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.
Proposes RPG-RT for red-teaming T2I models without internal access.
New framework assesses LLM security risks in BFSI.
We use SMC with twist functions to improve probabilistic inference in LLMs.
Simple online monitor detects unsafe LLM outputs.
Study of red blood cells using elastic surface theory.
We develop a real-time anomaly detection algorithm for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent factors in addition to sender- and receiver-specific popularity scores; deviation…
This paper presents the recurrent estimation of distributions (RED) for modeling real-valued data in a semiparametric fashion. RED models make two novel uses of recurrent neural networks (RNNs) for density estimation of general real-valued data. First, RNNs are used to transform input covariates into a latent space to …
ReD improves LLM inference efficiency at fixed budget, reducing attempts and cost.
Generative ML learns optimal pursuit trajectories in pursuit-evasion games.
Diffusion LLMs can efficiently generate harmful prompts for adversarial testing.
RED CoMETS improves multivariate time series classification accuracy.
Mathematical model describes how red blood cells return to equilibrium.
SPT predicts age and mass of red giants from spectra.
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.
Quantization and reduction for coisotropic A-branes on Hamiltonian manifolds.
This study uses deep learning to infer stellar parameters from short TESS and K2 observations.
New method classifies reticulocytes from red blood cells without labels.
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.
In this paper, we introduce an insurance ruin model with adaptive premium rate, thereafter refered to as restructuring/refraction, in which classical ruin and bankruptcy are distinguished. In this model, the premium rate is increased as soon as the wealth process falls into the red zone and is brought back to its regul…
VERA-V uses variational inference to discover vulnerabilities in multimodal vision-language models.
Human-AI teaming suffers from calibration issues.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
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…
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.
Scenario discovery is the process of finding areas of interest, known as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions, i.e., inputs of the simulation model, where the system is unstable. Subgroup discovery methods are commonly used for scenario discovery. They find…
After shortly recalling the construction of the Khovanov-Kuperberg algebras, we give a characterisation of indecomposable web-modules. It says that a web-module is indecomposable if and only if one can deduce it directly from the Kuperberg bracket (via a Schur lemma argument). The proofs relies on the construction of i…
We introduce \textcolor{red}{general} new techniques for computing the geometric index of a link in the interior of a solid torus . These techniques simplify and unify previous ad hoc methods used to compute the geometric index in specific examples \textcolor{red}{ and allow the simple computation of geometric i…
The study uses unsupervised machine learning to identify top European football teams.
RED detects sleep EEG events using deep neural networks, outperforming previous methods.
Paper presents Transfer Portal model for accurate player performance predictions.
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 …
We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(m*s+m*log(m)) time complexity, where m is the number of training examples, and s the average number of non-zer…
New approach for open ad hoc teamwork using graph-based policy learning.
Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared …
Gradient descent with error feedback performs better than vanilla when features are rare.
Although freelancing work has grown substantially in recent years, in part facilitated by a number of online labor marketplaces, (e.g., Guru, Freelancer, Amazon Mechanical Turk), traditional forms of "in-sourcing" work continue being the dominant form of employment. This means that, at least for the time being, freelan…
Introduces LTQL for factored policies in cooperative MARL.
BC-Aligner maintains backward compatibility of embeddings after frequent updates.
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…
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…
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…