Greedy algorithm approximates costs in interactive learning and covering problems.
problem Interactive learning and covering with response-dependent costs.
method Proposes a greedy algorithm and bounds its approximation factor.
result Greedy algorithm is near-optimal among all greedy algorithms in both settings.
Kernel models learn low-dimensional predictive subspaces from input data.
problem Learning effective feature transformations in kernel models.
method Study of a compositional kernel ridge regression model.
result Global minimizers of the objective function identify the subspace with high probability.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
problem Impact of climate change expectations on fossil fuel markets and prices.
method Dynamic, general equilibrium model of climate-change-linked transition risk.
result Climate change expectations can lead to either increased or decreased fossil fuel prices, depending on economic responses.
New algorithm profiles nuisance parameters for multidimensional deconvolution.
problem Statistical removal of instrumental effects in experimental measurements.
method Iterative algorithm called Profile OmniFold that profiles nuisance parameters.
result Illustrated with a Gaussian example, highlighting promising capabilities.
This work presents a method to localize targets in hyperspectral images using robust PCA.
problem Localizing targets in hyperspectral images with correlated signatures.
method Modeling HS images as a low-rank plus sparse component, using generalized robust PCA.
result Recovery guarantees and experimental validation show the method's effectiveness.
A new neural network model for ordinal regression.
problem Ordinal regression with non-proportional odds.
method Interpretable neural network for both continuous and discrete responses, training a non-linear neural network as a coefficient function.
result N3POM preserves interpretability while offering flexibility. When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most…
Study tackles nonlinear factor models with unknown monotone links from incomplete and noisy data.
problem Learning nonlinear factor models with unknown monotone links from incomplete and noisy data.
method Formulated as joint recovery of low-rank factors, loadings, and nonlinear link function; proposed BCD algorithm with regularization.
result Established convergence guarantees and sublinear regret bounds for link-function updates.
This paper uses Gaussian mixtures to mimic interactions in large language models.
problem Simulating interactions in large language models (LLMs) is computationally expensive.
method Developed an interacting Gaussian mixture model (GMM) system to mimic LLM interactions.
result The interacting Gaussian mixture model system can generate, exchange, and update data and parameters efficiently.
The paper tackles HS target localization using robust PCA with dictionary-based approach.
problem Localized target detection in hyperspectral images.
method Formulates HS image as low-rank + dictionary sparse, develops recovery guarantees.
result Recovery guarantees and performance analysis on real HS datasets.
New model accounts for sequential dependence in LLM reliability.
problem Uncertainty in LLM reliability assessment due to sequential interactions.
method Extended Bayesian framework with Hidden Markov Model for sequential dependence.
result Ignoring sequential dependence leads to overconfident reliability estimates.
Bayesian framework selects features and lags for time series forecasting.
problem Variable selection and lagged error term identification in time series models.
method Hierarchical Bayesian models with spike-and-slab priors, two-stage MCMC algorithm.
result Posterior selection consistency under mild conditions, improved predictive performance.
FlowSelect uses normalizing flows to control FDR in feature selection.
problem Controlled feature selection with knockoffs often fails to control false discovery rate (FDR).
method FlowSelect uses normalizing flows for accurate feature modeling and a novel MCMC-based p-value calculation to enforce knockoff properties.
result FlowSelect consistently controls FDR and demonstrates greater power compared to competing methods.
Proposes SROF for row-wise fusion in federated learning for multivariate responses.
problem Heterogeneous client models with shared variable-level structure.
method Sparse Row-wise Fusion (SROF) regularizer and RowFed algorithm.
result Empirically shows consistent error reduction and stronger variable-level cluster recovery.
New method predicts positive samples with missing labels.
problem Missing labels due to response-dependent factors.
method P(U)U-O-Mixture algorithm for joint estimation.
result Non-convex algorithm leads to optimal statistical error.
Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. The randomForestSRC packag…
Develops a new multivariate regression model for complex outcomes.
problem Flexible, heterogeneous, and residual-dependent multivariate regression problems.
method MultiVCBART framework with Graphical Horseshoe priors.
result Empirically outperforms existing models on sparse, high-dimensional datasets.
Gaussian Processes are widely used for regression tasks. A known limitation in the application of Gaussian Processes to regression tasks is that the computation of the solution requires performing a matrix inversion. The solution also requires the storage of a large matrix in memory. These factors restrict the applicat…
MFM integrates multiple evolving populations using Wasserstein manifold flows.
problem Learning dynamics of multiple interacting populations evolving over time.
method Meta Flow Matching (MFM) integrates vector fields on Wasserstein manifold using amortized flow models and GNN embeddings.
result MFM improves prediction of individual treatment responses on multi-patient single-cell drug screen data.