Cardea automates machine learning for EHRs, improving model building efficiency.
problem Lack of a trusted, open-source framework for automated machine learning in EHRs.
method Uses FHIR for data structure, AUTOML frameworks for feature engineering, model selection, and tuning, and an adaptive data assembler.
result Demonstrates framework's effectiveness on 5 prediction tasks, highlighting its flexibility and human competitiveness.
We analyze impermanent loss in AMMs and show G3Ms are simplest.
problem Understanding impermanent loss in automated market makers.
method Developed a general framework and analyzed Geometric Mean Market Makers (G3Ms).
result G3Ms have the simplest impermanent loss characteristics.
Automated framework forecasts correlated time series in minutes.
problem Forecasting correlated time series with high accuracy and efficiency.
method Data-driven iterative pruning, zero-shot search, fast parameter adaptation.
result Framework achieves state-of-the-art accuracy and is faster than existing methods.
This research compiles knowledge on decentralized exchanges with AMM protocols.
problem Improving and developing AMM-based decentralized exchanges.
method Established a general AMM framework, compared mechanics, discussed security and privacy.
result Illustrated conservation and slippage functions of AMM protocols.
Automates Bayesian signal processing algorithm design using factor graphs.
problem Designing efficient Bayesian signal processing algorithms.
method Factor graph approach and ForneyLab tool for automated inference.
result ForneyLab outperforms competitors in automated inference for state-space models.
Automated machine learning simplifies model selection and tuning.
problem Manual tuning of machine learning models by data scientists is time-consuming and requires extensive expertise.
method Review of AutoML techniques including automated feature engineering, model learning, and deep learning.
result Current AutoML techniques can significantly reduce the burden of manual tuning.
This review explores automated pain detection from facial expressions using FACS.
problem Automated detection of pain from facial expressions is challenging and underexplored.
method Examines progress in AFER and APD, focusing on FACS-based methods and deep learning.
result Limited studies on APD, highlighting the need for clinical applications.
Study proposes automated framework for REM Sleep Behaviour Disorder detection.
problem Early detection of REM Sleep Behaviour Disorder (RBD) as a predictor of Parkinson's disease.
method Automated sleep staging followed by RBD identification using a Random Forest classifier and 156 features from EEG, EOG, and EMG channels.
result Automated RBD detection achieved 96% accuracy, surpassing individual established metrics.
AutoCompete automates machine learning competition tasks.
problem Tackling machine learning competitions with minimal human intervention.
method A highly automated framework that learns from past competitions.
result Produces better results with less runtime compared to other approaches.
Framework uses LLMs to automate strategy finding in quantitative finance.
problem Brittleness of traditional deep learning models in financial applications.
method Three-stage framework with prompt-engineered LLMs, multimodal agent-based evaluation, and dynamic weight optimization.
result Robust performance in Chinese & US markets, superior risk-adjusted performance.
DRIFT uses RL to automate functional software testing efficiently.
problem Efficient and reliable automated software testing.
method DRIFT employs Q-learning with Graph Neural Networks on symbolic UI representations.
result DRIFT can robustly test software functionalities in a fully automated manner.
Survey on automating machine learning processes.
problem Automating the selection and tuning of machine learning algorithms.
method Comprehensive survey of techniques and frameworks.
result Reduction of human involvement in machine learning tasks.
This paper uses machine learning to assist automation engineers in decision making.
problem Imperfect decision making by automation engineers leads to multiple iterations and increased time for software development.
method Defined challenges and proposed solutions using machine learning for automation engineering, including code classification, finding similar code snippets, and hardware selection.
result Paragraph embedding techniques achieved an F1-score of 72% for classifying automation using code snippets, and autoencoder models for hardware recommendation achieved p@3 and p@5 of 0.79 and 0.95, respectively.
Study benchmarks automated sleep staging against human scorers, achieving human-level performance.
problem Lack of standardized comparison between human and automated sleep staging.
method Developed multi-scored datasets and a framework to compare multiple human scorers' consensus.
result Many automated methods can match human scorers' performance, with SimpleSleepNet achieving high F1 scores.
