PAMS is a Python-based platform for simulating artificial markets.
problem Simulating complex market behaviors for research and education.
method Developed as a Python-based simulator with deep learning integration.
result Demonstrated effectiveness through agent price prediction studies.
Python package for fast simulation-based inference.
problem Intractable likelihood functions in Bayesian inference.
method Uses neural networks as surrogate models for Bayesian inference.
result Highly efficient and user-friendly for constructing SBI estimators.
ParaMonte::Python streamlines Bayesian data analysis with fast Monte Carlo and MCMC routines.
problem Efficiently sampling posterior distributions in Bayesian modeling and data science.
method Serial and MPI-parallelized Markov Chain Monte Carlo (MCMC) routines.
result Automated model calibration and uncertainty quantification in Bayesian analysis.
Paper uses Simulated Bifurcation for quick asset allocation optimization.
problem Optimal asset allocation selection.
method Simulated Bifurcation algorithms applied to 441 S&P500 assets.
result Unrivaled timescale for optimal sub-allocation selection.
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents' world and henc…
FDApy simplifies analysis of functional data in Python.
problem Analysis of irregularly sampled functional data.
method Implementation of tools for representation, dimension reduction, and dataset generation.
result Efficient analysis of functional data, including irregularly sampled data.
PyVBMC speeds up Bayesian inference for expensive models in Python.
problem Efficient Bayesian inference for computationally expensive models.
method Variational Bayesian Monte Carlo (VBMC) algorithm.
result PyVBMC provides a flexible and efficient method for parameter estimation and model assessment.
Python toolbox for causal structure learning from data.
problem Causal structure learning from data.
method Generates data from simulators or real-world datasets, learns causal structure, evaluates graphs, and includes gradient-based methods.
result Convenience and efficiency in causal discovery with GPU acceleration.
A new Python-C++ framework for agent-based simulation.
problem Understanding market dynamics and effects of delays.
method User-friendly Python API with efficient C++ implementation, message-driven architecture.
result Investigated the role of order processing delay in financial markets.
PyDTS analyzes survival data with discrete intervals and competing risks.
problem Discrete-time survival analysis with competing risks and optional penalization.
method Regularized estimation methods, model evaluation metrics, variable screening tools, and simulation module.
result Supports research and development in discrete-time survival analysis.
SurvLIMEpy is a Python package for computing feature importance in survival analysis.
problem Computing feature importance in survival analysis models.
method Parallelized implementation of the SurvLIME algorithm for various survival models.
result SurvLIMEpy accurately captures feature importance in survival analysis models.
NUBO simplifies Bayesian optimization for researchers.
problem Optimizing expensive functions like experiments and simulations.
method Bayesian optimization using Gaussian processes and acquisition functions.
result Transparency and user-friendly design for easy access.
Quantum computing techniques applied to Monte Carlo simulations in finance.
problem Efficiently simulating quantum algorithms for financial modeling.
method Introduces quantum computing basics, amplitude estimation, and Grover's algorithm for unstructured search.
result Demonstrates quantum approaches to Monte Carlo integration and counting in finance.
BBE simulates sports betting exchanges for data generation.
problem Creating synthetic data for betting strategy testing.
method Agent-based model (ABM) for sports betting exchange simulation.
result Simulation runs up to 1000 times faster with GPU.
Efficient packages solve SLOPE problem in multiple languages.
problem Efficiently solving SLOPE problem for various data types.
method Hybrid coordinate descent algorithm for GLMs with multiple loss functions.
result Packages outperform existing SLOPE implementations in speed.
A Python tool generates synthetic data for cluster analysis from high-level descriptions.
problem Creating synthetic data for cluster analysis is laborious and requires detailed geometric parameters.
method Proposes natural language-based synthetic data generation and implements it in a Python package.
result Makes it easy to set up interpretable and reproducible benchmarks for cluster analysis.
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
OpenML-Python API simplifies access to OpenML for Python users.
problem Limited access to OpenML for Python users.
method Developed a Python API (OpenML-Python) to integrate OpenML with Python-based tools.
result Facilitates easy access to OpenML's datasets, tasks, and experiments.
In this work we simulate null geodesics for the Bonnor massive dipole metric by implementing a symbolic-numerical algorithm in Sage and Python. This program is also capable of visualizing in 3D, in principle, the geodesics for any given metric. Geodesics are launched from a common point, collectively forming a cone of …
This research explores complex-valued neural networks and their implementation.
problem The challenges of implementing complex-valued neural networks and their potential for non-complex data.
method Detailed theory and implementation of CVNN, including Wirtinger calculus, complex backpropagation, and modules like complex layers and activation functions. Python implementation using cvnn toolbox.
result Demonstrates the potential of CVNN for non-complex data through simulations.
Python scripts analyze MRAM-based neuromorphic devices' process variation impacts on machine learning accuracy.
problem Impact of process variation on MRAM-based neuromorphic devices' performance in machine learning applications.
method Developed transportable Python scripts to analyze output variation under changes in device dimensions.
result Revealed impacts and limits for processing variation of device fabrication on energy vs. accuracy tradeoffs.
DRAMA detects anomalies in high dimensions via dimensionality reduction.
problem Challenges in anomaly detection for large datasets in high dimensions.
method Dimensionality reduction and unsupervised clustering.
result DRAMA is robust and competitive in high dimensions, especially for online anomaly detection.
Python library for boosting statistical relational models.
problem Expressing learning and inference problems in statistical relational models.
method Adapting scikit-learn interface for boosted statistical relational models.
result Provides examples for using srlearn.
CausalML simplifies causal inference methods in Python.
problem Combining causal inference and machine learning.
method Collection of causal inference methods in Python.
result Makes causal inference methods accessible in Python.
