A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper analyzes fairness of compensation-based risk-sharing schemes for fund payouts.
Proposes a compensation mechanism for improving individual forecast confidence.
The excessive compensation packages of CEOs of U.S. corporations in recent years have brought to the foreground the issue of fairness in economics. The conventional wisdom is that the free market for labor, which determines the pay packages, cares only about efficiency and not fairness. We present an alternative theory…
This paper tackles fair online decision-making in contextual bandits, achieving optimal performance and fairness.
Formulates LGFO to measure fair ML systems using legal signals.
Q-learning methods represent a commonly used class of algorithms in reinforcement learning: they are generally efficient and simple, and can be combined readily with function approximators for deep reinforcement learning (RL). However, the behavior of Q-learning methods with function approximation is poorly understood,…
CFM fee income is insufficient to hedge market risk, study finds.
The Canonical Regression Quantile method predicts CEO compensation and future performance.
The paper simplifies calculus for semimartingales using multiplicative compensation.
The paper proposes multicalibration to improve matching in graphs with imperfect predictors.
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 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.
New neural network improves MRI reconstruction for non-Cartesian data.
The paper introduces Absolute Shapley Value to handle negative contributions in machine learning model training.
A machine learning model for PMD compensation in dual-polarization systems.
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…
This paper proposes a new method to fairly value data in federated learning.
Extends compactness theory to variable-coefficient pseudo-differential operators on manifolds.
Communication is a key bottleneck in distributed training. Recently, an \emph{error-compensated} compression technology was particularly designed for the \emph{centralized} learning and receives huge successes, by showing significant advantages over state-of-the-art compression based methods in saving the communication…
Data parallelism has become the de facto standard for training Deep Neural Network on multiple processing units. In this work we propose DC-S3GD, a decentralized (without Parameter Server) stale-synchronous version of the Delay-Compensated Asynchronous Stochastic Gradient Descent (DC-ASGD) algorithm. In our approach, w…
We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs hardware-friendly DBP and distributed PMD compensation with performance close to the PMD-free case.
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…
For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity trade-offs than their few-step counterparts.
Designing AI market for content creation
UDVD uses deep learning to denoise videos without supervision.
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…
A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on.It also needs to deal with the internal parametric variations of vehicle sub-syste…
Extends weak continuity of Yang-Mills connections to a broader class.
In recent years, unsupervised/weakly-supervised conditional generative adversarial networks (GANs) have achieved many successes on the task of modeling and generating data. However, one of their weaknesses lies in their poor ability to separate, or disentangle, the different factors that characterize the representation…
A second order self-adjoint operator is uniquely defined by its principal symbol and potential if it acts on half-densities. We analyse the potential as a compensating field (gauge field) in the sense that it compensates the action of coordinate transformations on the second derivatives in…
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.
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…
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 in the Gauss curva…
This paper explores how enforcing equivariance constraints limits neural network expressivity and proposes compensatory model size increases.
Two simple methods learn fair metrics from data to improve fairness in ML tasks.
New concept of within-group fairness improves AI fairness without sacrificing accuracy.
A new fairness metric for decision-making algorithms, conditioning on known fair variables.
Fair Mixup improves fairness in classifiers by interpolating between groups.
Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.
DFL framework improves action and outcome fairness in policy learning.
The paper connects counterfactual fairness to robust prediction and group fairness using causal context.
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…
Introduces principal fairness for fair decision-making.
New fairness notion helps identify fair auditors for evaluating decision-support systems.
Paper introduces non-linear discounting models for default compensation and climate valuation.
Federated Learning is a powerful machine learning paradigm to cooperatively train a global model with highly distributed data. A major bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) algorithm for large-scale Federated Learning is the communication overhead on pushing local gradients and …
The paper analyzes GMWB annuities in low interest rate environments.