RL approach improves MIS for complex environments.
problem Improving off-policy evaluation for complex environments.
method Uses successor representation from deep RL to decouple reward and dynamics.
result Empirically stable and applicable to high-dimensional domains.
Efficiently estimates marginal posteriors for complex simulations.
problem Bayesian inference in high-dimensional, intractable likelihood scenarios.
method Simulates and estimates low-dimensional marginal posteriors, using truncated indicators.
result Simulator efficiency and robustness testing of inference results.
SIXO improves inference by learning smoothing distributions from all observations.
problem Inference limitations due to ignoring future observations in filtering distributions.
method Density ratio estimation to warp filtering distributions into smoothing distributions, then use SMC with learned targets.
result Proves tighter log marginal lower bounds and more accurate inferences and estimates.
Smart Bayes integrates generative and discriminative features for improved classification.
problem Improving classification performance by combining generative and discriminative modeling.
method Integrates generative likelihood-ratio features into a logistic-regression-style classifier.
result Often outperforms logistic regression and Naive Bayes in simulations and real data.
Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.
problem Maximizing returns while minimizing risk in portfolio optimization.
method Incorporates pre-assignment constraints and margin trading strategies using Genetic Algorithms and Particle Swarm Optimization.
result Metaheuristic-based portfolio optimization yields superior risk-adjusted returns compared to traditional methods.
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.
The study tests a functional-form restriction on risk exposure dynamics using margin debt data.
problem Understanding risk exposure dynamics under capital constraints and slack.
method Testing a regime-conditional functional-form restriction on aggregate risk-exposure dynamics implied by VaR-constrained intermediary models.
result The contraction and growth of exposures under capital constraints and slack are observed and tested.
Develops conformal Bayes for two-sided censored Gaussian regression under label shift.
problem Prediction under label shift with censored responses.
method Combines posterior predictive tilting with weighted conformal calibration.
result Restores marginal coverage with smaller prediction sets.
New offline RL method works with limited data and function approximators.
problem Sample efficiency with limited data and weak function approximators.
method Pessimistic algorithm based on version space formed by marginalized importance sampling (MIS), with gap assumption.
result Guarantees sample efficiency for simple algorithm under specific assumptions.
Margin trading and short selling boost green tech innovation in China.
problem Encouraging green technology innovation in Chinese companies.
method Quasi-experimental research using panel data of Chinese listed companies, double difference model.
result Margin trading and short selling increase green tech innovation significantly.
EviTrack improves sequential prediction in delayed disambiguation scenarios.
problem Challenges in sequential prediction with delayed disambiguation where early observations are ambiguous.
method EviTrack operates over latent trajectories, applying evidence- and likelihood-ratio-based selection to delay commitment until supported by data.
result EviTrack outperforms sampling-based baselines in a controlled synthetic benchmark, achieving faster post-disambiguation recovery.
Researchers quantify risk exposure and sensitivities in financial markets under model uncertainty.
problem Optimizing investment and pricing under model uncertainty in financial markets.
method Distributionally robust optimization, Wasserstein ball, first-order sensitivity analysis.
result Sensitivities of value function, investment policy, and marginal prices to model uncertainty can be non-monotonic.
Triangle fees adjust fees based on trade size and price movement, improving price accuracy and revenue.
problem Price staleness and low fee revenue in AMMs.
method Decreasing marginal fees proportional to price movement, creating incentives for price accuracy.
result Triangle fees strictly improve the Pareto frontier of price accuracy versus losses.
A new thompson sampling method controls for time-varying effects.
problem Dynamic experiments in online services with time-varying effects.
method Odds-ratio Thompson Sampling
result The proposed method works robust to time-varying effects.
Optimal margin loan agreements for sophisticated gamblers and brokers.
problem Finding fair interest rates and loan sizes between gamblers and brokers.
method Derives formulas for optimal arrangements based on gamblers' risk preferences and market conditions.
result Gambler gains higher capital growth with lower interest rates, broker gains intermediary profit.
New study reveals a polynomial penalty for adapting to unknown margin parameters in batched nonparametric bandits.
problem Adapting to an unknown margin parameter in batched nonparametric bandits.
method Introduces the regret inflation criterion and develops RoBIN algorithm to achieve optimal regret inflation.
result The optimal regret inflation grows polynomially with the horizon T, characterized by a convex optimization problem.
New method for efficient inference over complex parameter spaces.
problem Challenges in Bayesian inference for high-dimensional, intractable likelihoods.
method Arbitrary Marginal Neural Ratio Estimation (AMNRE) for simulation-based inference.
result Efficient inference over arbitrary subsets of parameters without numerical integration.
Prognostic scores improve logistic regression analysis in RCTs with binary outcomes.
problem Non-collapsibility in logistic regression analysis of RCTs with binary endpoints.
method Prognostic score adjustment using AI predictions to address non-collapsibility.
result Prognostic score adjustment increases power or reduces sample size for estimating conditional odds ratios.
Attention models can overfit without harming test performance.
problem Understanding benign overfitting in single-head attention models.
method Analyzing conditions for benign overfitting in a single-head softmax attention model.
result A single-head attention model can overfit without harming test performance under certain conditions.
Paper analyzes U.S. broker call rate laws of motion and their implications.
problem Understanding the dynamics and pricing of margin loans in the U.S. market.
method Analysis of monthly observations, derivation of stochastic differential equations, application of arbitrage theory.
result Margin loan interest rate follows mean-reverting behavior, with total call loan volume constituting over 70% of leveraged portfolios.
Derives asymptotic generalization error for large-margin classifiers.
problem Understanding the generalization error of large-margin classifiers.
method Statistical physics replica method for deriving asymptotic expression.
result Establishes phase transition boundary for class separability.
Paper proposes transparent insurance models for PBMs.
problem PBMs' opaque business models and hidden profits.
method Quantitative estimates of two models with fixed premiums and fee-for-service.
result Proposes transparent models with fixed premiums and fee-for-service.
RNE provides a flexible framework for diffusion models, enabling inference-time control and energy-based training.
problem Insufficient knowledge of marginal densities in diffusion models.
method Introduces Radon-Nikodym Estimator (RNE) to reveal the connection between marginal densities and transition kernels.
result RNE delivers strong results in inference-time control and energy-based diffusion training.
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 (constrained) minimization of a ratio of set functions is a problem frequently occurring in clustering and community detection. As these optimization problems are typically NP-hard, one uses convex or spectral relaxations in practice. While these relaxations can be solved globally optimally, they are often too loos…
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.
Algorithm improves SVM classification in non-Euclidean spaces.
problem Limitations of traditional SVM in non-Euclidean spaces.
method Covariance-adjusted SVM using Cholesky Decomposition.
result Cholesky-SVM outperforms traditional SVM in non-Euclidean spaces.
A new copula estimation method using classification.
problem Estimating copula density from joint and marginal distributions.
method Train a classifier to distinguish joint density from product of marginals.
result Empirically outperforms existing copula estimators.
Improved resource allocation method reduces procurement costs.
problem Online resource allocation with procurement costs.
method Primal-dual algorithm with surrogate function optimization.
result Enhanced competitive ratio through design methods.
This paper simplifies hedge ratios in financial models using pathwise algorithmic differentiation.
problem Expensive and unstable computation of hedge ratios from pathwise sensitivities.
method Develops reduced stochastic hedge ratios of the form φ_j^r = Σ_j^r ξ_j^q X_q, retaining sensitivity tensor through empirical averages.
result Two coefficient criteria are introduced to minimize pathwise residuals and satisfy moment equations.