arXiv research

Develops scenario theory for multi-criteria decision making.

Researchers validate ML scenario generators by checking dependencies and detecting memorization effects.

Optimal portfolio yields a digital option payoff.

Study uncovers new phase transitions in asymmetric causal inference scenarios.

Risk measures such as Expected Shortfall (ES) and Value-at-Risk (VaR) have been prominent in banking regulation and financial risk management. Motivated by practical considerations in the assessment and management of risks, including tractability, scenario relevance and robustness, we consider theoretical properties of…

Study optimal timing to divest from assets with uncertain future scenarios.

The paper improves robust optimization by introducing margin theory.

Enhances scenario approach for certifying design properties post-design.

Paper extends learning theory to dependent data with uniform risk bounds.

We develop a robust framework for pricing and hedging of derivative securities in discrete-time financial markets. We consider markets with both dynamically and statically traded assets and make minimal measurability assumptions. We obtain an abstract (pointwise) Fundamental Theorem of Asset Pricing and Pricing--Hedgin…

Investigates timing and asset allocation for life insurance in uncertain financial planning.

It has recently been found that Bell scenarios are only a small subclass of interesting setups for studying the non-classical features of quantum theory within spacetime. We find that it is possible to talk about classical correlations, quantum correlations and other kinds of correlations on any directed acyclic graph,…

Paper investigates OOD detection learnability under various conditions.

Bayesian optimization surveys information-theoretic acquisition functions.

Quantum field theory connects Riemannian geometry to quantum fluctuations.

Autonomous vehicles are expected to navigate in complex traffic scenarios with multiple surrounding vehicles. The correlations between road users vary over time, the degree of which, in theory, could be infinitely large, thus posing a great challenge in modeling and predicting the driving environment. In this paper, we…

Study shows LLMs can extrapolate rules from out-of-distribution prompts.

This paper uses SLT to ensure learning guarantees in CD detection.

Paper investigates learnability of OOD detection under various conditions.

Developing an AI economist agent using RAG, knowledge graphs, and LLMs for economic scenario analysis.

This paper improves causal inference using deep neural networks for low-dimensional covariates.

In three-dimensional computational topology, the theory of normal surfaces is a tool of great theoretical and practical significance. Although this theory typically leads to exponential time algorithms, very little is known about how these algorithms perform in "typical" scenarios, or how far the best known theoretical…

A new approach for green investing in Indian markets considers environmental factors.

Descending phase retrieval algorithms show a phase transition with increasing sample complexity.

Quantizes neural networks using frame theory for improved accuracy.

Risk assessment under different possible scenarios is a source of uncertainty that may lead to concerning financial losses. We address this issue, first, by adapting a robust framework to the class of spectral risk measures. Second, we propose a Deviation-based approach to quantify uncertainty. Furthermore, the theory …

Penalized likelihood approaches are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well-developed, the relative efficacy of different approaches in finite-sample settings, as encountered in practice, remains incompletely understood. There is theref…

This paper studies the trading volumes and wealth distribution of a novel agent-based model of an artificial financial market. In this model, heterogeneous agents, behaving according to the Von Neumann and Morgenstern utility theory, may mutually interact. A Tobin-like tax (TT) on successful investments and a flat tax …

Study shows algorithms benefit from limited target data with many source domains.

New theory extends LQ control to non-exponential discount scenarios.

Reviews six finance topics, including 'radical complexity'.

Paper proposes method for optimal control of unknown systems with latent states.

Estimates outcomes under hypothetical scenarios using a flexible framework.

We study the propagation of bosonic strings in singular target space-times. For describing this, we assume this target space to be the quotient of a smooth manifold $M$ by a singular foliation ${\cal F}$ on it. Using the technical tool of a gauge theory, we propose a smooth functional for this scenario, such that the p…

New definition of disentanglement for non-independent factors of variation.

In this paper we use game theory to model poisoning attack scenarios. We prove the non-existence of pure strategy Nash Equilibrium in the attacker and defender game. We then propose a mixed extension of our game model and an algorithm to approximate the Nash Equilibrium strategy for the defender. We then demonstrate th…

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…

To meet the Basel II regulatory requirements for the Advanced Measurement Approaches in operational risk, the bank's internal model should make use of the internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. One of the unresolved challeng…

New model for pairwise comparisons without stochastic transitivity.

We propose a unified methodology to input non-linear views from any number of users in fully general non-normal markets, and perform, among others, stress-testing, scenario analysis, and ranking allocation. We walk the reader through the theory and we detail an extremely efficient algorithm to easily implement this met…

Paper addresses eigenvector perturbation in small eigen-gap scenarios.

Study OOD generalization in meta-reinforcement learning using information theory.

Two new methods score stress test scenarios for risk managers.

The paper presents a framework to quantify the trade-off between synthetic and real data.

Gravitational interactions of higher spin fields are generically plagued by inconsistencies. We present a simple framework that couples higher spins to a broad class of gravitational backgrounds (including Ricci flat and Einstein) consistently at the classical level. The model is the simplest example of a Yang--Mills d…

SHARC explains machine learning risk models for regulatory capital, linking outputs to scenarios.

We analyze training dynamics in Gaussian mixture models using a comparison theorem.

New methods improve prediction intervals across multiple environments.