The study models productivity growth and cost shares in Japan and Korea.
problem Estimating productivity growth and cost shares in multifactor CES models.
method Regression of cost shares on factor prices using linked input-output tables.
result Economy-wide propagation of productivity stimuli evaluated in a multi-sectoral model.
Develops multifactor approximations for SVEs with completely monotone kernels.
problem Approximating SVEs with kernels of completely monotone type.
method Multifactor approximation, Euler discretization, L2-estimation, convergence analysis. result New multifactor Euler scheme reduces computational cost and outperforms SVEs for option pricing.
State spaces of multifactor approximations of nonnegative Volterra processes are linear transformations of the nonnegative orthant.
problem Characterizing state spaces of multifactor approximations of nonnegative Volterra processes.
method Explicit linear transformation of the nonnegative orthant.
result State spaces of multifactor approximations of nonnegative Volterra processes are given by explicit linear transformation of the nonnegative orthant.
Paper prices geometric Asian options using a multifactor stochastic volatility model.
problem Pricing continuous geometric Asian options under multifactor stochastic volatility.
method Asymptotic expansion and perturbation techniques for both floating and fixed strike GAOs.
result Simplified pricing formulae for GAOs derived in a multifactor stochastic volatility framework.
The discrete-time multifactor Vasiček model is a tractable Gaussian spot rate model. Typically, two- or three-factor versions allow one to capture the dependence structure between yields with different times to maturity in an appropriate way. In practice, re-calibration of the model to the prevailing market conditions …
Develops multifactor risk models for equities using various factors.
problem Building robust risk models for equities using different factors.
method Constructs multifactor risk models via style factors, principal components, and industry factors. Uses the Russian-doll risk model for short horizons.
result Generalizes heterotic risk model to include arbitrary non-industry factors.
Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.
Study evaluates counterfactual explanations using Pearl's method.
problem Bias in counterfactual explanations generated from machine learning models.
method Evaluates counterfactual explanations using Judea Pearl's counterfactual method.
result Thirty percent of counterfactual explanations conflicted with Pearl's method.
Tamed Cross Entropy (TCE) loss outperforms standard CE loss in noisy classification tasks.
problem Improving classification performance in noisy data scenarios.
method Introducing Tamed Cross Entropy (TCE) loss, a derivative of Cross Entropy (CE) loss.
result TCE loss outperforms CE loss in all tested noisy classification scenarios.
Paper analyzes trade-offs in top-k classification accuracies and proposes a new loss function.
problem CE loss does not always optimize top-k prediction, especially with complex data.
method Introduces a novel top-k transition loss to improve top-k accuracy.
result Our loss function improves top-k accuracy, especially for k > 10.
CE-GAN improves deep learning for imbalanced data classification.
problem Difficulty in recognizing minority classes in imbalanced data.
method Class Expert Generative Adversarial Network (CE-GAN) architecture modification.
result CE-GAN provides better performance for imbalanced data classification.
Shrunk sample covariance matrix is a factor model of a special form combining some (typically, style) risk factor(s) and principal components with a (block-)diagonal factor covariance matrix. As such, shrinkage, which essentially inherits out-of-sample instabilities of the sample covariance matrix, is not an alternativ…
SIM-CE models C. elegans neural circuits for behavioral analysis.
problem Understanding the neural basis of C. elegans behavior.
method User-friendly Simulink platform with detailed neuron and synapse models.
result SIM-CE enables detailed multi-scale simulations of C. elegans behavior.
Stochastic momentum methods trade compute efficiency for serial runtime.
problem Stochastic momentum methods trade compute efficiency for serial runtime.
method Stochastic HB and ASGD for consistent linear regression with Gaussian covariates.
result HB preserves SGD-level CE over a larger batch-size window, allowing larger batches to reduce serial runtime until HB reaches its deterministic accelerated scale.
Develops a new method for benchmark portfolios and market outperformance strategies.
problem Creating effective benchmark portfolios for market outperformance.
method Explicit formulaic algorithm and multifactor risk model tailored for long-only portfolios.
result Explicit positive weights for benchmarks without principal components or iterations.
A new Bayesian perspective on counterfactual explanations improves model interpretability.
problem Improving interpretability of machine learning models through counterfactual explanations.
method Introducing a generalized Bayes perspective on counterfactual explanations, using a Gibbs posterior and distance-based priors.
result Counterfactual explanations are mathematically equivalent to the MAP estimate within the generalized Bayes framework.
New insights into CE dynamics reveal how Hadamard initialization simplifies softmax.
problem Understanding the dynamics of cross-entropy training loss in deep learning.
method Analyzing a two-layer linear neural network with standard-basis vectors as inputs.
result Gradient flow on cross-entropy converges to neural collapse geometry, proving global convergence.
CE improves climate uncertainty quantification using GCM ensembles and observational data.
problem Uncertainty in climate projections due to model inadequacies and variability.
method Conformal ensembles integrating GCM ensembles and observational data.
result CE generates statistically rigorous, easy-to-interpret uncertainty estimates.
Proposes a new method using Copula Entropy for variable selection.
problem Variable selection in machine learning and statistics.
method Copula Entropy (CE) based ranks for variable selection, model-free and tuning-free.
result CE based method selects variables more effectively and derives better interpretable results.
Privacy-preserving Bayesian inference framework for sensitive data.
problem Protecting sensitive information in Bayesian data analysis.
method Differential privacy framework for Variational Bayes, tailored to CE and non-CE models.
result Effective privatization of VB for CE models and improved privacy for non-CE models.
Study finds it hard to establish common factor pricing in corporate bonds.
problem Difficulty in establishing common factor pricing in corporate bonds.
method Portfolio- and bond-level analyses using multifactor models.
result Common factor pricing in corporate bonds is not significantly explanatory.
Our previous exploration of the $\cE_g^{PD}$-geometry has shown that the field is promising. Namely, the $\cE_g^{PD}$-approach is amenable to development of novel trends in relativistic and metric differential geometry and can particularly be effective in context of the Finslerian or Minkowskian Geometries. The main po…
Study shows ambiguity affects optimal timing in a two-dimensional model.
problem Understanding how ambiguity influences optimal timing in a two-dimensional setting.
method Analyzes a two-dimensional optimal stopping problem with ambiguity in a multifactor model.
result Ambiguity affects the rate at which the problem is discounted, not just the growth rate of underlying processes.
New method improves model calibration by adjusting confidence based on prediction correctness.
problem Improving model confidence alignment with true class probabilities.
method Post-hoc calibration objective using transformed samples for training.
result Competitive calibration performance on in-distribution and out-of-distribution test sets.
Estimates calibration error under label shift without labels.
problem Ensuring model reliability in the face of dataset shift without access to labels.
method Importance re-weighting of the labeled source distribution to estimate calibration error under label shift.
result Effective and reliable CE estimation with respect to the shifted target distribution.
The paper develops a method to optimize individualized treatment rules for cost-effectiveness.
problem Developing cost-effective individualized treatment rules for healthcare policy.
method Using conditional random forest and net-monetary-benefit (NMB) to estimate optimal CE-ITR.
result The approach optimizes healthcare resource allocation by maximizing health gains and minimizing costs.
We propose an extremely simple mathematical model that is shown to be able to account for more than 99 per cent of all the variation in economic and demographic macrodynamics of the world for almost two millennia of its history. This appears to suggest a novel approach to the formation of the general theory of social m…
The paper provides a uniform convergence bound for smooth calibration error and its relationship with functional gradient.
problem Limited theoretical understanding of learning algorithms achieving high accuracy and good calibration.
method Focuses on smooth calibration error, providing a uniform convergence bound and proving the relationship with functional gradient.
result Derives conditions for simultaneous classification and calibration guarantees in gradient boosting trees, kernel boosting, and neural networks.
GLOBE-CE offers efficient global counterfactual explanations.
problem Lack of reliable and scalable global counterfactual explanations.
method Translation-based approach for global counterfactual explanations.
result GLOBE-CE performs significantly better than current methods across multiple metrics.
Proposes CE-BASS for robust Kalman filtering with innovative and additive outliers.
problem Robustness to both innovative and additive outliers in Kalman filtering.
method Particle mixture Kalman filter with re-sampling of past states.
result CE-BASS efficiently handles multi-modality and trend changes in hidden state distributions.
Paper proposes a method to estimate Transfer Entropy using Copula Entropy.
problem Estimating Transfer Entropy for causal discovery.
method Non-parametric method based on Copula Entropy.
result The proposed method effectively infers causality relationships from data.
We develop high-order approximations for the Heston model.
problem Modeling the Heston model with high accuracy and efficiency.
method Combining approximation schemes on different random grids to achieve any order of convergence.
result Achieve any order of convergence for the Heston model.
En nous basant sur les résultats d'Arthur annoncés dans \cite[§30]{Arthur} nous démontrons les conjectures énoncées dans \cite{IMRN,BC,SMF} dans le cas des groupes orthogonaux à l'exclusion des groupes de type 6−D4. En ce qui concerne ces derniers, nous annonçons la démonstration -- encore en préparation - que …
Novel CE-method variants reduce local minima convergence with fewer function evaluations.
problem Local minima and expensive function evaluations in optimization.
method Surrogate model-based CE-method variants to reduce local minima convergence.
result Surrogate model-based approach reduces local minima convergence using fewer function evaluations.
