Investment tool predicts higher returns for Madrid real estate units.
problem Determining which real estate units have higher returns to investment in Madrid.
method Data collection from Idealista.com, descriptive statistics, return index, machine learning algorithms.
result Introduction of machine learning algorithms for rental real estate price prediction.
Improved tabular models learn better from real-world data.
problem Tabular models perform poorly on real-world datasets when trained only on synthetic data.
method Continued pre-training on a curated set of real-world datasets.
result Real-TabPFN achieves superior predictive accuracy on 29 datasets.
Real Lagrangians in toric manifolds are classified by combinatorial data.
problem Classifying real Lagrangian submanifolds in toric symplectic manifolds.
method Established a real analog of the Delzant construction.
result Real Lagrangians in toric del Pezzo surfaces have all possible diffeomorphism types.
Analyzes anomaly detection techniques for real-time streaming data.
problem Challenges in selecting effective anomaly detection techniques for real-time streaming data.
method In-depth analysis of anomaly detection techniques for real-time streaming data.
result First characterization of anomaly detection techniques across diverse fields using production data sets.
C-PP-COAD detects anomalies with limited real data, reducing dependency on real calibration data.
problem Limited real calibration data for online anomaly detection.
method Context-aware prediction-powered conformal online anomaly detection (C-PP-COAD).
result Significantly reduces dependency on real calibration data without compromising FDR control.
The paper presents a framework to quantify the trade-off between synthetic and real data.
problem Improving generalization with synthetic data when real data is scarce.
method Learning-theoretic framework leveraging algorithmic stability to derive generalization error bounds.
result Optimal synthetic-to-real data ratio minimizing expected test error as a function of Wasserstein distance.
Automated valuation model uses diverse data sources for real estate appraisal.
problem Accurate and efficient automated valuation of real estate properties.
method Web data acquisition and machine learning model combining structural and geographical data.
result The model achieves high prediction accuracy for real estate values.
Paper tackles sim-to-real domain adaptation in HEP.
problem Discrepancy between simulations and real data affects ML algorithms performance.
method Domain Adversarial Neural Network trained on HEP data.
result Ensures consistent ML algorithm performance on simulated and real HEP datasets.
Faster ICA for real data using Hessian approximations.
problem Efficiently solving ICA on large real datasets.
method Preconditioned ICA with sparse Hessian approximations.
result Superior performance on real data compared to other algorithms.
Hybrid RL combines simulated and real data for robust autonomous flight.
problem Challenges in training deep RL models for real-world robotic tasks.
method Combines real-world and simulated data to improve generalization.
result Quadrotor avoids collisions using only a monocular camera.
Machine learning detects type Ia supernovae from photometric data.
problem Detecting type Ia supernovae accurately from photometric data.
method Machine learning approach using only real observation data.
result Good results on real data from the Open Supernovae Catalog.
Study reveals how spectral bias affects learnability on real-world data.
problem Understanding how well complex datasets can be learned using kernel methods.
method Use eigenvalues and eigenfunctions from idealized data to reveal spectral bias on real-world data.
result Bound learnability on real-world data using symmetries of realistic kernels.
Improves synthetic-to-real generalization without real data.
problem Synthetic models struggle with real data generalization.
method Encourages similar ImageNet representations and automates learning rates.
result Significant improvement in synthetic-to-real generalization.
New algorithms improve federated learning accuracy and stability with real-world data.
problem Real-world data diversity and imbalance challenge federated learning.
method Developed new algorithms (FedVC, FedIR) to resample and reweight data.
result Significant improvements in accuracy and stability of federated learning.
New method ensures generated data statistics match real data distributions.
problem Ensuring generated data statistics match real data distributions in GANs.
method Added a new loss term to the generator loss function using f-divergences and kernel density estimation.
result Improved performance on synthetic and real-world datasets.
Synthetic data mimics real-world demographics for fairness testing.
problem Lack of complete, representative datasets for fairness testing.
method Construct synthetic datasets using overlapping real and separate datasets.
result Synthetic data yields consistent fairness metrics with real data.
