arXiv Machine Learning
Jun 9
Partially Performative Prediction
arXiv:2606. 07890v1 Announce Type: new Abstract: Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains.
The Stability of Online Algorithms in Performative Prediction
arXiv:2602. 24207v2 Announce Type: replace Abstract: The use of algorithmic predictions in decision-making leads to a feedback loop where the models we deploy actively influence the data distributions we see, and later use to retrain on.
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arXiv Machine Learning
Jul 28
Efficient Online Conformal Selection with Limited Feedback
arXiv:2605. 14953v2 Announce Type: replace Abstract: We address the problem of conformal selection, where an agent must select a minimal subset of options to ensure that at least one ``success'' is identified with a pre-specified target probability $\phi$.
arXiv Machine Learning
Jun 19
Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem
arXiv:2603. 10184v2 Announce Type: replace-cross Abstract: Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory.
arXiv Machine Learning
Aug 4
Robust Bayesian Optimization via Tempered Posteriors
arXiv:2601. 07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function.
arXiv Machine Learning
Jun 26
Blackwell Approachability and Gradient Equilibrium are Equivalent
arXiv:2606. 27315v1 Announce Type: new Abstract: Gradient equilibrium (GEQ) is a recently introduced online optimization framework that generalizes first-order stationarity from offline optimization and abstracts problems like online conformal prediction.
arXiv Machine Learning
Jun 2
Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
arXiv:2605. 26919v2 Announce Type: replace Abstract: Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts.
arXiv Machine Learning
Jun 3
Online Learning with Gradient-Variation Interval Regret
arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.
arXiv AI
Jun 17
A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction
arXiv:2606. 17649v1 Announce Type: cross Abstract: The high cost of fine-tuning LLMs poses a significant economic barrier; pre-hoc performance prediction offers a critical solution to substantially reduce this expense.
arXiv Machine Learning
Jul 20
Retraining Seeks Stable Signals
arXiv:2607. 15623v1 Announce Type: cross Abstract: Predictive models deployed at scale influence future data, a phenomenon called performativity.
arXiv AI
Jul 14
Learning to Fine-tune Foundation Models under Resource Limitations
arXiv:2607. 10694v1 Announce Type: cross Abstract: We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device.
arXiv Machine Learning
Jul 30
Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction
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 Machine Learning
Aug 10
Online Conformal Prediction Beyond Feedback
arXiv:2608. 07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications.