We analyze a new type of debt that rewards investors based on company performance.
problem Challenges in accounting and pricing equity-based debt obligations.
method Formulated and solved the associated mathematical problem in discrete and continuous time settings using FBSDE and decoupling fields.
result Solved the continuous time problem using FBSDE and decoupling fields.
Combines VaR and ES forecasts from a large pool of methods.
problem Combining forecasts from a large pool of VaR and ES methods.
method Adapted interval forecast combination methods, including trimmed means and mixtures approach.
result Trimmed mean combinations, mixtures method, and performance-based weighting delivered strong results.
Study uses machine learning to optimize seismic design parameters.
problem Optimizing seismic design parameters for performance-based design.
method Implementing explainable machine learning models to map design variables and performance metrics, integrated into a genetic optimization algorithm.
result Highly accurate surrogate models (R2> 90%) across diverse building types and hazards, identifying optimal member properties.
The Canonical Regression Quantile method predicts CEO compensation and future performance.
problem Determining fair CEO compensation and its impact on company performance.
method Canonical Regression Quantile method to assess CEO pay and performance.
result The method can predict future CEO performance and distinguish over/underpaid CEOs.
The paper simplifies calculus for semimartingales using multiplicative compensation.
problem Developing a formula for complex-valued semimartingales to simplify stochastic calculus.
method Multiplicative compensation for complex-valued semimartingales.
result The stochastic exponential of complex-valued semimartingales becomes a true martingale after compensation.
Machine learning improves PMD compensation in multiplexed systems.
problem Improving performance in multiplexed systems with PMD.
method Model-based machine learning parameterizing the Manakov-PMD equation.
result Performance close to PMD-free case achieved with hardware-friendly DBP and PMD compensation.
New multi-step approach improves fiber nonlinearity compensation efficiency.
problem Fewer steps are traditionally considered better for fiber nonlinearity compensation.
method Carefully designed multi-step machine learning approaches.
result Multi-step approaches lead to better performance-complexity trade-offs.
Model analyzes OTC market making with reputation feedback.
problem Optimizing electronic OTC liquidity provision considering reputation.
method Developed a stochastic-control model with feedback loops.
result Policy alternates between reputation-building and franchise monetization phases.
Model analyzes how reputation feedback affects OTC market making.
problem Understanding and optimizing OTC market making strategies.
method Developed a stochastic-control model with feedback loops.
result Policy alternates between reputation building and franchise monetization.
We propose and study the known-compensation multi-arm bandit (KCMAB) problem, where a system controller offers a set of arms to many short-term players for T steps. In each step, one short-term player arrives to the system. Upon arrival, the player aims to select an arm with the current best average reward and receiv…
Study incentivizes exploration in non-stationary MAB with compensation.
problem Incentivized exploration for non-stationary stochastic bandits with biased feedback.
method Proposed algorithms for abruptly-changing and continuously-changing non-stationary environments.
result Achieves sublinear regret and compensation over time.
New neural network improves MRI reconstruction for non-Cartesian data.
problem Improving MRI reconstruction for non-Cartesian data acquisitions.
method Density-compensated unrolled neural networks.
result Density-compensated unrolled neural networks outperform baselines.
Proposes a method to evaluate meta-learning performance based on task similarity.
problem Meta-learning performance evaluation ignores task similarity, leading to biased results.
method Generative approach using Latent Dirichlet Allocation to analyze task similarity.
result The proposed method provides an insightful evaluation of meta-learning algorithms, matching common intuition.
Unified framework for interpreting any predictive model.
problem Difficulty in comparing and understanding various model-agnostic interpretation techniques.
method SIPA (sampling, intervention, prediction, aggregation) framework
result Unified terminology and methodology for interpreting predictive models.
Proposes a new algorithm to reduce communication costs in decentralized training.
problem How to apply error-compensated compression to decentralized training.
method Error-compensated stochastic gradient descent for decentralized training.
result Proposed algorithm outperforms existing methods in communication cost reduction.
The paper analyzes fairness of compensation-based risk-sharing schemes for fund payouts.
problem Fair allocation of payouts in an endowment contingency fund.
method Analyzes two types of administrators and general non-negative loss distributions.
result General conditions for actuarial fairness are provided.
A machine learning model for PMD compensation in dual-polarization systems.
problem Compensating for polarization-mode dispersion (PMD) in dual-polarization systems.
method Model-based machine learning approach using the split-step Fourier method for the Manakov-PMD equation.
result The model converges to within 1% of peak dB performance after 428 iterations, achieving a 0.30 dB reduction in effective signal-to-noise ratio compared to PMD-free case.
We present a method to compensate statistical errors in the calculation of correlations on asynchronous time series. The method is based on the assumption of an underlying time series. We set up a model and apply it to financial data to examine the decrease of calculated correlations towards smaller return intervals (E…
Extends compactness theory to variable-coefficient pseudo-differential operators on manifolds.
problem Compensated compactness for pseudodifferential operators on vector bundles.
method Establishes a theorem for weakly convergent sequences of sections under a pseudo-differential operator.
result Quadratic form converges in distributional sense under certain conditions.
We introduce performance-based regularization (PBR), a new approach to addressing estimation risk in data-driven optimization, to mean-CVaR portfolio optimization. We assume the available log-return data is iid, and detail the approach for two cases: nonparametric and parametric (the log-return distribution belongs in …
A new IC-Connection improves disentanglement in conditional GANs.
problem Poor disentanglement of latent variables in conditional GANs.
method Information Compensation Connection (IC-Connection) for disentanglement.
result Our method achieves better disentanglement than state-of-the-art GANs.
DCSO dynamically selects top-performing base detectors for outlier ensembles.
problem Challenges in selecting and combining outlier scores from different detectors.
method DCSO dynamically selects top-performing base detectors based on local k-nearest neighbors.
result DCSO provides consistent performance improvement over static combination approaches.
We discuss the pricing of defaultable assets in an incomplete information model where the default time is given by a first hitting time of an unobservable process. We show that in a fairly general Markov setting, the indicator function of the default has an absolutely continuous compensator. Given this compensator we t…
Paper proposes E/PD-Control for better neural network training.
problem Training efficiency and robustness of CNNs in online data flows.
method E/PD-Control combines feedback PD controller with exponential signal.
result Better learning efficiency and robustness demonstrated experimentally.
This paper optimizes autonomous vehicle controllers using data-driven methods.
problem Designing robust controllers for autonomous vehicles that handle external and internal disturbances.
method Data-driven approach using principal component analysis and time delay neural networks.
result Improved controller performance through a feed-forward compensator.
Proposes DC-S3GD for efficient large-scale decentralized neural network training.
problem Training large-scale decentralized neural networks efficiently.
method Decentralized stale-synchronous version of DC-ASGD with gradient correction.
result Achieves state-of-the-art results in training Convolutional Neural Networks.
UDVD uses deep learning to denoise videos without supervision.
problem Lack of clean video data for training deep learning models.
method UDVD is a CNN trained solely on noisy video data, adapting to local motion.
result UDVD performs as well as supervised methods, even with limited training data.
FedSmart optimizes federated learning models for non-IID data.
problem Model performance on non-IID data is poor and privacy is at risk.
method FedSmart optimizes models by sharing global gradients and adjusting weights based on local validation set accuracy.
result FedSmart improves model performance by allocating more weight to similar data distributions.
We present two statistical causes for the distortion of correlations on high-frequency financial data. We demonstrate that the asynchrony of trades as well as the decimalization of stock prices has a large impact on the decline of the correlation coefficients towards smaller return intervals (Epps effect). These distor…
Extends weak continuity of Yang-Mills connections to a broader class.
problem Weak compactness of Ω-Yang-Mills connections. method Compensation compactness argument applied to Yang-Mills fields.
result Weak continuity result extended to Ω-Yang-Mills connections. A new method reduces communication in Federated Learning by pulling less often.
problem Reducing communication overhead in Federated Learning.
method Pulling Reduction with Local Compensation (PRLC) for SGD.
result PRLC achieves lower pulling frequency and maintains the same convergence rate as synchronous SGD.
A second order self-adjoint operator Δ=S∂2+U is uniquely defined by its principal symbol S and potential U if it acts on half-densities. We analyse the potential U as a compensating field (gauge field) in the sense that it compensates the action of coordinate transformations on the second derivatives in…
A new insurance and reinsurance pricing scheme based on realized loss.
problem Determining fair and risk-adjusted insurance premiums.
method Performance-based variable premium scheme with random initial premium adjusted based on realized loss.
result The variable premium scheme reduces reinsurer's total risk exposure compared to expected-value premium.
