Deep neural networks improve margin assessment of breast tissue from OCT images.
problem Margin assessment of human breast tissue from OCT images.
method Used deep neural networks with function norm regularization.
result Significantly better results than other techniques, reducing EER from 12% to 5%.
Tall wheatgrass outperforms rye in energy and environmental metrics, marginally improving economic viability.
problem Finding sustainable alternatives for marginal agricultural areas.
method Economic assessment using profit margin and Life Cycle Assessment (LCA) for energy and environmental performance.
result Tall wheatgrass shows better environmental and energy performance with reduced inputs and lower energy consumption.
The paper improves itemset quality assessment by incorporating background knowledge.
problem Assessing the quality of discovered itemsets is challenging due to many patterns being explainable by background knowledge.
method The authors introduce a maximum entropy approach to efficiently infuse additional background knowledge such as row margins, lazarus counts, and bounds of ones.
result More sophisticated models that incorporate background knowledge fit the data better and improve frequency prediction of itemsets.
This paper assesses tail risk and systemic risk in cryptocurrencies using expectiles and MES.
problem Quantifying tail risk and systemic risk in cryptocurrencies.
method The study uses expectiles and Marginal Expected Shortfall (MES) to assess tail risk and systemic risk of cryptocurrencies.
result The expectile-based approach and MES provide a dynamic method to evaluate the impact of single assets on systemic risk.
Adopting a zonal structure of electricity market requires specification of zones' borders. In this paper we use social welfare as the measure to assess quality of various zonal divisions. The social welfare is calculated by Market Coupling algorithm. The analyzed divisions are found by the usage of extended Locational …
Max-margin method for eliciting preferences in large spaces.
problem Preference elicitation in large configuration spaces.
method Setwise max-margin learning as a generalization of max-margin learning to sets.
result Produces diverse sets of items for user queries and encourages sparsity.
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 evaluates margin parameter effects on knowledge embedding quality.
problem Understanding margin parameter's impact on embedding quality.
method Examined margin parameter values for multi-relational categorized data.
result Lower margin values are insufficient, while larger values cause noise.
Optimizes AIS hyperparameters for efficient marginal likelihood estimation.
problem Limited computation budget affects AIS performance.
method Flexible intermediary distributions defined by residual density, parameter sharing, and fix linear schedule.
result Optimized-Path AIS reduces sampling iterations and improves performance.
Paper assesses adversarial robustness of MCMC and BDK methods for deep Bayesian networks.
problem Assessing adversarial robustness of deep neural networks under MCMC and BDK approximations.
method Characterizes robustness of MCMC and BDK methods to FGSM and PGD attacks.
result Full MCMC-based inference shows excellent robustness, outperforming standard point estimation.
CPA framework assesses conditional validity of conformal prediction.
problem Challenges in evaluating conditional validity of conformal prediction.
method Reframes conditional coverage evaluation as a supervised learning task.
result Establishes convergence rates and proves CVI consistency.
New method bounds causal effects using local consistency of marginals.
problem Bounding causal effects due to unmeasured confounding.
method Enforces compatibility between marginals of causal models and data.
result Explicit algorithm and implementation of causal marginal polytope.
The study assesses external validity by evaluating worst-case treatment effects across subpopulations.
problem Underrepresentation of marginalized groups and limited study populations.
method Develops a semiparametrically efficient estimator for worst-case treatment effects (WTE) and uses cross-fitting to guard against brittle findings.
result The proposed framework guards against invalid findings due to unanticipated population shifts.
New method assesses individual training points' privacy risk without retraining.
problem Privacy vulnerability of individual training points in membership inference attacks.
method Derives a closed-form decomposition of individual black-box MIA vulnerability, extending to deep networks.
result Proposes a surrogate score operating on last-layer representations that requires only a single trained model.
DIET tests conditional independence using marginal dependence measures of residual information.
problem Computational intractability of conditional randomization tests (CRTs).
method DIET avoids fitting large models by leveraging marginal independence statistics of information residuals.
result DIET achieves higher power than other tractable CRTs on synthetic and real benchmarks.
Proposes CCE to assess point-wise reliability of neural network predictions.
problem Overconfidence and misaligned predictive distributions in neural networks.
method Introduces Conditional Congruence (CCE) metric using conditional kernel mean embeddings.
result CCE exhibits correctness, monotonicity, reliability, and robustness in high-dimensional regression tasks.
