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.
CollaQ improves multi-agent performance in StarCraft by 40% with fewer samples.
New approach for open ad hoc teamwork using graph-based policy learning.
Adapts agent strategies on-the-fly for better cross-play in cooperative settings.
Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agent…
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space. STEVE only relies on freely available information about the matches teams played in the past. These vectors can serve as…
AHEAD improves financial market efficiency through ad-hoc auctions.
AD-HOC simplifies high-order derivative calculations in C++.
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…
Paper presents Transfer Portal model for accurate player performance predictions.
Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence o…
SCQRNN prevents quantile crossing and improves computational efficiency.
A Python approach minimizes risk in decentralized exchanges.
Proposes a framework to explain KS deterioration in credit risk models.
FCDD improves image anomaly detection without post-hoc explainers.
Hierarchical MARL learns complementary skills for team coordination.
Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Furthermore, relying solely on the a…
Over the last few decades, the player recruitment process in professional football has evolved into a multi-billion industry and has thus become of vital importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and adv…
Quo Vadis team improves Traffic4cast competition with a seasonal bias model.
Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as lo…
First, we analyze the variance of the Cross Validation (CV)-based estimators used for estimating the performance of classification rules. Second, we propose a novel estimator to estimate this variance using the Influence Function (IF) approach that had been used previously very successfully to estimate the variance of …
Deep learning predicts M&A events in industry networks.
Study uncovers tactical line-breaking passes in football using clustering.
RL controls small soccer robots in a real league, beating human-designed policies.
Cyclification of orbifolds explained in cohesive higher topos theory.
Machine learning suffers from poor design, data, and evaluation practices.
Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variab…
CalArena benchmarks post-hoc calibration methods across various tasks.
Post-hoc calibration of neural networks using g-Layers proves theoretical justification.
Enhances functional classifier performance with new tree-based methods and unbiased feature importance assessment.
This short note contains an explicit proof of the Jacobi identity for variational Schouten bracket in -graded commutative setup. For the reasoning to be rigorous, it refers to the product bundle geometry of iterated variations (see arXiv:1312.1262 [math-ph]); no ad hoc regularizations occur anywhere in this theory…
Researchers create a framework to value player actions in CSGO.
Differential quantities, including normals, curvatures, principal directions, and associated matrices, play a fundamental role in geometric processing and physics-based modeling. Computing these differential quantities consistently on surface meshes is important and challenging, and some existing methods often produce …
In this paper, we employ machine learning techniques to analyze seventeen seasons (1999-2000 to 2015-2016) of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 26 predictor variables and one binary response variable taking on a value …
New algorithm for identifying Condorcet team in noisy comparisons.
Geometries and dual field theories linked by AdS/CFT.
The Epps effect helps distinguish between continuous and discrete financial tick data.
We consider normal almost contact structures on a Riemannian manifold and, through their associated sections of an ad-hoc twistor bundle, study their harmonicity, as sections or as maps. We rewrite these harmonicity equations in terms of the Riemann curvature tensor and find conditions relating the harmonicity of the a…
There are several (mathematical) reasons why Dupire's formula fails in the non-diffusion setting. And yet, in practice, ad-hoc preconditioning of the option data works reasonably well. In this note we attempt to explain why. In particular, we propose a regularization procedure of the option data so that Dupire's local …
Enhances deep neural networks with fixed-mean Gaussian processes for uncertainty estimation.
Framework analyzes physical metrics in soccer to link performance with value.
Attackers can significantly reduce team rewards in cooperative multi-agent reinforcement learning.
Paper studies competitive networks where teams aim to minimize their own objectives, adapting to each other's strategies.
According to theoretical models of valuing risky corporate securities, risk of default is primary component in overall yield spread. However, sizable empirical literature considers it otherwise by giving more importance to non-default risk factors. Current study empirically attempts to provide relative solution to this…
Clustering samples according to an effective metric and/or vector space representation is a challenging unsupervised learning task with a wide spectrum of applications. Among several clustering algorithms, k-means and its kernelized version have still a wide audience because of their conceptual simplicity and efficacy.…
OpenAI Five defeated Dota 2 champions using deep reinforcement learning.
Conversion of raw data into insights and knowledge requires substantial amounts of effort from data scientists. Despite breathtaking advances in Machine Learning (ML) and Artificial Intelligence (AI), data scientists still spend the majority of their effort in understanding and then preparing the raw data for ML/AI. Th…
Random Fourier features improve tabular deep learning convergence.
Proposes TgNN-LD to improve neural network effectiveness and efficiency.