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.
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.
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.
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.
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.
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.
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.
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…
Python package for SPD matrix distances, reproducible and extensible.
problem Computing distances between SPD matrices for various applications.
method Unified, extensible framework supporting multiple SPD metrics.
result Reproducible and accessible SPD matrix comparison tool.
GridPyM handles grid diagrams for knot theory.
problem Handling grid diagrams for knot theory.
method Generates and simplifies grids, models local transformations.
result Models local transformations between grid diagrams.
Python tool calculates cobordism maps in Khovanov homology.
problem Computing cobordism maps on Khovanov homology.
method Developed a Python module to calculate these maps.
result Computed cobordism maps for all incompressible Seifert surfaces of prime knots up to 10 crossings.
We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a scikit-learn compliant API. GraSPy can be downloaded from Python Pac…
OGRePy simplifies tensor calculations in general relativity.
problem Complex tensor calculations in general relativity.
method Object-oriented Python package for symbolic tensor calculations.
result Reproduces functionality of Mathematica package OGRe with improvements.
Python tools for 3D shape analysis on Kendall's space.
problem Lack of practical utilities for advanced 3D shape analysis.
method Developed Python tools for 3D shape analysis on Kendall's 3D Shape Space.
result Efficient, accessible software solutions for researchers.
OPFython simplifies Optimum-Path Forest for Python users.
problem Lack of diversity and complexity in conventional classification algorithms.
method Develops a Python-based Optimum-Path Forest framework.
result OPFython provides a more friendly and faster prototyping environment.
Comprisk simplifies competing-risks analysis in Python.
problem Analyzing medical time-to-event data with competing risks.
method A scikit-learn-compatible toolkit for competing-risks survival analysis.
result Comprisk provides a unified API for various competing-risks methods.
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.
mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, repro…
Python's tools drive machine learning advancements across industries.
problem Processing and analyzing large data sets for insights.
method Advancements in deep learning, classical ML, and GPU computing.
result Python's dominance in scientific computing boosts machine learning adoption.
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.
mvlearn simplifies multiview machine learning for non-specialists.
problem Handling multiview data sets from disparate sources.
method Python library implementing multiview machine learning methods.
result Enables non-specialists to use multiview machine learning methods easily.
Python tool for constructing CMC surfaces using DPW method.
problem Constructing minimal and constant mean curvature surfaces.
method DPW method implemented in Python.
result Python package for CMC surfaces in various space forms.
Python package for projecting onto quadratic hypersurfaces.
problem Projections onto non-cylindrical central quadratic hypersurfaces.
method User-friendly Python package with documentation.
result Efficiently projects points onto quadratic hypersurfaces.
Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goa…
Picasso is a new library for sparse learning problems in R and Python.
problem Sparse learning problems in high-dimensional data analysis.
method Unified framework of pathwise coordinate optimization with efficient active set selection strategies.
result picasso can efficiently handle large-scale problems.
skfolio optimizes portfolios using Python, integrating machine learning.
problem Fundamental challenge in quantitative finance: robust portfolio optimization.
method Unified framework for diverse allocation strategies, including statistical and machine learning methods.
result Promotes reproducibility and transparency in quantitative finance.
SafePILCO is a Python tool for safe reinforcement learning.
problem Safe and efficient policy synthesis in reinforcement learning.
method Extends PILCO algorithm with safety features, implemented in Python.
result Safe and data-efficient policy synthesis achieved.
dalex simplifies model exploration and fairness for Python developers.
problem Model black-box nature and risks of discrimination, lack of reproducibility, and data drift.
method Model-agnostic interface for interactive model exploration.
result Enhances model transparency and accountability through interactive explainability and fairness.
Kernelmethods library simplifies kernel-based ML in Python.
problem Lack of a well-defined Python library for kernel-based ML.
method Develops a Python library that supports various kernel types and customizations.
result Enables efficient, modular, and interoperable kernel-based operations.
arfpy simplifies data generation with adversarial random forests.
problem Synthesizing new data that matches given data.
method Adversarial Random Forests (ARF) integrated into a Python package.
result Effective and user-friendly data generation for various fields.
Python models predict stock sentiment for market-beating returns.
problem Predicting public sentiment for stock trading.
method Crowd-sourced labeled data, trained and evaluated various models.
result Best models predict market-beating returns from public sentiment.
DADApy analyzes high-dimensional data manifolds in Python.
problem Analyzing complex, high-dimensional data.
method Estimating intrinsic dimension, density, clustering, comparing distance metrics.
result Effective analysis of data manifolds in Python.
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.
A Python tool assesses fairness, accountability, and transparency in AI decisions.
problem Lack of regulation and certification for AI-driven decisions.
method Developed an open-source Python toolbox to analyze fairness, accountability, and transparency aspects of machine learning.
result Automatically reports fairness, accountability, and transparency aspects of AI decisions to stakeholders.
We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Pytho…