arXiv research

Unified framework for Bayesian and Frequentist statistics.

We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predic…

Proposes a novel path generation and evaluation method for video games.

Paper resolves bias in ALFT training using generalized alignment games.

New framework recovers reward and rationality parameters from game behavior.

Develops a framework to estimate NBA player salary ROI.

New framework connects online learning to statistical learning for better generalization bounds.

In this paper, we study the problem of learning the set of pure strategy Nash equilibria and the exact structure of a continuous-action graphical game with quadratic payoffs by observing a small set of perturbed equilibria. A continuous-action graphical game can possibly have an uncountable set of Nash euqilibria. We p…

New algorithm finds near-optimal policies efficiently in zero-sum games.

GeoShapley uses game theory to measure spatial effects in ML models.

A brief review is given of the minority game, an idealized model stimulated by a market of speculative agents, and its complex many-body behaviour. Particular consideration is given to analytic results for the model rather than discussions of its relevance in real-world situations.

Proves minimax sample complexity for turn-based stochastic games.

In this paper I give a brief introduction to a family of simple but non-trivial models designed to increase our understanding of collective processes in markets, the so-called Minority Games, and their non-equilibrium statistical mathematical analysis. Since the most commonly studied members of this family define disor…

In this paper it was developed a modification of the known multiagent model Minority Game, designed to simulate the behavior of traders in financial markets and the resulting price dynamics on the abstract resource. The model was implemented in the form of software. The modified version of Minority Game was investigate…

A family of replicator-like dynamics, called the escort replicator equation, is constructed using information-geometric concepts and generalized information entropies and diverenges from statistical thermodynamics. Lyapunov functions and escort generalizations of basic concepts and constructions in evolutionary game th…

A new method uses Mean Field Games to optimize mixture models of Bernoulli and categorical distributions.

In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important tools to enhance fan experience, to evaluate in-game decision-making,…

Study on learning strategies in adaptive Markov games with policy regret as metric.

This thesis presents some geometric insights into three different types of two player prediction games -- namely general learning task, prediction with expert advice, and online convex optimization. These games differ in the nature of the opponent (stochastic, adversarial, or intermediate), the order of the players' mo…

A test measures artificial agents' human-like behavior in video games.

IGGP learns game rules from varying quality game play, finding no overall trend.

Study on liquidation games with market drop-out, proving unique equilibria.

This paper examines various definitions of adversarial risk and their implications.

Study online learning in unknown Markov games with sublinear regret.

RL in MFGs is as hard as solving many single-agent RL problems.

We analyze the relationships between game theory and quantum mechanics and the extensions to statistical physics and information theory. We use certain quantization relationships to assign quantum states to the strategies of a player. These quantum states are contained in a density operator which describes the new quan…

Method learns statistics of return distributions via neural networks and maximum mean discrepancy.

A game-theoretic approach selects features by testing their marginal contributions.

In this paper we study the problem of exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game from noisy observations of joint actions of the players alone. We consider sparse linear influence games --- a parametric class of graphical games with linear payoffs, and represented by directed gra…

Algorithm minimizes regret and converges to equilibria in Markov games.

Recent results and interpretations are presented for the thermal minority game, concentrating on deriving and justifying the fundamental stochastic differential equation for the microdynamics.

Financial markets are often driven by latent factors which traders cannot observe. Here, we address an algorithmic trading problem with collections of heterogeneous agents who aim to perform optimal execution or statistical arbitrage, where all agents filter the latent states of the world, and their trading actions hav…

Unified framework for feature-based explanations using ANOVA and game theory.

We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of prediction with expert advice, Hazan et al. (2016) established that in the optimization-oracle model, online learning requires exponentially …

Study uses complex networks and machine learning to predict soccer match outcomes.

This paper connects deep neural networks to game theory, revealing their congestion game properties.

Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suite…

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.

Framework for real-time win probability and player ability in sports.

Statistical physics method analyzes minority game dynamics in financial markets.

Study on multi-agent decision making complexity, showing sample efficiency gaps.

New bounds on learning from multiple distributions for VC classes.

Kernel-based mean-field games use MMD penalties for interaction and target costs.

A new MFG framework for evolving clusters from Gaussian mixtures.

New method attributes feature uncertainty in ML models using cooperative game theory.

Study improves $L^{\infty}$ estimates and extreme value behavior in stochastic differential games.

Olympic Games remain costly and overrun, despite reforms.

This work tackles learning Markov games with adversarial opponents and achieves both average reward and exploitation.