HB-PVI: A Hierarchical Bayesian Personalization and Value-of-Information Framework for Complex Activity Recognition

• URL: http://arxiv.org/abs/2609.05582v1
• Date: Fri, 04 Sep 2026 15:38:07 GMT
• Title: HB-PVI: A Hierarchical Bayesian Personalization and Value-of-Information Framework for Complex Activity Recognition
• Abstract summary: Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous.<n>This study presents HB-PVI, a hierarchical Bayesian personalization and value-of-information framework.<n>It shows that value-of-information reasoning, not raw predictive accuracy, should drive personalization decisions in health-sensing applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous, and every additional calibration label has an acquisition cost. This study presents HB-PVI, a hierarchical Bayesian personalization and value-of-information framework jointly modeling participant heterogeneity, the benefit and harm of four personalization mechanisms, and the economic value of an additional label, for the 47-participant MUSIC-CAR complex-activity cohort. A leakage-safe, leave-one-participant-out evaluation combines a sequential-Monte-Carlo participant-effect updater with a Student-$t$ hierarchical gain model and a one-step expected-value-of-sample-information (EVSI) stopping rule. Adapter personalization produced small positive mean F1 gains, growing from 0.00099 at one label to 0.00198 at ten, while adapter-plus-head and prototype-residual personalization were negative on average. Under the primary practical-benefit threshold ($Δ_{\min}=0.01$) and cost setting, one-step EVSI was zero at every decision state, so the policy purchased no labels and retained population inference for all 47 participants, matching always-stop exactly (region-of-practical-equivalence probability $=1$). Relative to fixed ten-shot adapter personalization, this reduced labeling by 100\% while keeping the posterior mean F1 loss at 0.00217 (95\% credible interval, 0.00048 to 0.00389), with posterior probability 0.9992 of remaining below the 0.005 tolerance. HB-PVI was utility-optimal in 199 of 216 cost-threshold settings and in every setting at or above the primary label cost. These results argue for a population-first deployment policy whenever personalization gains are small relative to labeling, computation, and harm costs, and show that value-of-information reasoning, not raw predictive accuracy, should drive personalization decisions in health-sensing applications.

Related papers

• End-to-End Neural Shrinkage of Indefinite Pairwise Correlation Matrices for Small-Cap-Inclusive Portfolios [0.0]
  Small-cap-inclusive equity universes contain recently listed and intermittently traded securities, so enforcing a common look-back discards a substantial fraction of the available information.<n>We adapt a rotation-invariant neural covariance estimator to this setting.<n>We evaluate 26 expanding-window models from 2000 to 2025 on up to 1,500 U.S. equities in a closing-auction simulator with point-in-time selection, commissions, financing, corporate actions, and market impact.
  arXiv  Detail & Related papers  (2026-08-31T08:36:09Z)
• PAIR: Pairwise-Aware Inclusion Reweighting for Adaptive Rollout Allocation in RLVR [0.0]
  Reinforcement learning with verifiable rewards spends most of its compute generating groups of long reasoning trajectories.<n>Recent allocators reduce this cost by assigning budgets to prompts, rollouts, or tokens according to a pointwise notion of difficulty or utility.<n>We identify a statistical mismatch: the unclipped leave-one-out group-relative score gradient is not a sum of independent point contributions, but a second-order U-statistic over pairs of rollouts.
  arXiv  Detail & Related papers  (2026-08-11T19:24:59Z)
• When Individually Calibrated Models Become Collectively Miscalibrated [3.556355987197792]
  We show that individually calibrated predictors can become collectively miscalibrated when their predictions interact strategically.<n>We prove that under Brier-score-based aggregation with positively correlated beliefs, each agent's individually optimal report systematically underestimates the positive-class probability.<n>In contrast, VCG-based aggregation aligns incentives by rewarding marginal contribution, achieving dominant-strategy incentive compatibility and near-optimal performance.
  arXiv  Detail & Related papers  (2026-05-14T05:25:16Z)
• Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback [1.2362187555287152]
  Forecast evaluation in finance has relied on aggregate accuracy metrics and predictive-accuracy tests built on point-forecast errors.<n>We propose a behavioral forecast-evaluation methodology that complements accuracy tests by assessing the intermediate decision process itself.
  arXiv  Detail & Related papers  (2026-05-07T06:31:34Z)
• FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization [84.58281577727566]
  We present Future-KL Influenced Policy Optimization (FIPO), a reinforcement learning algorithm designed to overcome reasoning bottlenecks in large language models.<n>FIPO addresses this by incorporating discounted future-KL divergence into the policy update, creating a dense advantage formulation that re-weights tokens based on their influence on subsequent trajectory behavior.<n> evaluated on Qwen2.5-32B, FIPO extends the average chain-of-thought length from roughly 4,000 to over 10,000 tokens and increases AIME 2024 Pass@1 accuracy from 50.0% to a peak of 58.0%.
  arXiv  Detail & Related papers  (2026-03-20T10:24:50Z)
• $V_{0.5}$: Generalist Value Model as a Prior for Sparse RL Rollouts [81.48669089692189]
  Generalist Value Models (such as $V_0.5$) achieve pre-trained value estimation by explicitly encoding model capabilities in-context.<n>We propose $V_0.5$, which adaptively fuses the baseline predicted by such value model with the empirical mean derived from sparse rollouts.<n>$V_0.5$ significantly outperforms GRPO and DAPO, achieving faster convergence and over some 10% performance improvement.
  arXiv  Detail & Related papers  (2026-03-11T14:57:41Z)
• PAC-Private Responses with Adversarial Composition [11.108854725676006]
  PAC privacy provides instance-based privacy guarantees for arbitrary black-box functions.<n>We introduce a new algorithm that achieves adversarial composition via adaptive noise calibration.<n>Experiments show that our method achieves high utility at extremely small per-step privacy budgets.
  arXiv  Detail & Related papers  (2026-01-20T14:53:39Z)
• Unified Generation and Self-Verification for Vision-Language Models via Advantage Decoupled Preference Optimization [48.078132893679744]
  We propose a unified reinforcement learning framework that jointly learns answer generation and self-verification within a single policy.<n>ADPO achieves up to +34.1% higher verification AUC and -53.5% lower inference time, with significant gains of +2.8%/+1.4% accuracy on MathVista/MMMU, +1.9 cIoU on ReasonSeg, and +1.7%/+1.0% step success rate on AndroidControl/GUI Odyssey.
  arXiv  Detail & Related papers  (2026-01-04T11:09:33Z)
• Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning [0.9861588522527782]
  We present RLDP, the first framework to cast DP optimization itself as a closed-loop control problem amenable to modern deep reinforcement learning (RL)<n>Across more than 1,600 experiments on GPT2-small, Llama-1B, Llama-3B, and Mistral-7B, RLDP delivers perplexity reductions of 1.3-3.0.5% and an average 5.6% downstream utility gain.
  arXiv  Detail & Related papers  (2025-07-30T10:46:53Z)
• Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning [50.868594148443215]
  We propose an Uncertainty-aware Ensemble Structure (UES) to assess the utility of pseudo-labels for unlabeled samples.<n>UES is lightweight and architecture-agnostic, easily extending to various computer vision tasks, including classification and regression.
  arXiv  Detail & Related papers  (2025-03-13T02:21:04Z)
• Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity [55.29408396918968]
  We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification. Our contributions include both consistency and robustness by establishing top-$k$ consistency of LDR losses for multi-class classification. We propose a new adaptive LDR loss that automatically adapts the individualized temperature parameter to the noise degree of class label of each instance.
  arXiv  Detail & Related papers  (2021-12-30T00:27:30Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

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.