arXiv research

Paper proposes a copula method to generate unfavorable VaR scenarios.

Algorithm reduces historical expected shortfall computation by focusing on worst-case scenarios.

Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may take many expensive evaluations of the target distribution and its gradient to converge and mix. We propose neural transport (NeuTra) HMC, …

Optimal Volt/VAR control rules designed using deep learning.

Study optimizes portfolio to minimize relative drawdown duration, penalizing unfavorable performance states.

In a continuous-time model with multiple assets described by càdlàg processes, this paper characterizes superhedging prices, absence of arbitrage, and utility maximizing strategies, under general frictions that make execution prices arbitrarily unfavorable for high trading intensity. Such frictions induce a duality bet…

New framework shows algorithmic recourse can be harmful.

The authors discuss fairness in machine learning and introduce new methods.

AI algorithms outperform traditional trading methods in stock markets.

The study examines the limitations of bi-Lipschitz Normalizing Flows in approximating certain distributions.

Market inefficiencies persist in DEXes, especially during high volatility.

Adaptive optimizers improve federated learning performance.

New approach to meaningful and robust algorithmic recourse.

New findings show that multitask learning can improve with more tasks, but not without additional information.

Continuous-time Bayesian networks (CTBNs) constitute a general and powerful framework for modeling continuous-time stochastic processes on networks. This makes them particularly attractive for learning the directed structures among interacting entities. However, if the available data is incomplete, one needs to simulat…

Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations. The current practice for estimating the parameters of such models relies on local search heuristics (e.g., the EM algorithm) which are prone to failure, and existing consistent methods …

Hybrid model combines risk measures for better portfolio allocation.

We study a game-theoretic variant of the maximum circulation problem. In a flow allocation game, we are given a directed flow network. Each node is a rational agent and can strategically allocate any incoming flow to the outgoing edges. Given the strategy choices of all agents, a maximal circulation that adheres to the…

Research shows that information asymmetry affects how quickly companies adjust their capital structure and expected returns.

With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable data owners to privatize their data while keeping it …

Fair k-means algorithm ensures equitable costs for different groups.

Enhances Gaussian processes with spherical features for better scalability and flexibility.

Group-invariant neural networks improve approximation accuracy for symmetric functions.

SAIL-RevKL improves SAIL's convergence by regularizing the objective function.

New method improves counterfactual distribution learning for high-dimensional outcomes.

Optimal exit strategies of CPT gamblers in unfair gambles

New algorithms reduce regret in neural logistic bandits.

A new federated learning framework with sparsification and adaptive optimization for privacy and efficiency.

Analog arrays are a promising upcoming hardware technology with the potential to drastically speed up deep learning. Their main advantage is that they compute matrix-vector products in constant time, irrespective of the size of the matrix. However, early convolution layers in ConvNets map very unfavorably onto analog a…

A new GCN variant tackles large eigengaps in dense graphs and hypergraphs.

Neural network MCMC sampler maximizes proposal entropy for efficient sampling.

JAMPR learns to solve complex VRP with time windows.

Study improves portfolio optimization by reducing estimation errors and turnover.

A new graph neural network framework captures long-range interactions efficiently.

This article derives prognostic expressions for the evolution of globally aggregated economic wealth, productivity, inflation, technological change, innovation and growth. The approach is to treat civilization as an open, non-equilibrium thermodynamic system that dissipates energy and diffuses matter in order to sustai…

Much recent work has concerned sparse approximations to speed up the Gaussian process regression from the unfavorable O(n3) scaling in computational time to O(nm2). Thus far, work has concentrated on models with one covariance function. However, in many practical situations additive models with multiple covariance func…

Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speakers, called a specific scenario. While some works included experiments for different scenarios, research towards combining data of different…

New bounds adaptively control spectral complexity of trained Transformers.

The paper studies causal effects of multiple treatments in healthcare databases with rare outcomes.

Investigates JM for reducing downside risk in market regimes.

This paper introduces Libra to analyze and optimize generalization in Federated Learning.

Proposes a new framework for balancing average- and worst-case performance in machine learning.

Two new methods score stress test scenarios for risk managers.

Least squares estimator fails to achieve optimal risk in bounded distributions, but non-linear predictors can.

Paper tackles sample-efficient offline RL, proposing data diversity and unified algorithms.

The paper analyzes and proposes a new stopping criterion for recursive Bayesian classification.

Doubly-stochastic normalization improves robustness to heteroskedastic noise.

Extracts representative scenarios from large data panels.