CoinTossX is a low-latency, open-source matching engine for financial trading.
problem Efficiently matching orders in financial markets with low latency and high throughput.
method Developed in Java, orders submitted via UDP SBE, low-latency message transport (Aeron Media Driver). Separates order generation and matching.
result Demonstrated low-latency, high-throughput performance in various deployment scenarios.
BayesFlow 2 speeds up Bayesian inference for complex models.
problem Slow Bayesian inference for complex models.
method Amortized Bayesian inference with neural networks.
result Streamlined workflow supports broad adoption.
Geometric Brownian motion simulates stock prices for Brazilian small caps index.
problem Simulating stock prices for the Brazilian small caps index.
method Used geometric Brownian motion to simulate stock prices of Brazilian small caps index using historical data.
result Simulated prices better for portfolios with higher returns, lower risks, and higher Sharpe Indexes.
Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT) in Python. It takes numeric functions written in a syntactic subset of Python and NumPy as input, and generates new Python functions which c…
BARMPy offers a Python package for Bayesian Additive Regression Models.
problem Making complex Bayesian models accessible to machine learning practitioners.
method Object-oriented design compatible with SciKit-Learn, documentation and tutorial provided.
result Ease of use and compatibility with existing machine learning tools.
MKLpy simplifies Multiple Kernel Learning in Python.
problem Learning optimal kernel functions from data.
method Python-based framework for Multiple Kernel Learning algorithms.
result Maximizes usability and simplifies development of novel solutions.
Python package ajdmom simplifies moment formula derivation for jump diffusions.
problem Deriving moment formulae for complex jump diffusion processes.
method Automatically generates closed-form expressions and derivatives for any order of moments.
result Enhances usability and usability of affine jump diffusion models.
Upper bounds for CV errors apply to lasso and other models.
problem Bounding CV errors for lasso and similar models.
method Rademacher complexity and Orlicz-Ψν norm. result Upper bounds are tight and stable for lasso.
PySAD offers a unified Python framework for efficient streaming anomaly detection.
problem Efficient anomaly detection in streaming data with strict constraints.
method Unified architecture with 17+ streaming algorithms, specialized components, and support for multiple learning paradigms.
result PySAD enables real-time processing with bounded memory and is compatible with other Python frameworks.
Cyanure offers efficient solvers for linear model learning in Python, C++, and more.
problem Efficiently solving empirical risk minimization problems for linear models.
method Stochastic variance-reduced optimization with acceleration mechanisms.
result Handles a wide range of loss and regularization functions.
The paper develops a new simulation technique for estimating conditional expectations in financial models.
problem Estimating conditional expectations in financial models with expensive simulation of endogenous variables.
method Introduces a hierarchical simulation scheme with oversimplified defaults to address variance issues.
result The hierarchical simulation technique significantly improves the success of neural net regression for conditional expectation estimation.
Python library for causal discovery from observational data.
problem Revealing causal relations from observational data.
method Comprehensive collection of causal discovery methods in Python.
result Ease of use for non-specialists and modular building blocks for developers.
Estimates causal contributions of multiple causes on outcome changes.
problem Quantifying the effect of multiple causes on an outcome change.
method Develops a multiply robust estimation strategy combining regression and re-weighting methods.
result The method recovers the target parameter under partial misspecification and is consistent and asymptotically normal.
Factor Engine simplifies financial factor computation and analysis in Python.
problem Efficient computation and analysis of financial factors.
method Modular, extensible Python library with decorators, integrates with data science ecosystem.
result Mispricing factors computed by Factor Engine and Stata implementation are highly similar.
NoMoPy models noise as HMM/FHMM in Python.
problem Modeling noise in data.
method Approximate and exact EM algorithms, cross-validation, confidence region estimation.
result Validated on example problems.
PyHHMM is a Python library for HHMMs with advanced features.
problem Handling heterogeneous observation models and missing data in HMMs.
method Object-oriented Python implementation with advanced features.
result PyHHMM supports a heterogeneous observation model and missing data inference.
Python package for functional data analysis.
problem Handling and analysis of functional data.
method Comprehensive tools for representation, preprocessing, and exploratory analysis of functional data.
result Scikit-fda package provides a comprehensive set of tools for functional data analysis.
AutoGMM automates Gaussian mixture modeling in Python.
problem Automatic clustering of complex data with uncertainty-aware grouping.
method Strategic initialization using an agglomerative Mahalanobis heuristic, parallelized model selection by information criteria.
result Strong out-of-the-box performance on classic benchmarks and real datasets.
Distiller simplifies DNN compression research with a Python package.
problem Efficiently compressing deep neural networks.
method Open-source Python package with DNN compression algorithms.
result Facilitates new research and learning tasks in DNN compression.
RobPy offers robust statistical methods in Python.
problem Lack of robust statistical methods in Python.
method Built on NumPy, SciPy, and scikit-learn, RobPy includes robust tools for various statistical tasks.
result RobPy enables more users to perform robust data analysis in Python.
This paper improves financial simulations using Tensor Processing Units and Tensorflow.
problem Estimating sensitivities in financial models efficiently.
method Utilizing Tensor Processing Units and Tensorflow for fast and automated differentiation.
result Single line of code for estimating sensitivities in financial models.
DoubleML is a Python library for causal inference using machine learning.
problem Estimating causal parameters in complex models with machine learning.
method Double machine learning framework for valid statistical inference.
result High flexibility and easy extension for various model specifications.
Sparklen is a Python toolkit for high-dimensional Hawkes processes.
problem Efficiently analyzing high-dimensional Hawkes processes.
method Combines Python for ease of use with C++ for performance.
result Provides state-of-the-art tools for estimating and classifying Hawkes processes.
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for us…