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based…
The study proves a theorem on Riemannian manifolds for wedge products of weakly convergent differential forms.
problem Analyzing the limiting behavior of wedge products of weakly convergent differential forms on Riemannian manifolds.
method Formulating and proving compensated compactness theorems for wedge products of differential forms on closed Riemannian manifolds.
result The theorem generalizes the div-curl lemma for vectorfields and applies to critical regularity exponents.
Integrated gradients are widely employed to evaluate the contribution of input features in classification models because it satisfies the axioms for attribution of prediction. This method, however, requires an appropriate baseline for reliable determination of the contributions. We propose a compensated integrated grad…
Proposes a compensation mechanism for improving individual forecast confidence.
problem Difficult to assess the quality of individual probabilistic forecasts and their utilities.
method Compensation mechanism based on fair bets and online learning.
result The proposed mechanism cannot be exploited and ensures forecasted utility matches actual utility.
In this paper the method of compensated compactness is applied to the problem of isometric immersion of a two dimensional Riemannian manifold with negative Gauss curvature into three dimensional Euclidean space. Previous applications of the method to this problem have required decay of order t−4 in the Gauss curva…
This paper explores how enforcing equivariance constraints limits neural network expressivity and proposes compensatory model size increases.
problem The impact of enforcing equivariance constraints on the expressive power of neural networks.
method Examined 2-layer ReLU networks, analyzed boundary hyperplanes and channel vectors, and constructed upper bounds on model size required for compensation.
result Enforcing equivariance constraints reduces the expressive power of neural networks, but this can be compensated by increasing model size.
A CNN-based method improves DTI of the human heart, compensating for motion.
problem Signal loss due to heart motion in DTI.
method Invertible Wavelet Scattering using CNN.
result Effective motion compensation and improved fiber structures.
Paper introduces non-linear discounting models for default compensation and climate valuation.
problem Valuation of non-replicable value and damage under default risk.
method Develops two models: one for risk-neutralising discounting and another for survival probability dependent discounting.
result Non-decaying discount factors (negative discount rates) are possible under certain scenarios.
We present a compensated compactness theorem in Banach spaces established recently, whose formulation is originally motivated by the weak rigidity problem for isometric immersions of manifolds with lower regularity. As a corollary, a geometrically intrinsic div-curl lemma for tensor fields on Riemannian manifolds is ob…
Survey on recent developments in isometric immersions using PDE techniques.
problem Analyzing isometric immersions with low Sobolev regularity.
method Compensated compactness and Coulomb-Uhlenbeck gauges.
result Weak continuity and stability of Gauss-Codazzi-Ricci equations.
The paper proves weak continuity of Cartan structural system on semi-Riemannian manifolds with lower regularity.
problem Weak continuity of the Cartan structural system on semi-Riemannian manifolds with lower regularity.
method Formulated and proved a geometric compensated compactness theorem, deduced Lp weak continuity of the Cartan structural system. result Weak continuity of the Cartan structural system and Gauss-Codazzi-Ricci system on semi-Riemannian manifolds with lower regularity.
Study proposes framework for cyber bonds to compensate cyber attack losses.
problem Cyber risk treatment in finance industry.
method Developed a framework, used publicly available data to determine loss distribution parameters, numerically simulated bond price and characteristics, considered two coupon calculation approaches.
result Numerical simulations of cyber bond price, yield, and characteristics.
Compensation methods correct overestimation of adversarial robustness in neural networks.
problem Overestimation of adversarial robustness using first-order attack methods.
method Proposed compensation methods address inaccurate gradient computation and reduce backpropagations.
result Empirical evaluation of adversarial robustness is improved with these methods.
Study optimal control strategy for hedge funds managers with PSAHARA utility family.
problem Optimizing risk and reward in incomplete markets with non-monotone risk aversion and convex compensation.
method Introduced PSAHARA utility family to model non-monotone risk aversion and convex compensation. Proved concavification techniques for non-concave utility functions. Derived explicit optimal control strategy.
result PSAHARA utility induces risk-taking behavior even with convex compensation, leading to high returns and volatility.
A machine learning surrogate model predicts earthquake-induced building responses.
problem Expensive FE model simulations for earthquake damage estimation.
method SVD-based earthquake characterization and machine learning model training.
result Deep neural network provides most accurate predictions of building responses.