high-dimensional

This paper introduces a low-rank degree-two density projection method for nonparametric changepoint detection in high dimensions, using matrix mean estimation to handle distributional changes without parametric assumptions.

This paper proposes a method to accelerate DNN training for high-dimensional functions by introducing contextual features, including rank-1 features and tensor features from decomposed pretrained DNNs, using randomized tensor decomposition to reduce storage costs by orders of magnitude.

This paper presents a scalable method for end-to-end learning of safe feedback controllers in high-dimensional systems by embedding a control barrier function-based safety filter as an optimization layer, using operator splitting and Jacobian-Free Backpropagation to overcome computational bottlenecks.

Proposes a method to embed graphs in high-dimensional space and search for informative 2D viewpoints that optimize aesthetic and readability metrics, enabled by a novel differentiable surrogate for edge crossings. Introduces an interactive system, DataFly, for exploring multiple candidate viewpoints.

This paper introduces a model-free deep learning method for solving high-dimensional nonlinear partial differential equations with unknown coefficients, using zeroth-order derivative estimators derived from perturbed Monte Carlo trajectories. The approach avoids automatic differentiation, provides theoretical error bounds, and demonstrates competitive performance in numerical experiments.

Introduces HDD-RoPE, an extension of rotary positional embeddings that uses high-dimensional chunks and data-dependent rotation rates, showing faster convergence on TinyStories compared to xPos.

Proposes GRACE, a method that combines constraint-based skeleton with gated refinement using L0 regularization for efficient and accurate causal edge discovery in high-dimensional time series. It outperforms existing methods in F1 and speed, demonstrated on synthetic and real-world river flow data.

Proposes Gaussian process latent factor regression (GPLFR) for low-data, high-dimensional output problems, demonstrating it with a spatially resolved emulator of global climate models for rocky exoplanets.

Introduces a perturbative approach for nonparametric instrumental variable estimation that extends kernel ridge methods with higher-order corrections, achieving up to 99% reduction in prediction error in high-dimensional settings.

This paper introduces Unicorn, a framework for scalable multi-dataset pretraining on high-dimensional time series that decouples correlation modeling from specific channel identities via a latent prototype codebook, enabling domain transfer and few-shot forecasting.

Proposes two-parameter flows to learn the dynamics of high-dimensional probability densities from unlabeled samples, using conditional flow matching to extract physics-time velocity fields.

This paper introduces PACE-GGM, a differentially private method for covariance estimation that adaptively selects and measures the most informative entries of the empirical covariance matrix, using Gaussian graphical models for reconstruction. It shows improved estimation error over baselines on real-world data, especially in high-dimensional settings.

The paper introduces Kernel Discovery, an LLM-driven evolutionary framework for high-dimensional Bayesian optimization that searches a broader kernel space and achieves state-of-the-art results on benchmarks.

Asymmetric Flow Modeling (AsymFlow) restricts noise prediction to low-rank subspaces for efficient high-dimensional flow-based generation, achieving state-of-the-art results on ImageNet and text-to-image tasks by fine-tuning from latent flow models.