Paper introduces estimator response curve to assess mutual information estimators.
problem Assessing the performance of mutual information estimators.
method Utilizes estimator response curve to test various measures of association.
result Suboptimal estimators perform worse than optimal ones in real-world data.
The study introduces a holdout-based framework to assess synthetic data fidelity and privacy.
problem Evaluating the quality and privacy of synthetic data solutions for mixed-type tabular data.
method Holdout-based empirical assessment framework measuring fidelity and privacy risk.
result Synthetic data samples are as close to the training as to the holdout data, indicating generalization and independence from individual records.
Kernel tests assess equivalence between distributions without assuming specific moments.
problem Traditional goodness-of-fit tests fail to detect meaningful distributional differences.
method Proposes kernel-based tests using kernel Stein discrepancy and Maximum Mean Discrepancy.
result Tests assess the absence of meaningful distributional differences under controlled error rates.
The Surprise index assesses autonomous systems' competency in uncertain environments.
problem Evaluating competency of autonomous systems in dynamic, uncertain environments.
method Surprise index, a measure that quantifies system performance based on available data.
result The Surprise index can be computed for dynamic systems with Gaussian marginal distributions.
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.
Model assesses risks in CCP networks, identifying wrong-way risks.
problem Credit and liquidity risks in CCP networks.
method Developed a model to capture features of gap risk, feedback, and different participant risks.
result Identified wrong-way risks between clearing member defaults and market turbulence.
Monotonic improvement in uncertainty estimation with Gaussian processes as dimension increases.
problem Uncertainty quantification in machine learning models, especially with Gaussian processes, is challenging and poorly understood.
method Analyzing the behavior of marginal likelihood and cross-validation metrics as input dimension increases, and exploring the effects of cold posteriors.
result The marginal likelihood improves monotonically with input dimension, while cross-validation metrics exhibit double descent behavior.
Dynamic model assesses CCP risk with time-consistent risk measures.
problem Assessing central counterparty risk in dynamic markets.
method Markovian structure model of joint credit migrations; time-consistent dynamic risk measures.
result Proposes a method for more accurate initial margin and default fund allocation.
New estimator GMIPS reduces variance in ranking policy evaluation.
problem High variance in off-policy evaluation for ranking policies.
method GMIPS estimator with user behavior model on ranking embedding spaces.
result GMIPS achieves lowest MSE and balances bias-variance trade-off.
We present surrogate regret bounds for arbitrary surrogate losses in the context of binary classification with label-dependent costs. Such bounds relate a classifier's risk, assessed with respect to a surrogate loss, to its cost-sensitive classification risk. Two approaches to surrogate regret bounds are developed. The…
New tree-structured Markov fields with Poisson marginals for counting variables.
problem Counting variables with complex dependencies.
method Tree-structured Markov random fields with Poisson marginals.
result Straightforward sampling and joint probability calculations.
Improved forecasting of financial risk using Diffusion-Copula framework.
problem Capturing complex, asymmetric dependence structures in financial markets.
method Explicitly decouples marginal distribution learning from dependence structure using Mixture Density Networks and Classification-Diffusion Copula.
result Superior performance in forecasting systemic extremes of marginal and joint events.
Copula-based method generates synthetic populations from marginal distributions.
problem Generating realistic synthetic populations from limited data.
method Copula-based framework for population synthesis.
result Copula framework enhances transferability and realism of synthetic populations.
Develops methods for constructing likelihoods and priors for Bayesian networks.
problem Learning parameters and structure of Bayesian networks from limited data.
method Introduces assumptions for constructing likelihoods and priors from small assessments.
result Allows construction of likelihoods and priors for a wide range of network structures.
New methods improve robust decision-making under uncertainty in off-policy evaluation.
problem Statistical uncertainty and causal considerations in off-policy evaluation.
method Marginal Ratio (MR) estimator, Conformal Off-Policy Prediction (COPP), causal bounds.
result Improved robustness and uncertainty quantification in off-policy decision-making.
The Neural Testbed evaluates joint predictions of neural agents, revealing their limitations.
problem Evaluating the quality of joint predictions generated by neural agents.
method Developed an open-source benchmark (The Neural Testbed) to assess agents' marginal and joint predictions.
result Popular Bayesian deep learning agents perform poorly on joint predictions, even with accurate marginal predictions.
