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.
MAPIE provides uncertainty quantification for ML models.
problem Estimating uncertainties in ML model predictions.
method Conformal prediction methods for single-output regression and multi-class classification.
result Strong theoretical guarantees on marginal coverages.
We consider the problem of finding the minimizer of a function f:Rd→R of the finite-sum form minf(w)=1/n∑infi(w). This problem has been studied intensively in recent years in the field of machine learning (ML). One promising approach for large-scale data is to use a stoc…
TFCheck detects training issues in ML programs using TensorFlow.
problem Training programs often contain inconsistencies and bugs.
method Developed a TensorFlow library (TFCheck) with verification routines.
result TFCheck successfully detects training issues in ML code.
A framework for developing ML systems from ML primitives.
problem Complexity and fragmentation in ML systems development.
method Unified API, ML primitives, AutoML strategies, and pipelines.
result General-purpose, multi-task AutoML system for various data modalities and problem types.
Tribuo integrates ML models with provenance in Java.
problem Tracking and ensuring ML models behave appropriately as they grow in number and complexity.
method Integrates model training, inference, type-safety, runtime checking, and automatic provenance recording into a single Java library.
result Automatic recording of full processing pipeline and training details inside the model object.
This work shows how to compute subderivatives efficiently without errors.
problem Inefficient and incorrect computation of subderivatives in ML libraries.
method Developed a method to compute provably correct generalized subderivatives at a cost close to the function itself.
result Provable correct generalized subderivatives can be computed at a cost within a factor of 6 of the function itself.
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.
ALICE combines feature selection and inter-rater agreeability for ML model insights.
problem Improving interpretability of black box machine learning models.
method Integrates feature selection and inter-rater agreeability into a user-friendly Python library.
result Initial experiments on customer churn modeling show promising insights.
pyLOT library simplifies machine learning on 3D point clouds via linearized optimal transport.
problem Performing machine learning tasks on 3D point clouds.
method Linearized optimal transport (LOT) to embed distributions into Hilbert space, enabling linear machine learning.
result Downstream tasks on embedded representations are simplified to linear operations.
MARVIN offers an open ML environment for automated primitive annotation and execution.
problem Automated machine learning primitive annotation and execution.
method Web-based Python application with Docker containers on Kubernetes.
result Over 400 datasets and challenges from various ML domains.
MLPerf benchmarks ML inference systems across diverse hardware.
problem Challenges in evaluating ML inference systems due to variety and complexity.
method MLPerf prescribes a set of rules and best practices for comparability.
result First call for submissions yielded over 600 reproducible measurements.
Mango automates hyperparameter tuning for large-scale ML training.
problem Manual hyperparameter tuning is tedious and inefficient for large-scale machine learning.
method Parallel hyperparameter tuning with intelligent search strategies and flexible abstractions.
result Mango achieves comparable performance to Hyperopt while supporting distributed computing.
With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticate…
This paper emphasizes the need for uncertainty quantification in data-driven ML models for nuclear engineering.
problem Uncertainty in ML predictions due to data noise, model architecture, and stochastic training.
method Explains and compares uncertainties in physics-based and data-driven models, and presents techniques to quantify ML prediction uncertainties.
result The importance of uncertainty quantification in ML models for nuclear engineering applications.
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.
SAGDA generates synthetic agricultural datasets to improve ML in African farming.
problem Data scarcity in African agriculture limits machine learning innovations.
method SAGDA is an open-source Python toolkit that generates, augments, and validates synthetic agricultural datasets.
result SAGDA enhances ML applications in agriculture, such as yield prediction and fertilizer recommendation.
RFX-Fuse combines Breiman and Cutler's Random Forest with modern ML capabilities.
problem Lack of a unified ML engine with diverse capabilities.
method Unified ML engine with native GPU/CPU support, delivering 5+ functionalities in one model.
result Native explainable similarity and imputation validation.
MLModelScope streamlines ML/DL model evaluation and benchmarking.
problem Challenges in evaluating and benchmarking ML/DL models.
method Open-source, framework/hardware agnostic platform with distributed design.
result Demonstrates the impact of model evaluation pipelines and HW/SW choices.
Develops ML-DQA for healthcare data quality assurance.
problem Inconsistent use of real-world data in machine learning projects.
method Develops ML-DQA framework based on RWD best practices.
result Five generalizable practices emerge from ML-DQA implementation.
XSP profiles ML models across hardware and software stacks.
problem Challenges in profiling ML model performance across different layers of the stack.
method XSP uses distributed tracing to aggregate data from various sources and introduces a leveled, iterative measurement approach.
result XSP provides insights into ML model execution not easily discernible otherwise.
Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques togeth…
Novel framework for ML-assisted inference valid for any statistical task.
problem Limited validity of existing methods for post-prediction inference.
method Introduces PSPS framework for task-agnostic ML-assisted inference.
result Valid and efficient inference for arbitrary ML models.
CodeReef enables sharing ML models across platforms efficiently.
problem Sharing and deploying ML models across different systems efficiently.
method Developed an open platform to share ML components, automate deployment, and benchmark models.
result Demonstrated efficient deployment and benchmarking of ML models across diverse platforms.
