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.
Enhances Bayesian learning with rule-based evolutionary techniques.
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted from the social media service StockTwits to accommodate the level of bullishness or bearishness of the online trading community towards certain…
I consider the geometry of the general class of scalar 2nd-order differential equations with parabolic symbol, including non-linear and non-evolutionary parabolic equations. After defining the appropriate -structure to model parabolic equations, I apply Cartan techniques to determine local geometric invariants (quan…
The paper proposes a technique to speed up evolutionary algorithms by using lower-cost approximations of the objective function.
The adaptation of numerical wind wave models to the local time-spatial conditions is a problem that can be solved by using various calibration techniques. However, the obtained sets of physical parameters become over-tuned to specific events if there is a lack of observations. In this paper, we propose a robust evoluti…
Evolutionary methods improve neural network loss functions, reducing overfitting.
Evolutionary algorithm finds optimal pixel perturbations to improve neural network generalization.
ESPD improves learning efficiency in sparse reward reinforcement learning.
A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measur…
A real-time federated neural architecture search approach reduces costs and improves performance.
PhyloGFN uses GFlowNets to infer phylogenetic trees from sequence data.
Evolutionary methods improve understanding of LLMs and their relationships.
In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being ro…
evo-RL combines evolutionary computation with reinforcement learning for better adaptability.
Momentum speeds up evolutionary processes in machine learning.
EvoGrad improves efficiency in meta-learning and hyperparameter optimization.
This research develops an evolutionary approach to discover non-Gaussian stochastic dynamical systems.
Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devic…
We define Lie algebroids over infinite jet spaces and establish their equivalent representation through homological evolutionary vector fields.
New method speeds up image denoising models without sacrificing performance.
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evolutionary optimization environment are ideally suited for an integration of various types of probabilistic constraints. W…
A coreset is a subset of the training set, using which a machine learning algorithm obtains performances similar to what it would deliver if trained over the whole original data. Coreset discovery is an active and open line of research as it allows improving training speed for the algorithms and may help human understa…
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Here we use phylogenetic Gaussian process regression to model the evolution of simulated function-valued traits. Given function-valued data only from the tips of an evolutionary tree and utilising independent pri…
We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently c…
A co-evolutionary approach for Heston model calibration reduces overfitting with diverse datasets.
The paper improves evolutionary computation by optimizing selection rates.
Novel evolutionary strategy solves stochastic constrained optimization problems.
CoNES optimizes blackbox functions using convex optimization and information geometry.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
Proves FR-NGD optimally approximates evolutionary dynamics and continuous Bayesian inference.
Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is criti…
EPNE models evolving network patterns for better predictions.
A method to control results of gradient descent unsupervised learning in a deep neural network by using evolutionary algorithm is proposed. To process crossover of unsupervisedly trained models, the algorithm evaluates pointwise fitness of individual nodes in neural network. Labeled training data is randomly sampled an…
New algorithm improves game learning with randomised optimism.
This paper studies adversarial examples in NIDS, revealing their vulnerability.
Combines variational and evolutionary optimization for generative models.
New research connects evolutionary dynamics to Bayesian learning.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
AutoML discovers complete machine learning algorithms from basic operations.
This research proposes the econophysics kinetic market model as an evolutionary algorithm's instance. The immediate results from this proposal is a new replacement rule for family competition genetic algorithms. It also represents a starting point to adding evolvable entities to kinetic market models.
The last financial and economic crisis demonstrated the dysfunctional long-term effects of aggressive behaviour in financial markets. Yet, evolutionary game theory predicts that under the condition of strategic dependence a certain degree of aggressive behaviour remains within a given population of agents. However, as …
Enhanced evolutionary algorithms solve NP-hard portfolio optimization with cardinality constraints.
In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid …
Automatically discovers effective activation functions for deep learning.
Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs) in a novel way - not for optimising a learning algorithm, but for finding a solution to an optimisation problem. Deep learning has been successfully applied to classification, regression, decision and generative tasks and in this paper…
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…