Automated HPO design using Bayesian optimization and benchmarking.
problem Designing effective hyperparameter optimization algorithms is manual and lacks systematic understanding.
method Formalized space of HPO candidates, Bayesian optimization for search, ablation analysis.
result Simple configurations can perform well in HPO, especially with right parameters.
Smart Close-out Netting aims to automate close-out netting processes.
problem Inefficiencies in close-out netting processes for financial institutions.
method Standardisation and automation of legal and regulatory processes using a data-driven framework and controlled natural language.
result Standardisation and automation can improve close-out netting processes for prudentially regulated financial institutions.
MetaDAgger improves sample efficiency and generalization in imitation learning for automated driving.
problem Improving sample efficiency and generalization in imitation learning for automated driving.
method Meta learning framework based on data set aggregation to improve generalization of imitation learning algorithms.
result Significant improvement in learning time and sample efficiency on unseen test tracks.
Framework generates pop song melodies and piano accompaniment.
problem Creating high-quality pop songs with automated systems.
method Unified strategy using three models: harmony alternation, melody generation, and melody integration.
result Generated melodies rated significantly higher than those from LSTM, and accompaniment comparable to Band in a Box.
AutoBayes automates Bayesian graph exploration for robust machine learning.
problem Learning representations invariant to nuisance variations in machine learning.
method Automated Bayesian inference framework exploring different graphical models.
result Significant performance improvement with nuisance-invariant machine learning pipelines.
This paper surveys and benchmarks AutoML frameworks.
problem Reducing the need for data scientists in building machine learning applications.
method Survey and benchmark of popular AutoML frameworks on real data sets.
result Evaluation of 137 data sets from established AutoML benchmark suits.
Modeling price dynamics in AMMs with fees using geometric Brownian motion.
problem Understanding price dynamics in AMMs with transaction fees.
method Geometric Brownian motion, local times, excursion theory.
result Derivation of time-changed representation and limiting behavior of AMM prices.
In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms. While Wang-Landau and other stochastic approximation methods have frequently been applied within the simulated tempering framework, this note demo…
CausalForge automates causal inference research with formal proofs and self-improvement.
problem Unreliable evaluation of automated research results by large language models.
method Formal proof assistant (Lean) and self-improving agentic pipeline.
result Automated research produces reliable formal proofs and artifacts.
AutoML frameworks outperform human data scientists on 7 out of 12 OpenML tasks.
problem Evaluating if AutoML can outperform human data scientists.
method Comparison of four AutoML frameworks on 12 popular OpenML datasets (6 supervised classification, 6 supervised regression).
result AutoML frameworks perform better or equal to human data scientists in 7 out of 12 tasks.
The paper establishes axioms for AMMs to ensure fair pricing and fee structures.
problem Ensuring fair and efficient pricing in decentralized finance (DeFi) AMMs.
method Formulating axioms on utility functions to characterize swap sizes and pricing oracles.
result Most existing AMMs satisfy the proposed axioms, and a new AMM is proposed with desirable properties.
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
problem Improving efficiency and safety of transportation systems with automated vehicles.
method Multi-agent graph reinforcement learning with attention mechanism.
result Automated vehicles can achieve better performance when learning to cooperate with each other.
Survey on automating geometry problem solving with large models.
problem Automating geometric problem solving with spatial understanding and logical reasoning.
method Synthesizes GPS advancements through benchmark construction, parsing, and reasoning paradigms.
result Unified analytical paradigm and emerging opportunities identified.
AdaNet automates ensemble learning with TensorFlow.
problem Automatic ensemble learning for high-quality models.
method Inspired by AdaNet algorithm, integrates TensorFlow ecosystem, offers flexible API.
result Efficiently accelerates training on various hardware.
Automated Bayesian inference for massive datasets with theoretical guarantees.
problem Intractable posterior inference in massive datasets.
method Hilbert coreset construction under log-likelihood space inner-product norm.
result Fully-automated, scalable Bayesian inference with theoretical guarantees.
Optimizes bidding strategies for LinkedIn ads across multiple platforms.
problem Optimizing automated bidding agents for dynamic online marketplaces.
method Developed a general optimization framework for buyer's interest, agnostic to auction mechanisms.
result Automatically guarantees the optimality of budget allocation across ad units and platforms.
