Tag: online
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Online Partitioned Local Depth for semi-supervised applications
Online Partitioned Local Depth for semi-supervised applications arXiv:2512.15436v1 Announce Type: new Abstract: We introduce an extension of the partitioned local depth (PaLD) algorithm that is adapted to online applications such as semi-supervised prediction. The new algorithm we present, online PaLD, is well-suited to situations where it is a possible to pre-compute a cohesion network from…
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Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy
Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy arXiv:2510.06515v1 Announce Type: new Abstract: Online matching problems arise in many complex systems, from cloud services and online marketplaces to organ exchange networks, where timely, principled decisions are critical for maintaining high system performance. Traditional heuristics in these settings are simple and interpretable but typically…
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Benefits of Online Tilted Empirical Risk Minimization: A Case Study of Outlier Detection and Robust Regression
Benefits of Online Tilted Empirical Risk Minimization: A Case Study of Outlier Detection and Robust Regression arXiv:2509.15141v1 Announce Type: new Abstract: Empirical Risk Minimization (ERM) is a foundational framework for supervised learning but primarily optimizes average-case performance, often neglecting fairness and robustness considerations. Tilted Empirical Risk Minimization (TERM) extends ERM by introducing an exponential tilt…
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Online Conformal Selection with Accept-to-Reject Changes
Online Conformal Selection with Accept-to-Reject Changes arXiv:2508.13838v1 Announce Type: new Abstract: Selecting a subset of promising candidates from a large pool is crucial across various scientific and real-world applications. Conformal selection offers a distribution-free and model-agnostic framework for candidate selection with uncertainty quantification. While effective in offline settings, its application to online scenarios, where data…
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Adaptive collaboration for online personalized distributed learning with heterogeneous clients
Adaptive collaboration for online personalized distributed learning with heterogeneous clients arXiv:2507.06844v1 Announce Type: new Abstract: We study the problem of online personalized decentralized learning with $N$ statistically heterogeneous clients collaborating to accelerate local training. An important challenge in this setting is to select relevant collaborators to reduce gradient variance while mitigating the introduced bias. To…
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Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling arXiv:2505.18327v1 Announce Type: new Abstract: Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biology. As datasets continue to grow, online inference methods have become crucial for enabling real-time decision-making without the need…
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Online Learning of Neural Networks
Online Learning of Neural Networks arXiv:2505.09167v1 Announce Type: new Abstract: We study online learning of feedforward neural networks with the sign activation function that implement functions from the unit ball in $mathbb{R}^d$ to a finite label set ${1, ldots, Y}$. First, we characterize a margin condition that is sufficient and in some cases necessary for…
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Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks arXiv:2504.10598v1 Announce Type: new Abstract: We revisit online binary classification by shifting the focus from competing with the best-in-class binary loss to competing against relaxed benchmarks that capture smoothed notions of optimality. Instead of measuring regret relative to the exact minimal binary error — a…
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Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting
Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting arXiv:2504.02518v1 Announce Type: new Abstract: Probabilistic electricity price forecasting (PEPF) is a key task for market participants in short-term electricity markets. The increasing availability of high-frequency data and the need for real-time decision-making in energy markets require online estimation methods for efficient model updating.…
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Online Selective Conformal Prediction: Errors and Solutions
Online Selective Conformal Prediction: Errors and Solutions arXiv:2503.16809v1 Announce Type: new Abstract: In online selective conformal inference, data arrives sequentially, and prediction intervals are constructed only when an online selection rule is met. Since online selections may break the exchangeability between the selected test datum and the rest of the data, one must correct for…
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Improved Online Confidence Bounds for Multinomial Logistic Bandits
Improved Online Confidence Bounds for Multinomial Logistic Bandits arXiv:2502.10020v1 Announce Type: new Abstract: In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regret. Recently, Lee & Oh (2024) established an online confidence bound for MNL models and achieved nearly…
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Online Covariance Matrix Estimation in Sketched Newton Methods
Online Covariance Matrix Estimation in Sketched Newton Methods arXiv:2502.07114v1 Announce Type: new Abstract: Given the ubiquity of streaming data, online algorithms have been widely used for parameter estimation, with second-order methods particularly standing out for their efficiency and robustness. In this paper, we study an online sketched Newton method that leverages a randomized sketching technique…
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Online Learning Algorithms in Hilbert Spaces with $beta-$ and $phi-$Mixing Sequences
Online Learning Algorithms in Hilbert Spaces with $beta-$ and $phi-$Mixing Sequences arXiv:2502.03551v1 Announce Type: new Abstract: In this paper, we study an online algorithm in a reproducing kernel Hilbert spaces (RKHS) based on a class of dependent processes, called the mixing process. For such a process, the degree of dependence is measured by various mixing…