New model detects crying in real-world settings with improved accuracy.
problem Generalization of cry detection models to real-world environments.
method Evaluated machine learning approaches on a novel dataset of real-world infant crying.
result Improved F1 score of 0.613 for crying event recognition in real-world settings.
A framework schedules hyperparameters for model-based reinforcement learning, improving performance.
problem Inadequate scheduling of hyperparameters in model-based reinforcement learning.
method Theoretical analysis and AutoMBPO framework to automatically schedule real data ratio and other hyperparameters.
result Training with hyperparameters scheduled by AutoMBPO significantly improves performance.
Sentinel analyzes Twitter streams in real-time with high accuracy.
problem Real-time analytics on fast data streams with minimal delay and high accuracy.
method Distributed system Sentinel using Apache Storm and SpaceSaving for summary storage.
result Sentinel achieves high analytical accuracy on Twitter streams.
LUNAR uses cellular automata for real-time data classification in fast streams.
problem Real-time machine learning challenges with fast data streams and concept drift.
method Streamified cellular automata approach for incremental learning and adaptation.
result Competitive performance in classification compared to established online learning methods.
Paper addresses challenges in benchmarking stream learning algorithms with real-world data.
problem Lack of publicly available non-stationary real-world datasets for evaluating stream algorithms.
method Proposes a new public data repository for benchmarking stream algorithms with real-world data.
result Mitigates problems related to dataset choice in experimental evaluation of stream classifiers and drift detectors.
Real-time event detection using human sensor data and adaptive machine learning.
problem Lack of effective real-time event detection using human sensor data.
method Combination of corroborative and probabilistic sources with drift adaptive machine learning.
result Automated continuous learning maintains high performance in the face of concept drift.
A new discriminator improves GANs by reducing the probability of real data being real.
problem Improving stability and quality of GAN-generated data.
method Introducing a relativistic discriminator that estimates the probability of real data being more realistic than fake data.
result Relativistic GANs and RaGANs generate higher quality data than non-relativistic counterparts.
Paper proposes a new framework to improve policy optimization by aligning real and simulated data distributions.
problem Inaccurate model estimation leads to performance degradation in model-based reinforcement learning.
method Introduces unsupervised model adaptation to minimize the IPM between real and simulated data distributions.
result Achieves state-of-the-art performance in sample efficiency on various continuous control tasks.
Developed a Swiss real estate portal using machine learning and public data.
problem Creating a real estate portal without domain expertise and making it accessible.
method Continuous web crawling of real estate ads, using machine learning for price estimation.
result Random Forest algorithm provides accurate rental price estimates with a median absolute relative error of 6.57 percent.
A deep neural network framework for forecasting sparse spatio-temporal data.
problem Forecasting sparse spatio-temporal data with real-time interactions.
method Coupling self-exciting point process and graph structured recurrent neural network.
result More accurate real-time forecasting of crime and traffic data.
Model predicts real-time job applicant numbers for regional economic analysis.
problem Real-time economic analysis using alternative data.
method Mixed-Frequency Aggregate Learning (MF-AGL) model.
result Model accurately predicts regional labor market conditions and economic status changes.
Satellite images improve real-estate price predictions.
problem Improving accuracy of real-estate price predictions.
method Used convolutional neural networks (CNN) to incorporate satellite image data.
result CNN model trained on satellite images and structured data performs 7% better in MAE.
Paper introduces a real-world image dataset for federated learning.
problem Lack of high-quality real-world data for federated learning.
method Created a dataset from 26 street cameras and 7 categories, implemented YOLO and Faster R-CNN.
result Benchmarked model performance, efficiency, and communication in federated learning.
TC-VAE generates robust financial time series data with causal constraints.
problem Generating realistic financial time series data with causal relationships.
method TC-VAE with causality constraint, RealNVP prior, and Wasserstein distance.
result TC-VAE loss controls discrepancy between market and generated distributions.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
Paper tackles AI driving competition challenges with mixed simulation and real-world data.
problem AI algorithms perform poorly in real-world environments compared to simulated ones and vice versa.
method Employed imitation learning on a mixed dataset to train algorithms equally well in all environments.
result Trained algorithms performed well in both simulated and real-world environments.
