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.
Evolutionary methods improve understanding of LLMs and their relationships.
New method embeds phylogenetic trees for clustering, recovering evolutionary relationships.
The key idea of this model is that firms are the result of an evolutionary process. Based on demand and supply considerations the evolutionary model presented here derives explicitly Gibrat's law of proportionate effects as the result of the competition between products. Applying a preferential attachment mechanism for…
Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within mu…
PhyloVAE learns tree topologies without supervision.
The relationships between game theory and quantum mechanics let us propose certain quantization relationships through which we could describe and understand not only quantum but also classical, evolutionary and the biological systems that were described before through the replicator dynamics. Quantum mechanics could be…
BoGA combines evolutionary search with Bayesian optimization for efficient protein design.
Mathematical foundation for phylogenetic tree uncertainty quantification.
GeoPhy uses geometric gradients to efficiently infer phylogenetic trees from molecular data.
Motivation. Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains very challenging to predict contact map using only sequence information. Most existing methods pr…
PhyloGFN uses GFlowNets to infer phylogenetic trees from sequence data.
Proposes an evolutionary approach to fitting acyclic VAR models.
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…
An analytic model is presented that considers the evolution of a market of durable goods. The model suggests that after introduction goods spread always according to a Bass diffusion. However, this phase will be followed by a diffusion process for durable consumer goods governed by a variation-selection-reproduction me…
evo-RL combines evolutionary computation with reinforcement learning for better adaptability.
Momentum speeds up evolutionary processes in machine learning.
A new method for embedding temporal relationships in graphs.
Study on cryptocurrency market dynamics and correlations over time.
We define Lie algebroids over infinite jet spaces and establish their equivalent representation through homological evolutionary vector fields.
New method infers causal relationships from nonstationary time series data.
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…
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…
Evolutionary algorithms improve neural network performance by discovering better activation functions.
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.
Combines variational and evolutionary optimization for generative models.
Evolutionary algorithms improve decision tree ensembles.
New research connects evolutionary dynamics to Bayesian learning.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
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.
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…
Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.
A financial system contains many elements networked by their relationships. Extensive works show that topological structure of the network stores rich information on evolutionary behaviors of the system such as early warning signals of collapses and/or crises. Existing works focus mainly on the network structure within…
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)…
The accurate and interpretable prediction of future events in time-series data often requires the capturing of representative patterns (or referred to as states) underpinning the observed data. To this end, most existing studies focus on the representation and recognition of states, but ignore the changing transitional…
I consider the existence and structure of conservation laws for the general class of evolutionary scalar second-order differential equations with parabolic symbol. First I calculate the linearized characteristic cohomology for such equations. This provides an auxiliary differential equation satisfied by the conservatio…