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.
Paper uses polar field data to improve solar flare prediction accuracy.
We present a novel algorithm that predicts the probability that the time derivative of the horizontal component of the ground magnetic field exceeds a specified threshold at a given location. This quantity provides important information that is physically relevant to Geomagnetically Induced Currents (GIC), whic…
CNNs improve wind speed forecasts in the Netherlands.
Three methods detect informed trading on prediction markets, each focusing on different aspects.
The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the forecast u…
New metrics fail adversarial tests, with some more robust than others.
MOVDA improves skill ratings by considering margin of victory deviations.
Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation. However, standard DRL methods often suffer from poor sample efficiency, partially because they aim to be entirely problem-agnostic. In this …
Study compares two methods for predicting extreme atmospheric events.
ReSkill reconciles RL skill creation with policy optimization.
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
Paper develops a framework to optimize neural networks using weighted metrics.
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…
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
Deep learning model forecasts PV power production with high accuracy.
Bayesian nonparametrics enables dynamic skill discovery from expert data.
Paper learns skill distributions from game outcomes, proving minimax optimality.
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are parameterized in a way that makes them easily reusable. This diversity and p…
We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up t…
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each…
Long-range climate forecasts use integrated assessment models to link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework outlined in part 1 of this study (Garrett, 2014) that approaches the global economy using purely physical principles rather than explicitly resolve…
The paper explains how language models acquire complex skills through scaling laws and statistical analysis.
PredictaBoard benchmarks LLM score predictors to assess their ability to anticipate errors.
This paper combines and improves probabilistic forecasts of wind speeds using advanced statistical methods.
Researchers study how skills are learned in neural networks using physics principles.
A study shows that a fine-tuned model's directional accuracy in financial forecasting is largely due to chance, not skill.
The paper examines skill estimation and variance under model misspecification in IRT.
Acquiring new robot motor skills is cumbersome, as learning a skill from scratch and without prior knowledge requires the exploration of a large space of motor configurations. Accordingly, for learning a new task, time could be saved by restricting the parameter search space by initializing it with the solution of a si…
Humans are able to perform a myriad of sophisticated tasks by drawing upon skills acquired through prior experience. For autonomous agents to have this capability, they must be able to extract reusable skills from past experience that can be recombined in new ways for subsequent tasks. Furthermore, when controlling com…
ABC method improves subseasonal weather forecasting by 60-90%.
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other …
Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing policies which generalize across tasks, despite training for thousands of hours equivalent real robot time. To address this shortcoming, we…
A planning approach learns skills from interactions, balancing exploration and exploitation.
Unified scoring model improves efficiency and performance across multiple tasks.
Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just …
Integrates skills and world models for efficient task solving and transfer.
New algorithm trains neural nets on simple skills to learn complex tasks faster.
Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of…
Sloth predicts LLM performance using latent skills across families.
The paper presents a method for personalized exercise recommendations that improves learner skill gain.
Study shows Elo models fail to accurately measure transitive strength in competitive games.
New methods for skill rating in sports using state-space models.
We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks from a distribution of interest and uses the corresponding learned policies to estimate the topology of the lower-dimensional piecewise-smooth…
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Leaving the skills fixed can le…
For robots to coexist with humans in a social world like ours, it is crucial that they possess human-like social interaction skills. Programming a robot to possess such skills is a challenging task. In this paper, we propose a Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like interaction skills thr…
Foresight Arena benchmarks AI forecasting on real-world markets, isolating predictive edge.
Enhances solar flare prediction with advanced preprocessing and contrastive learning.
New method boosts skill learning by encouraging optimistic exploration.