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.
RL models improve target control in SSGs for security applications.
Paper tackles multiplayer symmetric games, securing equal share for n players.
A quantum financial approach to finite games of strategy is addressed, with an extension of Nash's theorem to the quantum financial setting, allowing for an entanglement of games of strategy with two-period financial allocation problems that are expressed in terms of: the consumption plans' optimization problem in pure…
The paper analyzes security issues in blockchain ecosystems with multiple SSPs and proposes two models for better stake management.
Paper tackles learning unknown game parameters from observations.
This work develops secure distributed algorithms for machine learning to protect against data poisoning and network attacks.
The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…
We start briefly surveying research on optimal stopping games since their introduction by E.B.Dynkin more than 40 years ago. Recent renewed interest to dynkin's games is due, in particular, to the study of Israeli (game) options introduced in 2000. We discuss the work on these options and related derivative securities …
Distributed Support Vector Machines (DSVM) have been developed to solve large-scale classification problems in networked systems with a large number of sensors and control units. However, the systems become more vulnerable as detection and defense are increasingly difficult and expensive. This work aims to develop secu…
Bayesian methods improve adversarial machine learning robustness.
Automatically finds effective security strategies through reinforcement learning and self-play.
This paper makes a small step towards a non-stochastic version of superhedging duality relations in the case of one traded security with a continuous price path. Namely, we prove the coincidence of game-theoretic and measure-theoretic expectation for lower semicontinuous positive functionals. We consider a new broad de…
The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent's set of st…
The paper introduces and studies hedging for game (Israeli) style extension of swing options considered as multiple exercise derivatives. Assuming that the underlying security can be traded without restrictions we derive a formula for valuation of multiple exercise options via classical hedging arguments. Introducing t…
The paper argues for clearer threat models in adversarial example research.
Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, w…
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to mi…
Deep RL enhances cyber security through adaptive, responsive, and scalable defenses.
Game contingent claims (GCCs) generalize American contingent claims by allowing the writer to recall the option as long as it is not exercised, at the price of paying some penalty. In incomplete markets, an appealing approach is to analyze GCCs like their European and American counterparts by solving option holder's an…
Study uses Mean Field Game to analyze Bitcoin mining hashpower dynamics.
This paper examines anomalies and frauds in blockchain networks and proposes detection techniques.
Survey of game theory methods to secure machine learning against adversarial attacks.
We study the origins of the effect in finance and SDE. In particular, we show, in the game-theoretic framework, that market volatility is a consequence of the absence of riskless opportunities for making money and that too high volatility is also incompatible with such opportunities. More precisely, riskles…
We derive formulas for the performance of capital assets in continuous time from an efficient market hypothesis, with no stochastic assumptions and no assumptions about the beliefs or preferences of investors. Our efficient market hypothesis says that a speculator with limited means cannot beat a particular index by a …
This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
Study on price formation among investors with exponential utility and liabilities.
This paper establishes a non-stochastic analogue of the celebrated result by Dubins and Schwarz about reduction of continuous martingales to Brownian motion via time change. We consider an idealized financial security with continuous price path, without making any stochastic assumptions. It is shown that typical price …
This paper uses a mean-field game to model stablecoin market dynamics and recovery.
Game theory improves smart road sign security against small perturbations.
We develop a fundamentally different stochastic dynamic programming model of trading costs. Built on a strong theoretical foundation, our model provides insights to market participants by splitting the overall move of the security price during the duration of an order into the Market Impact (price move caused by their …
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
Study shows Bitcoin security tied to mining rewards and prices.
A pair of points in a riemannian manifold makes a secure configuration if the totality of geodesics connecting them can be blocked by a finite set. The manifold is secure if every configuration is secure. We investigate the security of compact, locally symmetric spaces.
This paper explores security threats in ML systems and proposes mitigation techniques.
We say that a pair of points x and y is secure if there exist a finite set of blocking points such that any geodesic between x and y passes through one of the blocking points. The main point of this paper is to exhibit new examples of blocking phenomena both in the manifold and the billiard table setting. As an approac…
Paper proposes a recursive PLS model for optimal response to security threats.
Libra ensures fair order-matching in electronic financial exchanges.
Industry lacks tools to secure ML systems, study finds.
Sparse oblique decision tree improves security rules for renewable power systems.
The paper evaluates methods for explaining deep learning in security.
A riemannian manifold is secure if the geodesics between any pair of points in the manifold can be blocked by a finite number of point obstacles. Compact, flat manifolds are secure. A standing conjecture says that these are the only secure, compact riemannian manifolds. The conjecture claims, in particular, that a riem…
New model values equity-linked securities with guaranteed return.
Securely trains regression models with secret sharing for data collaboration.
Abstract: Cyber-security challenges tackled with machine learning.
Secure linear regression at speed of plaintext methods.
Paper proposes a secure protocol for federated learning.
This paper reviews ML and DL for IoT security, highlighting gaps and future directions.
Proposes a secure communication method independent of eavesdropper's decoder.