Adapt Only When It Pays: Budgeted Decision-Loss Priority for Delayed Online Time-Series Adaptation

• URL: http://arxiv.org/abs/2606.25068v1
• Date: Tue, 23 Jun 2026 18:22:22 GMT
• Title: Adapt Only When It Pays: Budgeted Decision-Loss Priority for Delayed Online Time-Series Adaptation
• Abstract summary: We introduce ADOWIP: a residual-adapter framework with sealed delay queues, exact budget accounting, and auditable update telemetry.<n>Its main scheduler is an observed decision-loss priority gate that updates only after feedback is revealed.<n>We prove hard-budget feasibility, projected-OGD regret for a convex linear accepted-update subproblem, and stability plus conditional finite-sample gate-selection statements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online time-series forecasters receive labels only after horizon-dependent delays, while every adaptation step spends limited compute. We study when an online learner should update, not how to adapt at every opportunity, and introduce ADOWIP: a residual-adapter framework with sealed delay queues, exact budget accounting, and auditable update telemetry. Its main scheduler is an observed decision-loss priority gate that updates only after feedback is revealed, when downstream loss, optionally penalized by prediction MSE, exceeds a calibrated empirical quantile and budget remains. We prove hard-budget feasibility, projected-OGD regret for a convex linear accepted-update subproblem, and stability plus conditional finite-sample gate-selection statements. On public ETT capacity-planning tasks, a frozen calibration/evaluation split selects a gate that lowers held-out decision loss against always, fixed-period, and drift-triggered exact-update baselines under matched compute. Secondary threshold/load-index ETT suites are mixed: 33 of 41 selected contrasts clear the stricter cross-artifact Holm family, and the 8 nonpassing rows are explicitly excluded from primary claims. The same protocol improves an external UCI Bike capacity proxy with 20/0 held-out wins, and a fixed gate passes three full-year Capital Bikeshare station-rebalancing contrasts. Probe-based and finance experiments remain negative, delimiting the current scope of decision-prioritized adaptation.

Related papers

• Adaptive Conformal Inference Under Delayed Feedback: Coverage Guarantees and a Delay-to-Memory Diagnostic [0.0]
  We study how the effect of delay depends on the persistence of the residual process.<n>Overall, the results show that the effect of forecast delay is best understood relative to the time scale over which past residual information remains relevant.
  arXiv  Detail & Related papers  (2026-09-07T09:10:48Z)
• CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules [1.0390646006106319]
  We establish a deterministic target-transfer theorem for uniform, nonnested additive public cuts.<n>A component-resolved form of the theorem converts an execution trace into a numerical exploitability certificate.<n>A depth study locates a crossover between 32 and 64 full-cut outcome budgets, after which complete coverage dominates.
  arXiv  Detail & Related papers  (2026-08-14T09:28:35Z)
• 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)
• Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning [33.56197101721029]
  Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization.<n>The resulting staleness is an inevitable byproduct, compounded jointly by policy lag, engine delays, and mixture-of-experts routing.<n>We introduce the Staleness-Adaptive Trust Region (SAT), which uses the detached sampled log-ratio as a practical staleness proxy.
  arXiv  Detail & Related papers  (2026-07-21T05:27:50Z)
• Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions [10.945117945979566]
  We study reliable selection for black-box CATE predictors.<n>We construct doubly robust proxy errors from pseudo-outcomes.<n>We propose Denoised Conformal Alignment.
  arXiv  Detail & Related papers  (2026-07-03T10:00:28Z)
• Capacity-Constrained Online Convex Optimization with Delayed Feedback [58.59385794080679]
  We study delayed online convex optimization (OCO) under a hard capacity constraint.<n>We find that for first-order feedback, capacity $C = (log T)$ suffices to recover standard delayed OCO rates up to logarithmic factors.<n>For bandit feedback, the regret rates are modulated by powers of $(1 + _textmax/C)$, where $_textmax$ is the maximum number of pending observations at any time.
  arXiv  Detail & Related papers  (2026-06-10T06:37:06Z)
• STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting [6.053929364291382]
  Test-Time Adaptation (TTA) aims to improve time series forecasting under distribution shifts by using limited observations revealed during inference.<n>Existing methods can suffer from weak identifiability, error accumulation, and unstable long-horizon corrections when the revealed prefix is sparse or contaminated.
  arXiv  Detail & Related papers  (2026-05-08T16:58:10Z)
• SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data [49.370849653460716]
  We propose SOPE, an algorithm that uses an actor-aligned Off-Policy Policy Evaluation signal as an automated early-stopping mechanism.<n> evaluated on 25 continuous control tasks from the Minari benchmark suite.<n>SOPE improves baseline performance by up to 45.6% while reducing the required TFLOPs by up to 22x.
  arXiv  Detail & Related papers  (2026-05-07T08:32:09Z)
• Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration [0.13543803103181612]
  We study calibration scheduling as a predictive maintenance problem.<n>We adapt the NASA C-MAPSS benchmark into a calibration setting.<n>We show that condition-based calibration can be framed as a joint forecasting and decision problem.
  arXiv  Detail & Related papers  (2026-03-19T06:32:01Z)
• LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs [61.06744611795341]
  Medical vision-language models (VLMs) are strong zero-shot recognizers for medical imaging.<n>We propose texttttextbfLATA (Laplacian-Assisted Transductive Adaptation), a textittraining- and label-free refinement.<n>texttttextbfLATA sharpens zero-shot predictions without compromising exchangeability.
  arXiv  Detail & Related papers  (2026-02-19T16:45:38Z)
• LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
  Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
  arXiv  Detail & Related papers  (2025-12-01T11:27:09Z)
• 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)
• Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration [52.70324949884702]
  We quantify the excess risk incurred using approximate posterior probabilities in batch binary decision-making.<n>We identify regimes where recalibration alone addresses most of the regret, and regimes where the regret is dominated by the grouping loss.<n>On NLP experiments, we show that these quantities identify when the expected gain of more advanced post-training is worth the operational cost.
  arXiv  Detail & Related papers  (2025-03-23T10:52:36Z)
• Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading [52.499838151272016]
  This work introduces a new method to calibrate the HPD sets produced by PLS with the aim of guaranteeing long-term coverage requirements.<n>The proposed method, referred to as online conformal prediction-PLS (OCP-PLS), assumes sporadic feedback from cloud to edge.<n>The validity of OCP-PLS is verified via experiments that bring insights into trade-offs between coverage, prediction set size, and cloud usage.
  arXiv  Detail & Related papers  (2025-03-18T17:30:26Z)
• CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control [4.137346786534721]
  It is important to control the real-time false coverage-statement rate (FCR) which measures the overall miscoverage level.<n>We develop a general framework named CAP that performs an adaptive pick rule on historical data to construct a calibration set.<n>We prove that CAP can achieve an exact selection-conditional coverage guarantee in the finite-sample and distribution-free regimes.
  arXiv  Detail & Related papers  (2024-03-12T15:07:20Z)

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.