Automates discovery of interpretable Lagrangians from data.
problem Lack of interpretable Lagrangian discovery methods.
method Data-driven machine learning algorithm to derive interpretable Lagrangians.
result Automated discovery of interpretable Lagrangians and conservation laws.
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.
Automated energy forecasts using open data.
problem Lack of timely and accurate energy forecasts.
method Automated forecasting framework using open access data.
result Comparable prediction accuracy to country-provided estimates.
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
problem Lack of uncertainty representation in deep learning methods for patient-centered healthcare decisions.
method MCU-Net framework using U-Net and Monte Carlo Dropout with four uncertainty metrics.
result MCU-Net maximizes automated performance and refers truly uncertain cases.
Automated method identifies key hyperparameters across datasets.
problem Identify the most important hyperparameters for various algorithms.
method Meta-learning across multiple datasets to determine hyperparameter importance.
result The identified hyperparameters lead to statistically significant improvements in hyperparameter optimization.
Paper introduces ML for optimal motion planning in urban traffic.
problem Variations in search efficiency due to dynamic obstacles.
method Introduces machine learning-based heuristic for optimal motion planning considering dynamic obstacles.
result Added performance consistency for real-time implementation.
Paper uses LLMs for sector allocation, showing better returns.
problem Automated trading sector allocation inefficiencies.
method Systematic analysis of macroeconomic data and sentiment.
result LLM-based sector allocation outperforms traditional strategies.
Automated testing improves deep learning model accuracy by 259.2%.
problem Ensuring robustness of deep learning models through automated testing.
method Jointly optimizing differential behaviors and neuron coverage; generating corner-cases; applying transformations and GANs.
result Deep learning model accuracy increased by 259.2% using automated generated corner cases.
AI framework for automated policy-making connects with econometrics and social choice.
problem Improving government and economic policy-making through AI.
method Social Environment Design framework integrating Reinforcement Learning, EconCS, and Computational Social Choice.
result Promotes ethical and responsible decision making by solving open problems.
Framework automates microstructure image analysis for materials science.
problem Complex microstructures in materials require automated analysis.
method Combines unsupervised and supervised learning for classification and segmentation.
result Framework can automatically segment and classify micrographs.
A new framework for generating predictive features in noisy multivariate time series.
problem Predicting noisy multivariate time series with limited user effort.
method Develops a feature programming framework based on spin-gas dynamical Ising models.
result Validated the method on synthetic and real-world datasets.
Paper proposes AI solutions for social problems in low-income areas.
problem Effective preventive measures against complex social problems in low-income regions.
method Design framework using data-driven machine learning for adaptive solutions.
result Illustrated design approach with survey data from India.
Enhances anomaly detection in financial markets using AI agents.
problem Manual verification of financial market anomalies is time-consuming and error-prone.
method A multi-agent LLM framework for automated anomaly detection.
result Framework reduces human intervention and improves efficiency and accuracy.
Framework harmonizes EHR data across institutions for better analysis.
problem Heterogeneity of medical codes and terminologies hinder EHR data analysis.
method MASH (Multi-source Automated Structured Hierarchy) uses neural optimal transport and learned hyperbolic embeddings to align and structure EHR data.
result MASH generates interpretable hierarchical graphs for unstructured local laboratory codes.
A framework for automated models to defer decisions to external decision-makers to improve accuracy and fairness.
problem Improving fairness and accuracy in systems with multiple decision-makers.
method Two-stage framework with learning to defer, considering external decision-makers' biases.
result Learning to defer can make systems more accurate and less biased, even with inconsistent or biased users.
Deep neural networks automate statistical analysis for big data.
problem Challenges in applying statistical analysis to big data.
method Constructing CNNs for automatic model selection and parameter estimation.
result CNNs demonstrate excellent performance in automatic model selection and estimation.
AutoRec automates deep recommendation models using AutoML.
problem Adapting to changing data and tasks in recommender systems.
method AutoML platform for model search and hyperparameter tuning.
result Identifies models similar to the best model without prior knowledge.
Automated rock fragmentation assessment using deep learning and spatial statistics.
problem Assessing post-blast rock fragmentation in real-time.
method Fine-tuned YOLO12l-seg model for instance segmentation, followed by spatial statistics.
result Framework accurately assesses rock fragmentation patterns in real-time.