The Heston model optimizes portfolio management based on real market data.
problem Choosing between active and passive investment strategies.
method Calibrated Heston model to real stock market data.
result Passive strategy can outperform active strategy depending on market parameters.
This paper uses alternative data to forecast Japanese real estate performance.
problem Accurate rent and price forecasting in Japanese real estate markets.
method Created a comprehensive house price index using over 5 million transactions and economic factors.
result Alternative data variables can forecast real estate performance effectively.
Dividing local Gaussian processes improve real-time prediction efficiency.
problem Efficient online prediction for large data sets.
method Iterative data-driven division of input space for sublinear computational complexity.
result Sublinear computational complexity in real-time prediction.
Study improves accuracy of weather data for real-time building simulations.
problem Anomalous and missing weather data affect real-time building energy simulations.
method Introduces a framework for quality control of measured weather data using anomaly detection and neural network infilling.
result Neural Networks enhance the accuracy of data imputation compared to traditional methods.
RED models efficiently estimate distributions of real-valued data.
problem Efficient modeling of real-valued data with conditional dependencies.
method Semiparametric modeling using recurrent neural networks (RNNs).
result RED models achieve lower held-out negative log-likelihood than other neural network approaches.
Deep neural network detects falls from IoT sensor data.
problem Detecting irregular patterns in IoT streaming data for fall detection.
method Deep neural network model using accelerometer data.
result 98.75 percent accuracy in detecting falls.
Paper presents new dataset for disentanglement learning from physical objects.
problem Transfer of disentanglement models from synthetic to real-world data.
method Developed a dataset of physical objects with controlled variations, used a robotic arm for precise manipulation.
result Disentanglement models perform poorly on real data but selection of models and hyperparameters improves transfer.
Bayesian fusion forest combines clinical and real-world data to estimate treatment effects.
problem Estimating treatment effects on survival from mixed data sources with confounding.
method Nonparametric Bayesian fusion forest combining randomised and real-world data, relaxing unconfoundedness assumption.
result Fusion identifies treatment benefits not seen in clinical trials alone.
Develops an efficient method for real-time data analysis and visualization.
problem Challenges of analyzing high-dimensional data.
method Incremental non-linear manifold approximation using GMRA framework.
result Accurately represents non-linear manifolds with small initial samples.
FinBloom enhances LLMs for real-time financial queries.
problem Limited access to real-time financial data by LLMs.
method Developed a custom 7B parameter LLM, Financial Context Dataset, and a Financial Agent.
result Significantly improved LLMs' capability to handle dynamic financial tasks.
New RESK distributions improve robust clustering of skewed data.
problem Robustly clustering non-symmetric, heavy-tailed data clusters.
method Proposes RESK distributions and an EM algorithm with robust skew-Huber M-estimator.
result Numerical experiments confirm the effectiveness of the proposed methods.
Data set tracks real-time election results for 4 hours post-October 2019 Portuguese elections.
problem Real-time tracking of election results for predictive modeling.
method Real-time data collection and interval-based analysis.
result Data set supports various predictive modeling tasks including numerical forecasting.
Paper tackles model collapse in recursive generative models using a weighted training scheme.
problem Model collapse in recursive generative models trained on synthetic data.
method Iteratively trains models on real and synthetic data, evaluates weighted training schemes.
result Optimal weighting scheme for synthetic data follows a unified expression, revealing a trade-off with model performance.
RealUID distills matching models using real data without GANs.
problem Slow inference in matching models like diffusion and flow.
method RealUID is a universal distillation framework that incorporates real data into the distillation procedure without using GANs.
result RealUID offers a simple theoretical foundation that covers previous distillation methods for Flow Matching and Diffusion models.
Flow Matching models help generative models stay within the subspace of real data.
problem How do generative models stay within the subspace of real data?
method Flow Matching models using a learned velocity field to transform a simple prior into a complex target distribution.
result Generated samples memorize real data points and represent the sample data subspace exactly.
New method detects anomalies in time series data, especially useful for monitoring services.
problem Detecting anomalies in time series data, especially for monitoring services and cloud resources.
method Models time series of probability distributions over real values, scales to millions of time series.
result Outperforms state-of-the-art methods in detecting anomalies on various data sets.