Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computat…
Combines datasets to improve model fitting with small sample sizes.
problem Improving model performance with limited samples from at least one dataset.
method Proposes a novel framework called Combine datasets based on Imputation (ComImp) and PCA-ComImp for combining datasets with missing data.
result Significant improvement in model accuracy, especially with small datasets and when combined with transfer learning.
In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the perf…
Combines machine learning and data assimilation for improved forecasting.
problem Improving forecast accuracy with noisy observations.
method Sequentially learns a machine-learning model using an ensemble Kalman filter.
result The combined model achieves good forecast skill and computational efficiency.
Proposes a new technique for handling imbalanced data.
problem Data imbalance in classification tasks.
method Combines oversampling and undersampling techniques.
result CSMOUTE shows promise for handling imbalanced datasets.
Noisy Feature Mixup improves model robustness with noise-perturbed convex combinations.
problem Improving model robustness against data perturbations.
method Noise-perturbed convex combinations of pairs of data points in input and feature space.
result Improved model robustness and favorable trade-offs between accuracy and robustness.
In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the ε-tube of the regressor and also assigns reward for the data points which lie inside of the ε-tube of…
This paper evaluates methods to improve classification on imbalanced datasets.
problem Class imbalance in classification problems.
method Combination of data augmentation and ensemble learning methods.
result Combinations of data augmentation methods with ensemble learning can significantly improve classification performance.
Combines public and private data for better statistical estimation.
problem Estimating aggregate statistics from mixed data with varying privacy needs.
method Mixed estimators optimized for minimizing variance or median, using differential privacy techniques.
result Our mechanisms often outperform baseline methods in empirical tests.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
problem Ambiguous outcome labels in real-world datasets hinder accurate multi-class classification.
method Information-theoretic classification accuracy (ITCA) and search strategies (greedy, breadth-first) guide label combination.
result ITCA improves prediction accuracy and identifies ambiguous labels across diverse applications.
We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an…
Combines VaR and ES forecasts for cryptocurrency market risk management.
problem Improving tail risk forecasts in financial markets.
method Proposes semiparametric and parametric combination frameworks.
result Combined forecasts outperform individual VaR and ES forecasts.
Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables). The res…
Nonnegative matrix factorization (NMF) is a widely used linear dimensionality reduction technique for nonnegative data. NMF requires that each data point is approximated by a convex combination of basis elements. Archetypal analysis (AA), also referred to as convex NMF, is a well-known NMF variant imposing that the bas…
Combining various data types predicts S&P 500 stock prices with high accuracy.
problem Predicting S&P 500 stock prices with high accuracy.
method Combined technical, fundamental, and text data with machine learning models like Random Forest and LSTM.
result Achieved 66.18% accuracy in S&P 500 index prediction and 62.09% in individual stock prediction.
Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learning framework that first calculates the local maximum likelihood estimates (MLE) based on the data subsets, and then combines the local MLEs t…
It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally; indeed, train…
Combining ensembles and data augmentation harms model calibration.
problem Improving model calibration and robustness with ensembles and data augmentation leads to a trade-off.
method Combining ensemble averaging and data augmentation techniques.
result Combining ensembles and data augmentation can harm model calibration.
When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hyb…
Combines observational and randomized data to estimate treatment effects.
problem Estimating heterogeneous treatment effects using only observational data is biased.
method Two-step framework: learn shared structure from observational data, then data-specific structures from randomized data.
result Combining observational and randomized data improves treatment effect estimation.
We have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and one based…
New model generates unseen attribute combinations from limited data.
problem Lack of generalization in deep generative models for unseen attribute combinations.
method Introduces multilinear latent conditioning to capture multiplicative interactions.
result Demonstrates effectiveness on MNIST, Fashion-MNIST, and CelebA datasets.
A new MMD-based test combines kernels for two-sample testing without splitting data.
problem Efficiently testing if two datasets come from the same distribution without splitting data.
method Proposes a novel statistic based on Maximum Mean Discrepancy (MMD) that combines kernels, proving concentration bounds and showing data-dependent kernel selection.
result Exponential concentration bounds and improved test power compared to existing methods.
LOL method simplifies forming linear combinations of latent variables.
problem Lack of general-purpose methods for manipulating latent variables.
method Latent Optimal Linear combinations (LOL) method.
result LOL simplifies creation of expressive low-dimensional representations.
Universal approach combines OOD detection scores for robustness.
problem Combining diverse OOD detection scores for robustness.
method Quantile normalization to p-values, meta-analysis, probabilistic interpretation.
result Significantly improved robustness and performance across diverse OOD detection scenarios.
Combines experimental and historical data for robust policy evaluation.
problem Policy evaluation with mixed data sources, especially experimental vs historical.
method Linear integration of estimators from experimental and historical data, optimized for MSE minimization.
result Proposed estimators outperform traditional methods in ridesharing company data.
