LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees

• URL: http://arxiv.org/abs/2602.22432v1
• Date: Wed, 25 Feb 2026 21:44:19 GMT
• Title: LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
• Abstract summary: LoBoost is a model-native local conformal method that reuses the fitted ensemble's leaf structure to define multiscale calibration groups.<n>Experiments show competitive interval quality, improved test MSE on most datasets, and large calibration speedups.
• Score: 3.4988194836573894
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal prediction provides distribution-free marginal coverage, yet split conformal uses a single global residual quantile and can be poorly adaptive under heteroscedasticity. Methods that improve adaptivity typically fit auxiliary nuisance models or introduce additional data splits/partitions to learn the conformal score, increasing cost and reducing data efficiency. We propose LoBoost, a model-native local conformal method that reuses the fitted ensemble's leaf structure to define multiscale calibration groups. Each input is encoded by its sequence of visited leaves; at resolution level k, we group points by matching prefixes of leaf indices across the first k trees and calibrate residual quantiles within each group. LoBoost requires no retraining, auxiliary models, or extra splitting beyond the standard train/calibration split. Experiments show competitive interval quality, improved test MSE on most datasets, and large calibration speedups.

Related papers

• GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection [4.969458805037944]
  On-device training of deep neural networks is constrained by the computational and memory costs of large-scale datasets.<n>We propose GLOBE, a trajectory-aligned coreset selection framework that formulates sample selection as a globally optimized sparse weighting problem.
  arXiv  Detail & Related papers  (2026-08-03T09:01:29Z)
• Adaptive Patching Is Harder Than It Looks For Time-Series Forecasting [11.469701541304737]
  Under conditions a content-adaptive patching operator should outperform a tuned uniform one.<n>We show that adaptive patching should be evaluated against a tuned uniform baseline.
  arXiv  Detail & Related papers  (2026-06-02T15:49:06Z)
• Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration [76.08899010904652]
  CapCal is a training-free framework that mechanically decouples positional bias from ranking decisions.<n>It achieves superior performance among training-free methods while preserving single-pass efficiency.
  arXiv  Detail & Related papers  (2026-04-11T10:47:22Z)
• Learnable Chernoff Baselines for Inference-Time Alignment [64.81256817158851]
  We introduce Learnable Chernoff Baselines as a method for efficiently and approximately sampling from exponentially tilted kernels.<n>We establish total-variation guarantees to the ideal aligned model, and demonstrate in both continuous and discrete diffusion settings that LCB sampling closely matches ideal rejection sampling.
  arXiv  Detail & Related papers  (2026-02-08T00:09:40Z)
• Hinge Regression Tree: A Newton Method for Oblique Regression Tree Splitting [18.562483381753804]
  We present the Hinge Regression Tree (HRT), which reframes each split as a non-linear least-squares problem over two linear predictors.<n>We analyze this node-level optimization and, for a backtracking line-search variant, prove that the local objective decreases monotonically and converges.<n>We show on synthetic and real-world benchmarks that HRT matches or outperforms single-tree baselines with more compact structures.
  arXiv  Detail & Related papers  (2026-02-05T06:49:01Z)
• From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models [90.45197506653341]
  Large reasoning models generate intermediate reasoning traces before producing final answers.<n> aligning LRMs with human preferences, a crucial prerequisite for model deployment, remains underexplored.<n>A common workaround optimized a single sampled trajectory, which introduces substantial gradient variance from trace sampling.
  arXiv  Detail & Related papers  (2025-10-06T17:58:01Z)
• Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing [58.52119063742121]
  Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for improving the model performance.<n>This paper addresses the question of how to optimally combine the model's predictions and the provided labels.<n>Our main contribution is the derivation of the Bayes optimal aggregator function to combine the current model's predictions and the given labels.
  arXiv  Detail & Related papers  (2025-05-21T07:16:44Z)
• Soft regression trees: a model variant and a decomposition training algorithm [0.24578723416255752]
  We propose a new variant of soft multivariate regression trees (SRTs) where, for every input vector, the prediction is defined as a linear regression associated to a single leaf node.<n>SRTs exhibit the conditional computational property, i.e., each prediction depends on a small number of nodes.<n> Experiments on 15 wellknown datasets indicate that our SRTs and decomposition algorithm yield higher accuracy and robustness compared with traditional soft regression trees.
  arXiv  Detail & Related papers  (2025-01-10T13:06:36Z)
• Semiparametric conformal prediction [79.6147286161434]
  We construct a conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores.<n>We flexibly estimate the joint cumulative distribution function (CDF) of the scores.<n>Our method yields desired coverage and competitive efficiency on a range of real-world regression problems.
  arXiv  Detail & Related papers  (2024-11-04T14:29:02Z)
• Adaptive Uncertainty Quantification for Generative AI [0.0]
  Mirroring split-conformal inference, we design a wrapper around a black-box algorithm which calibrates conformity scores.<n> adaptive partitioning is achieved by fitting a robust regression tree to the conformity scores on the calibration set.<n>Unlike traditional split-conformal inference, adaptive splitting and within-group calibration yields adaptive bands which can stretch and shrink locally.
  arXiv  Detail & Related papers  (2024-08-16T19:37:33Z)
• Regression Trees for Fast and Adaptive Prediction Intervals [2.6763498831034043]
  We present a family of methods to calibrate prediction intervals for regression problems with local coverage guarantees. We create a partition by training regression trees and Random Forests on conformity scores. Our proposal is versatile, as it applies to various conformity scores and prediction settings.
  arXiv  Detail & Related papers  (2024-02-12T01:17:09Z)
• An information criterion for automatic gradient tree boosting [0.0]
  An information theoretic approach to learning the complexity of classification and regression trees and the number of trees in gradient tree boosting is proposed. The optimism (test loss minus training loss) of the greedy leaf splitting procedure is shown to be the maximum of a Cox-Ingersoll-Ross process. Relative to xgboost, speedups on numerical experiments ranges from around 10 to about 1400, at similar predictive-power measured in terms of test-loss.
  arXiv  Detail & Related papers  (2020-08-13T14:24:27Z)
• Distribution-free binary classification: prediction sets, confidence intervals and calibration [106.50279469344937]
  We study three notions of uncertainty quantification -- calibration, confidence intervals and prediction sets -- for binary classification in the distribution-free setting. We derive confidence intervals for binned probabilities for both fixed-width and uniform-mass binning. As a consequence of our 'tripod' theorems, these confidence intervals for binned probabilities lead to distribution-free calibration.
  arXiv  Detail & Related papers  (2020-06-18T14:17:29Z)
• Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation [77.7420231319632]
  We adapt contextual generation of categorical sequences to a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control. We also demonstrate the use of correlated MC rollouts for binary-tree softmax models, which reduce the high generation cost in large vocabulary scenarios.
  arXiv  Detail & Related papers  (2019-12-31T03:01:55Z)

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.