Pipeline-aware hyperparameter tuning speeds up machine learning pipelines by reusing intermediate computations.
problem High computational burden in hyperparameter tuning of multi-stage pipelines.
method Proposes a hybrid hyperparameter tuning method and a caching problem formulated as an ILP to maximize reuse.
result Pipeline-aware approach offers over an order-of-magnitude speedup over independent evaluations.
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …
Pipeline decomposes portfolio optimization problems into smaller, solvable subproblems.
problem Large-scale portfolio optimization with constraints.
method Decomposition pipeline with preprocessing, clustering, and risk rebalancing.
result Pipeline reduces problem size by 80% and computation time.
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …
RankML predicts machine learning pipeline performance quickly and accurately.
problem Lack of machine learning experts and high computational costs.
method Meta-learning approach to predict pipeline performance.
result RankML outperforms or matches state-of-the-art approaches with less time and resources.
Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibili…
Optimizes pipelined computation and communication for edge learning within latency constraints.
problem Balancing data transmission and model training to meet latency requirements.
method Analyzes the optimal packet payload size tradeoff between bias and variance.
result Derives analytical bounds on the expected optimality gap for effective optimization.
DeepLine automates ML pipeline generation using reinforcement learning.
problem Automatic generation of end-to-end ML pipelines combining multiple algorithms.
method Deep Reinforcement Learning with hierarchical actions filtering.
result DeepLine outperforms state-of-the-art approaches in accuracy and computational cost.
Unreduced PDs can perform similarly to reduced PDs in machine learning tasks.
problem Ignoring much of the information in persistence diagrams in machine learning pipelines.
method Developed methods to generate topological feature vectors from unreduced boundary matrices.
result Unreduced PDs can perform on par with, and sometimes outperform, fully-reduced PDs in machine learning tasks.
New approach identifies and explains errors in machine learning pipelines.
problem Challenges in identifying and explaining errors in complex machine learning pipelines.
method Uses iteration and provenance to automatically infer root causes of failures.
result Significantly improves precision and recall compared to state-of-the-art methods.
CARV reduces compute cost for downstream pipelines using diffusion models.
problem High variance in Monte Carlo estimators from diffusion models limits compute efficiency.
method CARV uses hierarchical MC estimation with amortized upstream computation and stratified-inverse-CDF.
result CARV delivers 2-3x effective compute multipliers without changing the objective.
OpTorch optimizes deep learning for resource-limited environments.
problem Resource constraints in deep learning training.
method Optimized deep learning pipelines in training time and memory.
result Achieved similar accuracy to existing libraries with reduced memory usage.
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
problem Gradient-based optimization is limited by non-differentiable components in CAE workflows.
method Surrogate models replace non-differentiable pipeline components, enabling gradient-based optimization.
result Gradient-based shape optimization possible without differentiable solvers.
Optimizes deep learning pipelines with novel algorithms for smooth and non-smooth functions.
problem Optimizing deep learning pipelines for smooth and non-smooth functions.
method Provided matching lower and upper bounds for smooth convex and non-convex functions, and developed PPRS for non-smooth convex functions.
result PPRS achieves near-linear speed-up and convergence time for non-smooth non-convex problems.
Study finds unsupervised imputation before cross-validation can reduce computational costs without significantly degrading model performance.
problem High computational costs in pipeline modeling algorithms with imputation steps.
method Empirical assessment of unsupervised imputation before vs during cross-validation.
result Reduced variance of imputation before cross-validation leads to lower overall root mean squared error.
Mathematical pipeline identifies structural homology of knotted proteins.
problem Quantification and classification of protein structures, especially knotted proteins, require noise-free and complete data.
method Developed a geometric framework using persistent homology to analyze protein structures.
result Persistent homology accurately represents structural homology of knotted proteins and identifies geometric features of protein entanglement.
AVATAR uses a surrogate model to quickly evaluate ML pipelines, saving time and resources.
problem Time-consuming evaluation of ML pipelines limits exploration of complex models.
method AVATAR employs a surrogate model to assess pipeline validity without execution.
result AVATAR accelerates ML pipeline evaluation, improving efficiency in complex scenarios.