Proposes a new loss function for learning with noisy labels.
problem Improving model learnability with noisy labels.
method Uses generalized Jensen-Shannon divergence as a noise-robust loss function.
result Shows state-of-the-art results on noisy data.
Paper proposes SL to improve DNN learning with noisy labels.
problem Learning with noisy labels in deep neural networks.
method Symmetric Cross Entropy (SL) with Reverse Cross Entropy (RCE).
result SL outperforms state-of-the-art methods on various datasets.
New bounds on geodesic dimension and curvature exponent in Carnot groups.
problem Characterizing geodesic dimension and curvature exponent in Carnot groups.
method Characterization and lower bound calculation for geodesic dimension and curvature exponent.
result Found an example where curvature exponent is greater than geodesic dimension.
Proposes sparse local and regional counterfactual rules for robust recourses.
problem Challenges in counterfactual explanations, especially stability, synthesis, and implementation.
method Probabilistic framework using Random Forest to derive sparse local and regional counterfactual rules.
result Effective recourses derived from high-density regions, providing sparse and robust counterfactual rules.
Formality theorem established for g-manifolds, relating cohomologies of polyvector fields.
problem Formality of g-manifolds and their cohomologies.
method Established an L-infinity quasi-isomorphism for g-manifolds, relating two dglas.
result Hochschild-Kostant-Rosenberg map is an isomorphism of Gerstenhaber algebras.
Motivated by the foliation by stable spheres with constant mean curvature constructed by Huisken-Yau, Metzger proved that every initial data set can be foliated by spheres with constant expansion (CE) if the manifold is asymptotically equal to the standard [t=0]-timeslice of the Schwarzschild solution. In this paper, w…
We review the properties of transversality of distributions with respect to submersions. This allows us to construct a convolution product for a large class of distributions on Lie groupoids. We get a unital involutive algebra $\cE\_{r,s}'(G,Ω^{1/2})$ enlarging the convolution algebra C∞_c(G,Ω1/2) associate…
Study finite-energy metrics over complex manifold degenerations.
problem Finite-energy metrics on complex manifolds with singularities.
method Investigate spaces of plurisubharmonic metrics with finite-energy conditions.
result Complete and geodesic metric structure on finite-energy metrics space.
Study the Hull-White model with volatility uncertainty, finding an arbitrage-free term structure.
problem Finding an arbitrage-free term structure in the Hull-White model with volatility uncertainty.
method Representing volatility uncertainty with sublinear expectation and G-Brownian motion; adjusting the model to find an arbitrage-free term structure.
result The resulting term structure is affine with respect to the short rate and the adjustment factor, consistent with the traditional Hull-White model after fitting the yield curve.
CONFETTI improves interpretability of deep learning models for MTS by providing counterfactual explanations.
problem Lack of transparency in deep learning models for multivariate time series classification.
method CONFETTI is a novel multi-objective counterfactual explanation method that balances prediction confidence, proximity, and sparsity.
result CONFETTI outperforms state-of-the-art methods in various metrics, improving interpretability and decision support.
This paper introduces DCE for better counterfactual explanations using optimal transport.
problem Lack of nuanced distributional characteristics in existing counterfactual explanations.
method Formulates a chance-constrained optimization problem using optimal transport to derive counterfactual distributions.
result DCE provides deeper insights into decision-making models by aligning counterfactual distributions with factual ones.
IAMs overestimate carbon prices due to flawed modeling of technology transitions.
problem IAMs use CES function to model technology transitions, leading to unrealistic cost curves.
method Propose using dynamically varying elasticity of substitution instead of CES.
result IAMs' monotonically increasing carbon cost is an artifact of modeling, not reality.
Proposes a new test for validating multivariate dynamic regression models.
problem Inadequate exogeneity conditions for conventional model specification tests in dynamic systems.
method Develops a generalized Durbin estimator for multiple-equation systems with dynamic dependencies, and constructs Wald tests.
result Bootstrap-based Wald tests improve finite-sample size control and validate the null hypothesis in multifactor models.
We construct a non-abelian extension Γ of S1 by $\cy 3 \times \cy 3$, and prove that Γ acts freely and smoothly on S5×S5. This gives new actions on S5×S5 for an infinite family $\cP$ of finite 3-groups. We also show that any finite odd order subgroup of the exceptional Lie group $G_…