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 explores game-theoretic alignment of LLMs with human preferences, finding limitations and conditions.
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…
Efficiently audits model fairness with continuous monitoring and flexible data collection.
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…
Paper compares fairness measures and feature importance measures using SHAP.
Game-theoretic model captures investor interactions for stock price forecasting.
This work builds a hedging mechanism for experimental risk.
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
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…
This work examines the effects of allowing borrowing in betting-based hypothesis testing.
A game-theoretic framework identifies influential hyperparameters for neural networks.
This paper introduces a method to find complete and interpretable concept-based explanations for deep neural networks.
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 …
Paper proposes a game-theoretic approach to generate unlearnable examples.
Many generative models have to combat . The conventional wisdom to this end is by reducing through training a statistical distance (such as -divergence) between the generated distribution and provided data distribution. But this is more of a heuristic than a guarantee. The statistical distanc…
GT-DDP optimizer trains residual networks using game theory.
Proposes a game-theoretic approach for class-dependent rationalization.
Unified framework for Bayesian and Frequentist statistics.
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…
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…
Game-theoretic analysis of mining gaps in blockchain systems.
The paper compares LOCO and Shapley values for feature importance, highlighting their limitations and suggesting improvements.
LARP filters data to protect model performance across various learners.
New method for evaluating LLMs reduces bias in open-ended evaluations.
The paper improves dropout's utility by reducing interactions in deep neural networks.
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 …
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…
We solve a continuous-time game-theoretic problem for Kihlstrom-Mirman preferences.
The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.
Motivated by the recent applications of game-theoretical learning techniques to the design of distributed control systems, we study a class of control problems that can be formulated as potential games with continuous action sets, and we propose an actor-critic reinforcement learning algorithm that provably converges t…
End-to-end model predicts multiagent trajectories using game theory and neural nets.
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a ful…
A game-theoretic approach selects features by testing their marginal contributions.
Paper analyzes adversarial attacks and defenses using game theory.
For a monotonically advancing front, the arrival time is the time when the front reaches a given point. We show that it is twice differentiable everywhere with uniformly bounded second derivative. It is smooth away from the critical points where the equation is degenerate. We also show that the critical set has finite …
Proposes CoPO, a new policy optimization method for competitive games.
Decentralised optimisation tasks are important components of multi-agent systems. These tasks can be interpreted as n-player potential games: therefore game-theoretic learning algorithms can be used to solve decentralised optimisation tasks. Fictitious play is the canonical example of these algorithms. Nevertheless fic…
It is now well known that decentralised optimisation can be formulated as a potential game, and game-theoretical learning algorithms can be used to find an optimum. One of the most common learning techniques in game theory is fictitious play. However fictitious play is founded on an implicit assumption that opponents' …
Despite the notable successes in video games such as Atari 2600, current AI is yet to defeat human champions in the domain of real-time strategy (RTS) games. One of the reasons is that an RTS game is a multi-agent game, in which single-agent reinforcement learning methods cannot simply be applied because the environmen…
This paper gives yet another definition of game-theoretic probability in the context of continuous-time idealized financial markets. Without making any probabilistic assumptions (but assuming positive and continuous price paths), we obtain a simple expression for the equity premium and derive a version of the capital a…
This work presents a game-theoretic method for AVs that handles imperfect communication and individual rewards.
We investigate upper and lower hedging prices of multivariate contingent claims from the viewpoint of game-theoretic probability and submodularity. By considering a game between "Market" and "Investor" in discrete time, the pricing problem is reduced to a backward induction of an optimization over simplexes. For Europe…
Bayesian framework robustifies classifiers against malicious attacks.
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…
Unified framework for feature-based explanations using ANOVA and game theory.