COBRAS-TS improves semi-supervised clustering for time series.
problem Semi-supervised clustering of time series data.
method Adapting COBRAS for time series data with semi-supervision.
result COBRAS-TS outperforms existing methods in time series clustering.
COBRAS uses super-instances to quickly cluster data with user queries.
problem Clustering data with user-defined pairwise constraints efficiently.
method Top-down construction of super-instances, iterative refinement based on user queries.
result COBRAS produces high-quality clusterings at fast run times.
COBRA efficiently clusters data using pairwise constraints with minimal queries.
problem Clustering datasets with user-defined constraints.
method Over-clusters data with K-means, then merges clusters based on constraints.
result COBRA outperforms state-of-the-art methods in clustering quality and runtime.
Paper introduces COBRA variations for multivariate time series forecasting.
problem Multivariate time series forecasting challenges.
method Innovative COBRA variations, data preprocessing, Bayesian optimisation vs. grid search.
result Proposed methodologies outperform state-of-the-art models.
SACOBRA solves complex optimization problems in fewer than 500 evaluations.
problem High-dimensional constrained optimization with limited function evaluations and no analytical function information.
method Surrogate modeling and adaptive parameter control.
result SACOBRA consistently outperforms COBRA with fewer function evaluations and no parameter tuning.
KernelCobra combines multiple predictors using a kernel to improve prediction performance.
problem Combining multiple predictors for better classification and regression.
method Kernel-based ensemble learning using COBRA algorithm with kernel smoothing.
result KernelCobra outperforms COBRA in classification and regression tasks.
COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.
problem Ensuring truthful reporting from strategic agents in online platforms.
method Proposes COBRA, an algorithm for contextual bandits involving strategic agents that disincentivizes strategic behavior.
result COBRA achieves sub-linear regret guarantee and incentive compatibility without monetary incentives.
COBRA reduces modality gap in cross-modal tasks.
problem Joint embedding spaces fail to sufficiently reduce modality gap in multi-modal tasks.
method COBRA trains image and text modalities in a joint fashion using Contrastive Predictive Coding and Noise Contrastive Estimation.
result COBRA significantly reduces the modality gap and generates robust joint-embedding space.
The paper predicts survival functions using random survival trees and concordance maximization.
problem Predicting conditional survival functions in right-censored data.
method The approach combines regression strategies with random survival trees and maximizes concordance.
result The proposed weighted predictor outperforms the usual survival cobra in terms of concordance.
Paper proposes a new combined regression strategy for conditional survival prediction.
problem Improving survival prediction accuracy using conditional survival function.
method Uses regression-based weak learners with area-norm proximity measure to create an ensemble technique.
result The proposed model outperforms Random Survival Forest and selects important variables effectively.
In the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high dimensional genomic data motivates this work. In this context, biclust…
Bayesian analysis uncovers flux couplings in metabolic networks.
problem Uncertainty and unrealistic assumptions in traditional flux analysis methods.
method Introduces Bayesian metabolic flux analysis to model reactions probabilistically and infer flux distributions.
result Reveals informative flux couplings and more unobserved fluxes in metabolic networks.