The paper investigates Active Feature Acquisition (AFA), where features are selected sequentially to maximize predictive accuracy within a budget. It introduces a theoretical certificate that decomposes the gap between a greedy Expected Information Gain minus cost (EIG-Cost) policy and the optimal hard‑budget policy into a budget‑slack term and a greedy‑suboptimality term, δ_λ. Experiments on synthetic data, standard benchmarks, and a large clinical dataset (MIMIC‑IV) show that δ_λ is close to zero in many cases, and that EIG‑Cost achieves competitive performance while using only a small fraction of the full feature cost.
The information bottleneck (IB) trades compression of an input $X$ against prediction of a target $Y$ through a multiplier $β=1/λ$, yet each operating point is usually served by training, or fully con...
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
arXiv:2604. 13130v2 Announce Type: replace Abstract: We study learning to learn through the lens of hyperparameter tuning.
arXiv:2610. 29425v1 Announce Type: new Abstract: Understanding generalization of randomized learning algorithms remains a central challenge.
arXiv:2607. 23480v1 Announce Type: new Abstract: Variational autoencoders (VAEs) transform high-dimensional, often noisy data into a compact latent representation, making downstream optimization more tractable.
arXiv:2605. 31014v2 Announce Type: replace Abstract: Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine.
The paper introduces Kernelized Parameter Sensitivity Atlases (KPSA), a method that uses a block decomposition of the empirical neural tangent kernel to estimate input‑specific parameter sensitivities efficiently. KPSA embeds parameter groups in a query space and applies a lightweight kernel to predict sensitivities for unseen inputs, achieving a high fraction of the perturbation susceptibility of full back‑propagation while dramatically reducing computation and memory usage. Experiments across vision, forecasting, and protein‑modeling tasks show that KPSA identifies sparse, depth‑spanning sensitivity circuits and improves throughput and memory efficiency in activation‑attribution problems.
arXiv:2608. 08826v1 Announce Type: new Abstract: Adaptive procedures must work without nuisance information an oracle may use, such as a gradient scale or smoothness index, and robust procedures may have to answer queries whose coordinate and inspection time are chosen only after the data are seen.
arXiv:2610.35821v1 Announce Type: cross Abstract: Statistical procedures often control expected loss at a prescribed level while leaving part of their risk budget unused. Building on the e-value repr...
arXiv:2608. 02305v1 Announce Type: new Abstract: Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget.
arXiv:2607. 05903v1 Announce Type: cross Abstract: We present K-ABENA (K-Adaptive Backpropagation with Error-based N-exclusion Algorithm), a selective gradient computation framework that reduces per-iteration training cost by excluding a fraction of low-loss ("minor") observations from the backward pass.
arXiv:2608. 25138v1 Announce Type: new Abstract: Stochastic masking, cropping, or modality removal makes deterministic reconstruction an incomplete target: one observation can admit many clean completions.