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Tag: adaptive

  • Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents

    Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents arXiv:2603.03401v1 Announce Type: new Abstract: This paper proposes a novel parameter selection strategy for kernel-based gradient descent (KGD) algorithms, integrating bias-variance analysis with the splitting method. We introduce the concept of empirical effective dimension to quantify iteration increments in KGD, deriving an adaptive parameter selection strategy…

  • Efficient Inference after Directionally Stable Adaptive Experiments

    Efficient Inference after Directionally Stable Adaptive Experiments arXiv:2602.21478v1 Announce Type: new Abstract: We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, directional stability, which is strictly weaker than previously imposed target-agnostic stability conditions. Under directional stability, we show that estimators that…

  • Learning Multinomial Logits in $O(n log n)$ time

    Learning Multinomial Logits in $O(n log n)$ time arXiv:2601.04423v1 Announce Type: cross Abstract: A Multinomial Logit (MNL) model is composed of a finite universe of items $[n]={1,…, n}$, each assigned a positive weight. A query specifies an admissible subset — called a slate — and the model chooses one item from that slate with probability…

  • Self-adaptive weighting and sampling for physics-informed neural networks

    Self-adaptive weighting and sampling for physics-informed neural networks arXiv:2511.05452v1 Announce Type: new Abstract: Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remains challenging, often leading to limited accuracy and efficiency. In this work, we introduce a hybrid adaptive sampling and weighting…

  • Input Adaptive Bayesian Model Averaging

    Input Adaptive Bayesian Model Averaging arXiv:2510.22054v1 Announce Type: new Abstract: This paper studies prediction with multiple candidate models, where the goal is to combine their outputs. This task is especially challenging in heterogeneous settings, where different models may be better suited to different inputs. We propose input adaptive Bayesian Model Averaging (IA-BMA), a Bayesian method…

  • Adaptive randomized pivoting and volume sampling

    Adaptive randomized pivoting and volume sampling arXiv:2510.02513v1 Announce Type: new Abstract: Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the volume sampling distribution and active learning algorithms for linear regression. As consequences, this paper presents new analysis…

  • Effective continuous equations for adaptive SGD: a stochastic analysis view

    Effective continuous equations for adaptive SGD: a stochastic analysis view arXiv:2509.21614v1 Announce Type: new Abstract: We present a theoretical analysis of some popular adaptive Stochastic Gradient Descent (SGD) methods in the small learning rate regime. Using the stochastic modified equations framework introduced by Li et al., we derive effective continuous stochastic dynamics for these methods.…

  • Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices

    Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices arXiv:2507.19663v1 Announce Type: new Abstract: Solder joint reliability related to failures due to thermomechanical loading is a critically important yet physically complex engineering problem. As a result, simulated behavior is oftentimes computationally expensive. In an increasingly data-driven world, the usage of efficient data-driven design…

  • Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation

    Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation arXiv:2507.02084v1 Announce Type: new Abstract: The adaptive Iterative Soft-Thresholding Algorithm (ISTA) has been a popular algorithm for finding a desirable solution to the LASSO problem without explicitly tuning the regularization parameter $lambda$. Despite that the adaptive ISTA is a successful practical algorithm, few theoretical results exist.…

  • Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect

    Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect arXiv:2502.04673v1 Announce Type: new Abstract: Estimation and inference for the Average Treatment Effect (ATE) is a cornerstone of causal inference and often serves as the foundation for developing procedures for more complicated settings. Although traditionally analyzed in a batch setting, recent advances in martingale theory…

  • Adaptive Conformal Inference by Betting

    Adaptive Conformal Inference by Betting arXiv:2412.19318v1 Announce Type: new Abstract: Conformal prediction is a valuable tool for quantifying predictive uncertainty of machine learning models. However, its applicability relies on the assumption of data exchangeability, a condition which is often not met in real-world scenarios. In this paper, we consider the problem of adaptive conformal inference…