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.
New approach for open ad hoc teamwork using graph-based policy learning.
Study on academic finance evolution over 30 years.
The paper offers algorithms for managing freelancers and in-house workers in online labor markets.
Paper studies competitive networks where teams aim to minimize their own objectives, adapting to each other's strategies.
Introduces LTQL for factored policies in cooperative MARL.
Investigates fairness in pipeline models where individuals may drop out.
Technology offers new ways to measure the locations of the players and of the ball in sports. This translates to the trajectories the ball takes on the field as a result of the tactics the team applies. The challenge professionals in soccer are facing is to take the reverse path: given the trajectories of the ball is i…
Proposes RPG-RT for red-teaming T2I models without internal access.
Paper presents Transfer Portal model for accurate player performance predictions.
Algorithm for decentralized competition among adaptive agents.
Proposes a nonparametric model for dynamic team rankings.
ContestTrade uses competitive teams to improve LLM trading performance.
Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills. As a step toward creating intelligent agents with this capability for fully cooperative multi-agent settings, we propose a two-level hierarchica…
This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.
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…
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…
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.
Framework for real-time win probability and player ability in sports.
Improved trajectory prediction for team sports using sparse outputs.
This paper analyzes how diffusion models learn and generalize concepts.
Attackers can significantly reduce team rewards in cooperative multi-agent reinforcement learning.
The paper explores the dynamics of composite symplectic Dehn twists with nonuniform hyperbolicity.
This article is motivated by soccer positional passing networks collected across multiple games. We refer to these data as replicated spatial passing networks---to accurately model such data it is necessary to take into account the spatial positions of the passer and receiver for each passing event. This spatial regist…
Human-AI teaming suffers from calibration issues.
An online labor platform faces an online learning problem in matching workers with jobs and using the performance on these jobs to create better future matches. This learning problem is complicated by the rise of complex tasks on these platforms, such as web development and product design, that require a team of worker…
CGAs estimate team performance from data, simplifying SV computation.
Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dy…
Model learns collective and individual dynamics in time series data.
Annealed Langevin dynamics improves sampling from composite scores in SBI.
The study uses unsupervised machine learning to identify top European football teams.
How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combin…
Dynamic paired comparison models, such as Elo and Glicko, are frequently used for sports prediction and ranking players or teams. We present an alternative dynamic paired comparison model which uses a Gaussian Process (GP) as a prior for the time dynamics rather than the Markovian dynamics usually assumed. In addition,…
Develops a deep learning architecture for rich-item recommendations.
Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a novel, end-to-end solution for coordinating an escort team for protecting high-value payloads. Our solution employs deep reinforcement learning …
Study shows how transformers learn to combine simple tasks into complex ones.
New method learns time-varying home field advantage in football.
Deep neural networks can approximate complex functions through repeated compositions of a fixed-size ReLU network.
Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause language to be compositional -- i.e., express meaning by combining words which themselves have meaning. Evolutionary linguists have found th…
BC-Aligner maintains backward compatibility of embeddings after frequent updates.
This review explores ML in predicting team sport outcomes, identifying successful strategies and themes.
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…
Study fair team formation in online labor marketplaces.
The study reveals simplicity bias in neural networks leading to better compositional mappings.
We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then trans…
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…
We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths of a team over time. This model was designed to be applied to sports such as soccer or hockey, in which contest outcomes take value in a limi…
A portfolio of different stocks and a risk-less security whose composition is dynamically maintained stable by trading shares at any time step leads to a growth of the capital with a nonrandom rate. This is the key for the theory of optimal-growth investment formulated by Kelly. In presence of transaction costs, the op…