Underspecified ML models can behave unpredictably in real-world use.
problem ML models can fail in real-world deployment due to ambiguous predictors.
method Identified underspecification as the cause, showing it affects various ML domains.
result Underspecified models can behave differently in deployment domains.
OFTER predicts multivariate time series online, outperforming baselines.
problem Mid-sized multivariate time series forecasting challenges.
method k-nearest neighbors, Generalized Regression Neural Networks, dimensionality reduction.
result OFTER outperforms state-of-the-art baselines in financial multivariate time series forecasting.
Cilia are hairlike structures protruding from nearly every cell in the body. Diseases known as ciliopathies, where cilia function is disrupted, can result in a wide spectrum of disorders. However, most techniques for assessing ciliary motion rely on manual identification and tracking of cilia; this process is laborious…
We translate ML pipelines into neural networks to optimize multiple models together.
problem Isolated training of ML pipelines limits joint optimization of multiple models.
method Propose translating ML pipelines into neural networks and fine-tuning them jointly.
result Fine-tuning translated pipelines increases final accuracy.
Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which…
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.
Study proposes a statistical testing framework for evaluating clustering pipelines.
problem Quantifying the statistical reliability of clustering results from data analysis pipelines.
method Selective inference-based statistical testing framework for clustering pipelines.
result The proposed test controls the type I error rate and is effective in validating clustering results.
Investigates fairness in pipeline models where individuals may drop out.
problem Fairness in pipeline models where individuals may drop out and subsequent stages depend on remaining individuals.
method Rigorous framework for evaluating fairness guarantees, showing that naïve auditing is insufficient and dependence must exist between stages.
result Fairness in pipelines can be arbitrary, even with just two stages, and requires dependence between stages.
PipeMare enables efficient DNN training with minimal memory and pipeline sacrifices.
problem Sacrificing hardware efficiency to maintain statistical efficiency in pipeline parallel DNN training.
method PipeMare is a simple yet robust training method that tolerates asynchronous updates during pipeline parallelism without sacrificing pipeline utilization or memory.
result PipeMare achieves up to 2.7x less memory usage or 4.3x higher pipeline utilization compared to state-of-the-art synchronous PP training techniques.
New method predicts bankruptcy by imputing missing data with granular semantics.
problem Missing data, high dimensional data, and class imbalance in bankruptcy prediction.
method Granular computing for missing data imputation with feature semantics and AI-driven pipeline.
result Efficient solution for big datasets with high imputation rates.
Modular pipeline improves stock portfolio prediction robustness under regime changes.
problem Overfitting in deep learning models for non-stationary datasets.
method Modular machine learning pipeline with GBDT models and online learning techniques.
result GBDT models with dropout show high performance, robustness, and generalisability.
The paper proposes a ML workflow for B2B sales prediction.
problem Predicting business to business sales outcomes using subjective human evaluations.
method A two-pipeline ML approach on Azure ML, including data enrichment and model training.
result ML predictions improve accuracy and increase sales value.
Automates supervised learning pipeline design with matrix and tensor factorization.
problem Designing effective supervised learning pipelines with many choices.
method Uses matrix and tensor factorization to model pipeline search space and develops greedy experiment design protocols.
result Demonstrates the effectiveness of the approach on real-world classification problems.
New model optimizes oil product distribution via pipelines.
problem Optimizing oil product distribution via pipelines.
method Discrete-time mixed integer linear programming model.
result Significant reductions in pipeline operational cost.
Paper proposes a statistical test for feature selection pipelines using selective inference.
problem Assessing the significance of feature selection pipelines in data analysis.
method Selective inference technique applied to feature selection pipelines composed of various algorithms.
result The proposed statistical test controls false positive feature selection probabilities.
A hybrid RL-Bayesian search configures machine learning pipelines efficiently.
problem Optimizing hyper-parameters in a hierarchical conditional space.
method Combines Reinforcement Learning and Bayesian Optimization.
result Outperforms state-of-the-art methods in pipeline optimization.