Divide-and-conquer method splits large data sets for efficient analysis.
problem Handling large data sets that exceed computational limits.
method Split data into smaller sets, analyze each separately, then combine results.
result Combined results provide statistical inference similar to analyzing entire data set.
Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.
problem Estimating unit-specific causal outcomes for all combinations of p interventions in a heterogeneous setting. method Latent factor model with Fourier expansion sparsity, imposing structure across units and interventions.
result Synthetic Combinations provides consistent estimation with poly(r) * (N + s^2p) observations, outperforming previous methods.
Combines Xgboost and transductive SVM for semi-supervised learning.
problem Improving semi-supervised learning performance with heterogeneous tabular data.
method Proposes an optimization-based ensemble method to adaptively combine Xgboost and transductive SVM.
result Significantly improves classification accuracy over state-of-the-art methods.
Introduces MFCNs for better understanding manifold neural networks.
problem Understanding manifold neural networks (MNNs).
method Filter-combine framework on high-dimensional point clouds, approximating manifold by sparse graph.
result Method converges to continuum limit as data points increase.
Combines historical and market data for better portfolio selection.
problem Improving portfolio selection through diverse information integration.
method Bayesian learning via Gaussian mixture model to harmonize historical and market data.
result The method enhances forecasting accuracy and robustness across various capital markets.
System learns to combine multiple model components for personalized text generation.
problem Adapting and biasing language models for personal preferences.
method Combines model-defined components, learns activation and probability combination from unlabeled text.
result Directly generates text with personalized components from unlabeled data.
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a num…
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
problem Improving forecast accuracy by combining multiple forecasts.
method Generates forecast combinations using a portfolio analogy, allowing negative weights for hedging.
result Demonstrates improved performance in weighted random forest forecasts.
Combines multiple OPE estimators into a more accurate and efficient estimate.
problem Offline evaluation of recommender systems using biased data.
method Meta-analysis of correlated OPE estimators, accounting for inter-estimator correlation.
result Improved statistical efficiency and accuracy in estimating policy value.
GOLFS selects features for clustering by combining global and local information.
problem Feature selection for high-dimensional clustering without labels.
method Combines global and local information via manifold learning and regularized self-representation.
result Improves feature selection and clustering accuracy.
When the available data of a target speaker is insufficient to train a high quality speaker-dependent neural text-to-speech (TTS) system, we can combine data from multiple speakers and train a multi-speaker TTS model instead. Many studies have shown that neural multi-speaker TTS model trained with a small amount data f…
Single neural network learns multiple tasks from combined data.
problem Can a single neural network learn multiple unrelated tasks?
method Investigates how task representations affect joint learning; uses various task encoding methods.
result Single neural network can learn multiple tasks from combined data, even when tasks are unrelated and different.
New method improves deep RL by combining emphatic weightings with replay data.
problem Improving sample efficiency and scaling model-free RL methods.
method Developed a multi-step emphatic weighting and time-reversed n-step TD learning algorithm. result The new approach reduces variance and provides convergence guarantees.
New method combines experimental and observational data for causal inference.
problem Combining internal validity of experiments and larger sample sizes of observations.
method Empirical risk minimization (ERM) framework with cross-validation.
result Efficacy and reliability demonstrated on real and synthetic data.
Survey combines FL and control for better adaptability and privacy.
problem Combining FL and control for better adaptability and privacy.
method Combining Federated Learning (FL) and control methods.
result Combining FL and control enhances adaptability, scalability, generalization, and privacy.
The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the proble…
A new algorithm improves federated learning by combining knowledge distillation and weighted combination loss.
problem Non-IID client data in federated learning leads to model drift and poor generalization.
method pFedKD-WCL integrates knowledge distillation with bi-level optimization to address non-IID challenges.
result pFedKD-WCL outperforms state-of-the-art algorithms in accuracy and convergence speed.
Unified model combines neural networks and dictionary learning for clinical predictions from brain data.
problem Predicting clinical severity from brain imaging data.
method Combines neural networks with dictionary learning to model patient-specific and shared features.
result Unified model outperforms state-of-the-art methods in predicting clinical severity.
The nonnegative matrix factorization is a widely used, flexible matrix decomposition, finding applications in biology, image and signal processing and information retrieval, among other areas. Here we present a related matrix factorization. A multi-objective optimization problem finds conical combinations of templates …
Proposes a spectral method to assess and combine multiple data visualizations.
problem Evaluating and combining the strengths of different data visualization algorithms.
method Spectral method for assessing and combining multiple visualizations.
result Proposes a visualization eigenscore to quantify relative performance and a consensus visualization.
This paper introduces a privacy-aware Bayesian approach that combines ensembles of classifiers and clusterers to perform semi-supervised and transductive learning. We consider scenarios where instances and their classification/clustering results are distributed across different data sites and have sharing restrictions.…
We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a given set of unlabeled data (a transductive setting), where prior label information is encoded as constraints on the game. The minimax solutio…