Efficient Online Conformal Selection with Limited Feedback

• URL: http://arxiv.org/abs/2605.14953v1
• Date: Thu, 14 May 2026 15:22:46 GMT
• Title: Efficient Online Conformal Selection with Limited Feedback
• Abstract summary: 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.<n>We show that the simple Adaptive Conformal Inference (ACI) update rule, when applied to the appropriate control parameter or dual variable, is both adversarially valid and efficient.
• Score: 14.326282143930769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $φ$. While traditional online conformal prediction focuses on maintaining validity for the observed sequence, minimizing the resource cost (efficiency) of such selections, especially under limited feedback, remains a significant challenge. In this work, we consider settings with the most limited ``bandit'' feedback, and demonstrate that the simple Adaptive Conformal Inference (ACI) update rule, when applied to the appropriate control parameter or dual variable, is both adversarially valid, ensuring the success target is met on average for any input sequence (and hence under distribution shifts), and stochastically efficient, achieving sublinear efficiency regret for $i.i.d.$ inputs against an appropriate stochastic benchmark. We show such guarantees under canonical models capturing bandit and semi-bandit feedback to the agent via a unifying algorithmic technique, and analytic framework involving Lyapunov functions. Our approach handles more complex settings than prior work, while requiring significantly less feedback, and our results provide a new theoretical bridge between efficient online learning with limited feedback and distribution-free uncertainty quantification.

Related papers

• Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction [5.533296779866529]
  Adaptive conformal inference (ACI) of Gibbs and Cands and its variants are the standard approach to online conformal prediction under distribution shift.<n>Existing guarantees say nothing about prediction-set size, so validity can be achieved trivially at the cost of unduly wide prediction sets.<n>We consider a unified online learning framework that simultaneously controls absolute, non-optimal coverage violation and prediction-set efficiency.
  arXiv  Detail & Related papers  (2026-07-29T07:56:59Z)
• Selective Ensemble Based on Preference-Directed Multi-Objective Bandits [90.75513823660775]
  We formalize a sequential decision problem under partially specified linear preferences.<n>We then propose a preference-directed upper confidence bound (PrefUCB) algorithm.<n> Experiments on large pre-trained model selective ensemble tasks and online asset allocation under institutional mandates validate our method.
  arXiv  Detail & Related papers  (2026-06-20T07:52:46Z)
• Reasoning Is Not Free: Robust Adaptive Cost-Efficient Routing for LLM-as-a-Judge [4.511996087821266]
  Reasoning-capable large language models (LLMs) have recently been adopted as automated judges.<n>We show that explicit reasoning substantially improves judgment accuracy on tasks requiring structured verification.<n>We propose a Robust Adaptive Cost-Efficient Routing (RACER) which selects between reasoning and non-reasoning judges.
  arXiv  Detail & Related papers  (2026-05-11T16:30:20Z)
• Towards Anytime-Valid Statistical Watermarking [63.02116925616554]
  We develop the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference.<n>Our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-19T18:32:26Z)
• On the Optimality of Tracking Fisher Information in Adaptive Testing with Stochastic Binary Responses [3.491999371287298]
  We study the problem of estimating a continuous ability parameter from sequential binary responses.<n>We propose a simple algorithm that adaptively selects questions to maximize Fisher information.<n>We prove that this Fisher-tracking strategy achieves optimal performance in both fixed-confidence and fixed-budget regimes.
  arXiv  Detail & Related papers  (2025-10-09T07:10:00Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)
• Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models [69.1820058966619]
  We tackle average-reward infinite-horizon POMDPs with an unknown transition model.<n>We present a novel and simple estimator that overcomes this barrier.
  arXiv  Detail & Related papers  (2025-01-30T22:29:41Z)
• Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer [52.09480867526656]
  We identify the source of misalignment as a form of distributional shift and uncertainty in learning human preferences.<n>To mitigate overoptimization, we first propose a theoretical algorithm that chooses the best policy for an adversarially chosen reward model.<n>Using the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines a preference optimization loss and a supervised learning loss.
  arXiv  Detail & Related papers  (2024-05-26T05:38:50Z)
• Introduction to Online Control [31.67032731719622]
  In online nonstochastic control, both the cost functions as well as the perturbations from the assumed dynamical model are chosen by an adversary.<n>The target is to attain low regret against the best policy in hindsight from a benchmark class of policies.
  arXiv  Detail & Related papers  (2022-11-17T16:12:45Z)
• Budgeted Classification with Rejection: An Evolutionary Method with Multiple Objectives [0.0]
  Budgeted, sequential classifiers (BSCs) process inputs through a sequence of partial feature acquisition and evaluation steps. This allows for an efficient evaluation of inputs that prevents unneeded feature acquisition. We propose a problem-specific genetic algorithm to build budgeted, sequential classifiers with confidence-based reject options.
  arXiv  Detail & Related papers  (2022-05-01T22:05:16Z)
• Error-based Knockoffs Inference for Controlled Feature Selection [49.99321384855201]
  We propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees.
  arXiv  Detail & Related papers  (2022-03-09T01:55:59Z)
• Linear Stochastic Bandits over a Bit-Constrained Channel [37.01818450308119]
  We introduce a new linear bandit formulation over a bit-constrained channel. The goal of the server is to take actions based on estimates of an unknown model parameter to minimize cumulative regret. We prove that when the unknown model is $d$-dimensional, a channel capacity of $O(d)$ bits suffices to achieve order-optimal regret.
  arXiv  Detail & Related papers  (2022-03-02T15:54:03Z)
• Regularized Online Allocation Problems: Fairness and Beyond [7.433931244705934]
  We introduce the emphregularized online allocation problem, a variant that includes a non-linear regularizer acting on the total resource consumption. In this problem, requests repeatedly arrive over time and, for each request, a decision maker needs to take an action that generates a reward and consumes resources. The objective is to simultaneously maximize additively separable rewards and the value of a non-separable regularizer subject to the resource constraints.
  arXiv  Detail & Related papers  (2020-07-01T14:24:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.