A JAX toolbox solves optimal transport problems for point clouds and histograms.
problem Optimal transport problems between point clouds and histograms.
method Automatic and custom reverse mode differentiation, vectorization, just-in-time compilation, and accelerators support.
result Solves a wide range of optimal transport problems including regularized OT, barycenters, Gromov-Wasserstein, and low-rank solvers.
A Matlab toolbox for tensor operations based on t-product.
problem Extending matrix operations to tensors.
method Developed a Matlab toolbox implementing tensor operations based on t-product.
result Implemented several tensor operations including SVD, spectral norm, and nuclear norm.
Uncertainty Toolbox aids in assessing and improving uncertainty quantification in machine learning.
problem Disparate evaluation metrics and implementations hinder direct comparison of uncertainty quantification results.
method Provides an open-source Python library for assessing, visualizing, and improving uncertainty quantification.
result Facilitates more accurate and comparable uncertainty quantification across different works.
advertorch is a PyTorch toolbox for adversarial robustness.
problem Improving model robustness against adversarial attacks.
method Contains various attack, defense, and training methods.
result Efficient and concise reference implementations for adversarial robustness.
This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease relate…
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data s…
Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification. When dealing with mixture models, there are many issues that the experimenter should be aware of and needs to solve. The MixEst toolbox is a powerful and user-friendly package for M…
ZOOpt toolbox simplifies derivative-free optimization for machine learning tasks.
problem Efficiently solving optimization problems with complex functions.
method Derivative-free solvers, parallel and distributed optimization.
result ZOOpt toolbox simplifies optimization for machine learning tasks.
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 statistical toolbox for analyzing model performance in medical imaging.
problem Analyzing model performance by patient and recording properties, especially in medical imaging.
method Selection of appropriate performance metrics, correction of multiple comparisons, and finding interesting subgroups.
result Enables rigorous assessment of model performance for potential subgroup disparities.
Pattern recognition and machine learning are becoming integral parts of algorithms in a wide range of applications. Different algorithms and approaches for machine learning include different tradeoffs between performance and computation, so during algorithm development it is often necessary to explore a variety of diff…
Toolbox for stochastic Euler equations using Ebin-Marsden theory.
problem Applying geometric framework to stochastic PDEs.
method Combining infinite-dimensional geometry and stochastic analysis.
result Local well-posedness of maximal solutions for incompressible Euler equation with noise.
PyOD offers scalable outlier detection for multivariate data.
problem Scalable outlier detection for multivariate data.
method Wide range of outlier detection algorithms, including ensembles and neural networks.
result Robust and scalable outlier detection.
Developing active inference agents for edge devices with limited resources.
problem Creating effective active inference agents on edge devices with limited computational resources.
method Introducing a software toolbox to accelerate the development of active inference agents by non-experts.
result Accelerates the democratization of active inference agents for edge devices.
Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold. While many optimization problems are of the described form, technicalit…
New survival learners estimate heterogeneous treatment effects from time-to-event data.
problem Estimating HTEs from time-to-event data with censoring outcomes.
method Orthogonal survival learners with theoretical guarantees and custom weighting functions.
result Orthogonal survival learners provide robust and model-agnostic HTE estimation.
Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the number of generated data of ordinal nature is increasing, the importance and role of the PL field becomes central within machine learning research and practice. This paper introduces an open source, scalable…
Polylab is a MATLAB toolbox for multivariate polynomial modeling.
problem Efficiently modeling and manipulating multivariate polynomials across CPU and GPU.
method Unified symbolic-numeric interface, three aligned classes (MPOLY, MPOLY_GPU, MPOLY_HP), polynomial operations, differentiation, matrix computations.
result Advantages of MPOLY-HP for reduction-heavy simplification and large-scale computations, and the stochastic log-determinant variant for sparse regimes.
ART is a Python library for defending ML models against adversarial threats.
problem Vulnerability of ML models to adversarial examples.
method Pre-processing, adversarial data augmentation, runtime detection.
result Provides tools to certify and verify model robustness.
Python toolbox uncovers causal relationships from data.
problem Discovering causal relationships from observational data.
method End-to-end approach using algorithms from 'Bnlearn' and 'Pcalg', including pairwise causal discovery.
result Recovery of direct dependencies and causal relationships.
One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods based on extreme learn…
Toolbox provides datasets and metrics for state representation learning.
problem Lack of standard evaluation datasets, metrics and tasks for state representation learning.
method Provides a set of environments, data generators, robotic control tasks, metrics and tools.
result Facilitates iterative state representation learning and evaluation in reinforcement learning settings.
SchNetPack 2.0 enhances atomistic machine learning with improved neural networks.
problem Improving atomistic machine learning methods and applications.
method Improved data pipeline, equivariant neural networks, PyTorch implementation, PyTorch Lightning, Hydra configuration framework.
result Easy extension and complex training tasks support.
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.
Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on problems where the smooth geometry of the search space can be leveraged to design efficient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. S…
PoPPy simplifies point process modeling and analysis.
problem Efficient modeling and analysis of sequential data.
method Flexible design and efficient learning of point process models.
result PoPPy enables large-scale point process analysis, simulation, and prediction.
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.
MATLAB toolbox pde2path solves geometric PDEs and finds bifurcations in immersed surfaces.
problem Finding bifurcations in geometric PDEs of immersed surfaces.
method Solving PDEs for surface displacement, updating surface, detecting and localizing bifurcations, and switching branches.
result Symmetry breaking bifurcations in various geometric surfaces.
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi…
simpcomp is an extension (a so called package) to GAP, the well known system for computational discrete algebra. The package enables the user to compute numerous properties of (abstract) simplicial complexes, provides functions to construct new complexes from existing ones and an extensive library of triangulations of …
A Python package for GLHMM, a flexible HMM framework.
problem Handling diverse HMM applications in neuroscience.
method Stochastic variational inference for large datasets.
result Enables statistical testing and out-of-sample prediction.
mlr3mbo is a modular R toolbox for Bayesian optimization.
problem Efficiently solving optimization problems with multiple objectives and constraints.
method Bayesian optimization with support for multi-objective, multi-point proposals, parallelization, and custom algorithms.
result mlr3mbo performs competitively with state-of-the-art optimizers and robustly handles various optimization regimes.
Advbox generates adversarial examples to test neural network robustness.
problem Vulnerability of neural networks to adversarial examples.
method Tool to create adversarial examples in multiple frameworks.
result Benchmarking robustness of machine learning models.
BPt is a Python library for ML with neuroimaging data.
problem Analyzing large neuroimaging datasets using machine learning.
method Unified framework of ML tools for tabulated and neuroimaging data.
result Unified ML tools for neuroimaging and tabulated data.
Ludwig simplifies deep learning for non-experts.
problem Making deep learning accessible to non-experts.
method Type-based data abstraction and declarative configuration files.
result Ludwig democratizes deep learning, making it accessible to a broader audience.
Twitmo analyzes geo-tagged Twitter data for topic modeling and visualization.
problem Analyzing public discourse on Twitter for various topics, parties, or individuals.
method Collects and preprocesses geo-tagged Tweets, applies LDA, CTM, STM, and visualizes results.
result Automatic pooling of Tweets into pseudo-documents improves topic coherence.
ALiPy simplifies active learning in Python.
problem Reducing labeling costs in supervised learning.
method Python toolbox for active learning.
result Facilitates evaluation and comparison of active learning methods.
MLaut automates machine learning benchmarking.
problem Benchmarking machine learning algorithms on diverse datasets.
method High-level workflow interface, database of datasets, scikit-learn and keras integration.
result Deep neural networks perform poorly on standard supervised learning tasks.
New bounds for non-convex estimators without Bernstein condition.
problem Sharp excess risk bounds for non-convex and improper estimators.
method Exponential-tail local Rademacher complexity risk bounds with offset condition.
result Sharp bounds for non-convex and improper estimators without Bernstein condition.
DPPy offers Python tools for sampling DPPs.
problem Sampling from DPPs is challenging.
method Gathers exact and approximate sampling algorithms for finite and continuous DPPs.
result DPPy provides Python tools for DPP sampling.
Probabilistic models with deep neural networks now handle large data sets.
problem Historical constraints on probabilistic modeling.
method Variational inference, stochastic gradient descent, distributed computation.
result Probabilistic models can now handle large data sets and complex relationships.
PHS optimizes hyperparameters in parallel for expensive computations.
problem Optimizing hyperparameters in computationally expensive tasks.
method Bayesian optimization for parallel hyperparameter search.
result Efficient hyperparameter optimization on multiple instances.
We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. Our approach first solves a L1 penalized version of the NP-hard sparse PCA optimization problem and then uses a randomized rounding strategy to sparsify the resulting dense solution. Our main theoretical r…
SMT-EX enhances SMT for explaining surrogate models of mixed-variable design problems.
problem Making decisions and understanding complex systems using surrogate models of mixed-variable design problems.
method Integrates explainability techniques into SMT, including Shapley Additive Explanations, Partial Dependence Plot, and Individual Conditional Expectations.
result Demonstrates versatility in addressing diverse problem characteristics.
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of interm…
A new 1-transfer space construction for hyperbolic groups.
problem Proving the Farrell-Jones conjecture for hyperbolic groups.
method Explicit construction of a 1-transfer space.
result A new method to prove the Farrell-Jones conjecture for hyperbolic groups.
Geometric approach solves Euler equations with random forces.
problem Solving Euler equations with stochastic forcing.
method Infinite-dimensional geometric approach, combining stochastic analysis and Sobolev mappings.
result Local existence and uniqueness of strong solutions.
Machine learning aids in neuroscience across various tasks.
problem Understanding complex brain functions through ML.
method ML for engineering, predictive modeling, benchmark setting, and modeling brain function.
result Machine learning is a valuable tool for systems neuroscience.