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.
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
The paper explores game-theoretic alignment of LLMs with human preferences, finding limitations and conditions.
This paper monetizes customer load data to boost energy retailer profits.
A game-theoretic framework identifies influential hyperparameters for neural networks.
Game-theoretic models predict asset prices in financial markets.
In this expository paper we illustrate the generality of game theoretic probability protocols of Shafer and Vovk (2001) in finite-horizon discrete games. By restricting ourselves to finite-horizon discrete games, we can explicitly describe how discrete distributions with finite support and the discrete pricing formulas…
Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theore…
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or c…
Model predicts BESS interactions and price impacts in energy markets.
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…
This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting c…
A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incremental…
GT-DDP optimizer trains residual networks using game theory.
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…
Paper compares fairness measures and feature importance measures using SHAP.
Game-theoretic model captures investor interactions for stock price forecasting.
Paper analyzes adversarial attacks and defenses using game theory.
We consider the game-theoretic scenario of testing the performance of Forecaster by Sceptic who gambles against the forecasts. Sceptic's current capital is interpreted as the amount of evidence he has found against Forecaster. Reporting the maximum of Sceptic's capital so far exaggerates the evidence. We characterize t…
We study optimal behavior of energy producers under a CO_2 emission abatement program. We focus on a two-player discrete-time model where each producer is sequentially optimizing her emission and production schedules. The game-theoretic aspect is captured through a reduced-form price-impact model for the CO_2 allowance…
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…
This work develops secure distributed algorithms for machine learning to protect against data poisoning and network attacks.
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…
Proposes a game-theoretic framework for ML trust regulation.
This work shows how evaluation metrics can be seen as fair gambles.
A game-theoretic approach to multi-criteria ranking from ordinal data.
Efficiently audits model fairness with continuous monitoring and flexible data collection.
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…
We propose a betting strategy based on Bayesian logistic regression modeling for the probability forecasting game in the framework of game-theoretic probability by Shafer and Vovk (2001). We prove some results concerning the strong law of large numbers in the probability forecasting game with side information based on …
A game-theoretic approach simplifies MBRL design and improves sample efficiency.
In this article we consider a game theoretic approach to the Risk-Sensitive Benchmarked Asset Management problem (RSBAM) of Davis and Lleo \cite{DL}. In particular, we consider a stochastic differential game between two players, namely, the investor who has a power utility while the second player represents the market …
New model considers wealth and time affecting risk aversion in portfolio selection.
Paper proposes a game-theoretic approach to generate unlearnable examples.
We study multistep Bayesian betting strategies in coin-tossing games in the framework of game-theoretic probability of Shafer and Vovk (2001). We show that by a countable mixture of these strategies, a gambler or an investor can exploit arbitrary patterns of deviations of nature's moves from independent Bernoulli trial…
The game-theoretic risk management framework put forth in the precursor work "Towards a Theory of Games with Payoffs that are Probability-Distributions" (arXiv:1506.07368 [q-fin.EC]) is herein extended by algorithmic details on how to compute equilibria in games where the payoffs are probability distributions. Our appr…
Proposes a game-theoretic approach for class-dependent rationalization.
CityTFT models urban building energy using a data-driven approach.
Paper explores sustainable machine learning with energy harvesting.
Investment decisions shift earlier as patience decreases, with implications for pasting conditions.
Derive Dirichlet scalar curvature energy functional variation formula
Study solves HJB equations for time-inconsistent control problems.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
Modeling European spot power markets with game theory for Nash equilibria.
Bayesian model for energy-efficient EV navigation.
The paper analyzes optimal stock position-building strategies in competitive markets.
Unified framework for feature-based explanations using ANOVA and game theory.
FEAT estimates free energy using adaptive transports.
Game-theoretic analysis of mining gaps in blockchain systems.