Pipelined Backpropagation trains large models without batches efficiently.
problem Training large models efficiently on hardware with limited batch sizes.
method Fine-grained Pipelined Backpropagation with Spike Compensation and Linear Weight Prediction.
result Fine-grained Pipelined Backpropagation with a batch size of one matches the accuracy of SGD for multiple networks.
The paper uses deep learning to speed up spatial and visual connectivity analysis.
problem Slow calculation of spatial and visual connectivity metrics.
method Investigates machine learning models and a pipeline for training them on spatial and visual connectivity analysis.
result Deep learning models significantly speed up the analysis process.
Analog method solves portfolio optimization problems faster and more efficiently.
problem Accurate covariance matrix estimation and fast optimal portfolio selection for financial applications.
method Two-step process using equilibrium propagation and analog Hopfield networks.
result Fully analog pipeline calculates optimal portfolios in energy-efficient manner.
Transformer models improve arithmetic accuracy with number decomposition.
problem Transformer models struggle with arithmetic operations without decomposition.
method Fine-tuning models with a pipeline that decomposes numbers into units, tens, etc.
result Accuracy increased by 63% in five-digit addition tasks.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
problem Understanding the dynamics of drug pipelines in global pharmaceutical industry.
method Multilayer network analysis of drug pipeline, global supply chain, and ownership data.
result Proven similarities in knowledge flows between pharmaceutical and AI industries.
Modyn automates continuous ML model training on growing datasets.
problem Continuous model retraining is costly and impractical.
method Data-centric ML platform with policies for continuous training.
result Modyn enables high throughput training with sample-level data selection.
TODS automates time series outlier detection with customizable pipelines.
problem Automated detection of outliers in time series data.
method Modular system with 70 primitives for data processing, time series analysis, and detection algorithms. GUI and data-driven searcher for pipeline design.
result Automated discovery and construction of effective outlier detection pipelines.
Hungarian text processing improved with efficient, accurate NLP pipelines.
problem Improving text processing for Hungarian language.
method Implemented in spaCy framework, focusing on efficiency and accuracy.
result Near state-of-the-art performance in all text preprocessing steps.
AL modularizes deep learning networks, improving training speed and accuracy.
problem Inefficiency of backpropagation due to backward locking and challenges in parallel computing.
method Associated Learning (AL) decomposes the network into independent components for simultaneous learning.
result Training time complexity improved from O(nl) to O(n + l), with comparable accuracy.
A rigorous ML pipeline for binary classification in biomedical studies, focusing on pancreatic cancer.
problem Handling bias in ML models for complex biomedical data.
method Customizable ML analysis pipeline with 9 algorithms, hyperparameter optimization, and thorough evaluation.
result Comparison of ML algorithms to ExSTraCS, highlighting interpretability and bias handling.
Adaptive Bayesian model optimizes machine learning pipelines.
problem Automated model selection and hyperparameter tuning for datasets.
method Combines adaptive Bayesian regression and neural network basis function with acquisition function.
result Identifies high-performance pipelines efficiently and outperforms baseline methods.
Proposes VEESA pipeline for interpreting ML models with functional data.
problem Interpreting ML models with functional data inputs for high-consequence applications.
method VEESA pipeline using efPCA and PFI.
result Provides explanation of model's use of functional data variability for prediction.
A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this obs…
Pipeline for comparing trading algorithms in finance and crypto.
problem Disconnected research and applications in algorithmic trading.
method General pipeline for designing, programming, and evaluating trading strategies.
result Systematic comparison of trading algorithms in finance and crypto.
Paper proposes a multi-phase pruning pipeline for deep ensemble learning on IIoT devices.
problem Computational limitations of IoT devices for deep learning models.
method Generates diverse pruned models, applies integer quantization, and uses clustering-based pruning.
result Significant reduction in model size (up to 90%) and improved performance (up to 7%) on IIoT devices.