SparseChem speeds up ML for small molecules.
problem Training fast and accurate ML models for high-dimensional data.
method Supports millions of features and compounds, trains various models.
result Fast and accurate machine learning models for biochemical applications.
Paper introduces probabilistic approach to CO layers in ML.
problem Challenges in training CO layers with stochastic gradient descent.
method Probabilistic perspective, approximate differentiation, structured losses.
result InferOpt.jl package for training CO layers.
AutoML library TPOT optimizes brain age prediction models without prior knowledge.
problem Optimizing machine learning models for brain age prediction.
method TPOT uses genetic programming to find optimal machine learning pipelines.
result TPOT outperformed state-of-the-art models in brain age prediction.
Efficiently reduces costs for Bayesian networks in FGrn form.
problem High computational and memory costs of Bayesian networks in FGrn form.
method Detailed algorithmic and structural analysis leading to cost reduction solutions, including an online learning algorithm.
result Proposed solutions and online learning algorithm significantly reduce costs for Bayesian networks.
We introduce vine computational graphs for efficient ML integration of vine copulas.
problem Integrating vine copulas into modern machine learning pipelines.
method Developed vine computational graphs and algorithms for conditional sampling, scheduling, and structure construction.
result Gradient flow through vine copulas improves performance in machine learning models.
This paper automates multi-label classification using an extended AutoML tool.
problem Lack of automated solutions for multi-label classification.
method Extends ML-Plan for multi-class classification to multi-label classification using MEKA.
result The proposed approach yields superb results and performs significantly better than baselines.
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.
Import2vec creates embeddings for software libraries to improve learning tasks.
problem Developing semantic representations for software libraries.
method Applied word embedding techniques from NLP to library packages.
result Library vectors capture meaningful relationships among libraries.
CSLVAE generates large chemical libraries efficiently.
problem Navigating ultra-large combinatorial synthesis libraries.
method Hierarchically-organized database with molecular encoder and decoder.
result Generates valid molecular graphs without autoregression.
Extracts StarCraft II tournament data for AI and ML studies.
problem Lack of accessible esports data for scientific use.
method Gathered and processed StarCraft II tournament replays using an API parser library.
result The largest publicly available StarCraft II esports dataset.
MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript.…
TSGM generates synthetic time series data for ML research.
problem Scarce or sensitive real-time series data.
method Generative models, probabilistic, and simulator-based approaches.
result Framework evaluates data quality from multiple angles.
AITuning uses reinforcement learning to optimize communication libraries.
problem Optimizing communication libraries for better performance in parallel applications.
method Deep reinforcement learning applied to tuning communication libraries.
result Demonstrates the effectiveness of reinforcement learning for tuning communication libraries.
MRCpy implements minimax risk classifiers with performance guarantees and distribution shift adaptability.
problem Classical risk minimization approaches are not robust to distribution shifts.
method Robust risk minimization approach for minimax risk classifiers.
result MRCs provide performance guarantees and adapt to distribution shifts.
πBO augments BO with user beliefs for better hyperparameter optimization.
problem BO ignores user beliefs, reducing its appeal to practitioners.
method Proposes πBO, an acquisition function that incorporates user-provided prior beliefs. result πBO outperforms competing approaches and deep learning tasks. LIBTwinSVM offers a free library for efficient Twin Support Vector Machines.
problem Large-scale classification problems.
method Efficient implementation of Twin Support Vector Machines.
result Effectiveness demonstrated through benchmarks.
TrueLearn Python library for personalized educational recommendations.
problem Building educational recommendation systems with humanly-intuitive user representations.
method Online learning Bayesian models and open learner concept.
result Library includes models and representations for user control and interpretability.
IllinoisSL is a Java library for learning structured prediction models. It supports structured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to …
Analyzes 6M Python notebooks and 2M enterprise DS pipelines to guide investments in data science.
problem Challenges in following the rapidly evolving landscape of data science technologies and applications.
method Downloaded and analyzed over 6M Python notebooks and 2M enterprise DS pipelines, performing statistical and comparative analyses.
result Identifies actionable conclusions for system builders and technology bets for practitioners based on current trends.
Modified BFGS and LBFGS++ libraries boost performance for non-parallelizable functions.
problem Improving performance of non-parallelizable functions using SIMD and AAD.
method Modifications to BFGS and LBFGS++ libraries, utilizing SIMD and Automatic Differentiation (AAD).
result Up to 3.8 times faster for European Swaption curve calibration and 1.4 times faster for LMM model calibration.
Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of twe…
Automatically extracts hyperparameter schemas from AI library documentation.
problem Lack of machine-readable hyperparameter schemas for AI automation tools.
method Automatically mines Python docstrings in AI libraries to extract JSON Schemas.
result Effective at extracting machine-readable schemas from 119 AI models.
FCL3 learns from multiple representative models to adapt to new tasks.
problem Degradation of performance in lifelong learning models for new task environments.
method Two knowledge libraries: feature learning and model knowledge. Self-selection of representative models for new tasks.
result FCL3 achieves better performance than most lifelong learning frameworks.
Python library for conformal prediction, licensed under MIT.
problem Improving prediction accuracy with uncertainty quantification.
method Conformal prediction framework implemented in Python.
result Stable API and algorithms for conformal prediction.