:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wang, Yi-Siang, Chen, Kuan-Yu, Den, Yu-Chen, Chang, Darby Tien-Hao
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2605.06117
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting
by: Den, Yu-Chen, et al.
Published: (2026)

LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data
by: Wang, Peng, et al.
Published: (2025)

Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
by: Hsiung, Lei, et al.
Published: (2025)

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
by: Zhou, Huichi, et al.
Published: (2025)

Ratio-Variance Regularized Policy Optimization for Efficient LLM Fine-tuning
by: Luo, Yu, et al.
Published: (2026)

Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification
by: Shi, Ruxue, et al.
Published: (2025)

SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection
by: Shen, Han, et al.
Published: (2024)

ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning
by: Shi, Jimeng, et al.
Published: (2024)

LLM Meeting Decision Trees on Tabular Data
by: Ye, Hangting, et al.
Published: (2025)

RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
by: Najafi, Saeed, et al.
Published: (2024)

Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning
by: Lee, Yu-Ang, et al.
Published: (2026)

Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation
by: Liang, Yunkai, et al.
Published: (2025)

Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning
by: Huertas, Carlos
Published: (2024)

Analyzing the Effect of Noise in LLM Fine-tuning
by: Li, Lingfang, et al.
Published: (2026)

Two-stage LLM Fine-tuning with Less Specialization and More Generalization
by: Wang, Yihan, et al.
Published: (2022)

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
by: Shi, Ruxue, et al.
Published: (2025)

Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
by: Kobalczyk, Katarzyna, et al.
Published: (2024)

Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training
by: Zhuang, Xuanyu, et al.
Published: (2025)

CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks
by: Wang, Tianlong, et al.
Published: (2024)

Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks
by: Eisenbürger, Anita, et al.
Published: (2024)

MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning
by: Li, Xuchen, et al.
Published: (2026)

DOFEN: Deep Oblivious Forest ENsemble
by: Chen, Kuan-Yu, et al.
Published: (2024)

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification
by: Yuan, Yifei, et al.
Published: (2025)

Boosting-inspired online learning with transfer for railway maintenance
by: Risca, Diogo, et al.
Published: (2025)

Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning
by: Chen, Sirui, et al.
Published: (2026)

TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning
by: Yang, Shenzhi, et al.
Published: (2025)

SPIRe: Boosting LLM Inference Throughput with Speculative Decoding
by: Neelam, Sanjit, et al.
Published: (2025)

PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning
by: Wang, Qibin, et al.
Published: (2024)

XGenBoost: Synthesizing Small and Large Tabular Datasets with XGBoost
by: Achterberg, Jim, et al.
Published: (2026)

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning
by: Perin, Gabriel J., et al.
Published: (2025)

WiSparse: Boosting LLM Inference Efficiency with Weight-Aware Mixed Activation Sparsity
by: Chen, Lei, et al.
Published: (2026)

Data Selection for LLM Alignment Using Fine-Grained Preferences
by: Zhang, Jia, et al.
Published: (2025)

Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling
by: Gao, Yunlong, et al.
Published: (2026)

Boosting Relational Deep Learning with Pretrained Tabular Models
by: Lachi, Veronica, et al.
Published: (2025)

FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning
by: Yang, Zhihan, et al.
Published: (2026)

Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers
by: Breejen, Felix den, et al.
Published: (2024)

ICPL: Few-shot In-context Preference Learning via LLMs
by: Yu, Chao, et al.
Published: (2024)

Rethinking Safety in LLM Fine-tuning: An Optimization Perspective
by: Kim, Minseon, et al.
Published: (2025)

Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance
by: Yan, Kai, et al.
Published: (2026)

From Imperative to Declarative: Towards LLM-friendly OS Interfaces for Boosted Computer-Use Agents
by: Wang, Yuan, et al.
Published: (2025)