:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Qian, Jian, Ge, Shu
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.18866
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
by: Xu, Wanghan, et al.
Published: (2024)

Leave-One-Out Prediction for General Hypothesis Classes
by: Qian, Jian, et al.
Published: (2026)

XGrad: Boosting Gradient-Based Optimizers With Weight Prediction
by: Guan, Lei, et al.
Published: (2023)

ResBit: Residual Bit Vector for Categorical Values
by: Fuchi, Masane, et al.
Published: (2023)

Support Vector Boosting Machine (SVBM): Enhancing Classification Performance with AdaBoost and Residual Connections
by: Lian, Junbo Jacob
Published: (2024)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
by: Meunier, Dimitri, et al.
Published: (2024)

Predicting Long Term Sequential Policy Value Using Softer Surrogates
by: Nam, Hyunji, et al.
Published: (2024)

Boosted Conformal Prediction Intervals
by: Xie, Ran, et al.
Published: (2024)

Stabilizing Estimates of Shapley Values with Control Variates
by: Goldwasser, Jeremy, et al.
Published: (2023)

Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks
by: Valle, Marcos Eduardo
Published: (2023)

Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets
by: Dan, Bozhi, et al.
Published: (2025)

LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
by: Santos, Vagner, et al.
Published: (2026)

MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models
by: Tziorvas, Antonios, et al.
Published: (2026)

Efficient Sampling for Data-Driven Frequency Stability Constraint via Forward-Mode Automatic Differentiation
by: Xu, Wangkun, et al.
Published: (2024)

$α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
by: Schnoor, Ekkehard, et al.
Published: (2026)

Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
by: Matsubara, Takuo
Published: (2024)

VP-VAE: Rethinking Vector Quantization via Adaptive Vector Perturbation
by: Zhai, Linwei, et al.
Published: (2026)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
by: Li, Zhu, et al.
Published: (2023)

Internal Value Alignment in Large Language Models through Controlled Value Vector Activation
by: Jin, Haoran, et al.
Published: (2025)

Universal Approximation Theorem for Vector- and Hypercomplex-Valued Neural Networks
by: Valle, Marcos Eduardo, et al.
Published: (2024)

Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
by: Wang, Wenbin, et al.
Published: (2026)

Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts
by: Datta, Shounak, et al.
Published: (2017)

Predicting Where Steering Vectors Succeed
by: Billa, Jayadev
Published: (2026)

Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions
by: Zheng, Wenjie, et al.
Published: (2022)

ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via $α$-$β$-Divergence
by: Wang, Guanghui, et al.
Published: (2025)

Prediction-Powered E-Values
by: Csillag, Daniel, et al.
Published: (2025)

Prediction via Shapley Value Regression
by: Alkhatib, Amr, et al.
Published: (2025)

Robustifying and Boosting Training-Free Neural Architecture Search
by: He, Zhenfeng, et al.
Published: (2024)

Path-Based Gradient Boosting for Graph-Level Prediction
by: Meggio, Claudio, et al.
Published: (2026)

Gradient Boosting Decision Tree with LSTM for Investment Prediction
by: Yu, Chang, et al.
Published: (2025)

$α$-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models
by: Cheng, Chaoran, et al.
Published: (2025)

Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics
by: Bowen, Tian, et al.
Published: (2024)

HT-GNN: Hyper-Temporal Graph Neural Network for Customer Lifetime Value Prediction in Baidu Ads
by: Zhao, Xiaohui, et al.
Published: (2026)

Generative Conformal Prediction with Vectorized Non-Conformity Scores
by: Zheng, Minxing, et al.
Published: (2024)

Set Valued Predictions For Robust Domain Generalization
by: Tsibulsky, Ron, et al.
Published: (2025)

E-Values Expand the Scope of Conformal Prediction
by: Gauthier, Etienne, et al.
Published: (2025)

Value-Based Deep RL Scales Predictably
by: Rybkin, Oleh, et al.
Published: (2025)

Prediction Models That Learn to Avoid Missing Values
by: Stempfle, Lena, et al.
Published: (2025)

Aligning the Evaluation of Probabilistic Predictions with Downstream Value
by: Shahroudi, Novin, et al.
Published: (2025)

Vector-Valued Distributional Reinforcement Learning Policy Evaluation: A Hilbert Space Embedding Approach
by: Mohammadi, Mehrdad, et al.
Published: (2026)