Estimates statistical shifts across subjects for EEG-based mental workload assessment.
problem Variability in EEG correlates of mental workload across subjects makes model generalization difficult.
method Proposes a strategy to estimate marginal and conditional shifts between multiple data distributions.
result Estimates of statistical shifts can improve mental workload prediction accuracy.
The paper explores how gradient descent trains associative memories, revealing oscillations and convergence issues.
problem Training dynamics of associative memories in overparameterized and underparameterized settings.
method Reduction to particle system dynamics, theory, and experiments.
result Oscillatory transitory regimes and benign loss spikes in overparameterized settings, suboptimal memorization in underparameterized settings.
A new backward Monte Carlo method reduces variance in exotic option pricing.
problem Pricing exotic options with high variance in local volatility models.
method Backward Monte Carlo algorithm using a discrete multinomial tree.
result Substantial variance reduction in exotic option pricing.
Proposes MvTPMSVM to improve multiview learning with reduced computational complexity.
problem Challenges in multiview learning, especially with heteroscedastic noise.
method Introduces MvTPMSVM, a parametric margin SVM model that avoids matrix inversions.
result Demonstrates superior generalization compared to baseline models.
A new test for independence using mutual information.
problem Testing independence between two multivariate random vectors.
method MINT test based on mutual information decomposition and efficient entropy estimators.
result The test has nominal size and provides local power analyses.
Paper applies quantization to polynomial processes for option pricing.
problem Quantization for polynomial processes in finance.
method Two quantization procedures for stochastic volatility Jacobi process.
result Theoretical and practical tools for fast option pricing.
Solves worst-case joint distribution problem for financial risk factors.
problem Finding worst-case joint distribution of risk factors given marginals and loss function.
method Uses linear programming to solve the problem when CVaR is the risk measure and distributions are discretized.
result Demonstrates method's applicability to various financial contexts, including counterparty credit risk.
A new method scores contextual Markov networks without assuming chordality.
problem Learning structure in contextual Markov networks is hard due to many possible structures.
method Marginal pseudo-likelihood as a consistent structure estimator.
result Marginal pseudo-likelihood yields a consistent structure estimator.
We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the indu…
Paper tackles high-dimensional bipartite ranking using PAC-Bayesian approach.
problem High-dimensional bipartite ranking problem.
method PAC-Bayesian approach with non-asymptotic risk bounds and oracle inequalities.
result Proves minimax optimality of the proposed scoring and ranking strategy.
EBBS integrates expert assessments into MIO best-subsets problem.
problem Identifying relevant features in statistical models.
method Expert-implied Bayesian approach using MIO.
result Reduces to classical best-subsets when experts are silent.
TAMW detects joint associations among low marginal effect genetic variants.
problem Detecting joint associations among low marginal effect genetic variants in complex diseases.
method Trees Assembling Mann Whitney (TAMW) approach for genome-wide analysis.
result TAMW outperforms other methods in detecting joint associations among LME genetic variants.
New insights on bias in multi-armed bandits under conditional sampling.
problem Understanding bias in multi-armed bandits under conditional sampling.
method Characterized the sign of conditional bias of monotone functions of rewards.
result Sign of conditional bias can differ from marginal bias, depending on conditioning events.
The paper proposes a method to test properties of the optimal assortment in multinomial logit models.
problem Uncertainty quantification for the optimal assortment in multinomial logit models.
method The paper proposes a novel inferential framework to test properties of the optimal assortment in multinomial logit models, reducing the problem to detecting the sign change point of marginal revenue gaps.
result The asymptotic normality of the marginal revenue gap estimator and the construction of a maximum statistic to detect the sign change point.
The study uses Gaussian mixture models to estimate pipe wall thickness from partial scans.
problem Estimating remaining wall thickness of critical water mains in non-destructive evaluation.
method Developed a robotic vehicle for pipe inspections. Used Gaussian Processes and Gaussian Mixture Models to infer RWT at unseen sections.
result Gaussian mixture models effectively capture the probability of RWT values in inspected data.
Semantic paraphrases can fool financial sentiment classifiers due to geometric shifts in model representations.
problem Semantic paraphrase attacks on financial sentiment classifiers
method Developing a continuous local model of semantic paraphrase perturbations
result The worst-case local displacement of the target representation is governed by the largest generalised eigenvalue of a matrix pencil constructed from the Jacobians of the two embedding maps.