Mixture Data for Training Cannot Ensure Out-of-distribution Generalization
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Songming, Luo, Yuxiao, Wang, Qizhou, Chi, Haoang, Chen, Xiaofeng, Han, Bo, Li, Jinyan |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ShiftKD: Benchmarking Knowledge Distillation under Distribution Shift
by: Zhang, Songming, et al.
Published: (2023)
by: Zhang, Songming, et al.
Published: (2023)
Causal Representation Learning with Optimal Compression under Complex Treatments
by: Liang, Wanting, et al.
Published: (2026)
by: Liang, Wanting, et al.
Published: (2026)
Adaptive-Boundary-Clipping GRPO: Ensuring Bounded Ratios for Stable and Generalizable Training
by: Liu, Chi, et al.
Published: (2026)
by: Liu, Chi, et al.
Published: (2026)
AEGIS: Adversarial Target-Guided Retention-Data-Free Robust Concept Erasure from Diffusion Models
by: Li, Fengpeng, et al.
Published: (2026)
by: Li, Fengpeng, et al.
Published: (2026)
M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling
by: Luo, Yuxiao, et al.
Published: (2025)
by: Luo, Yuxiao, et al.
Published: (2025)
Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data
by: Zhang, Jie, et al.
Published: (2024)
by: Zhang, Jie, et al.
Published: (2024)
On the Learnability of Out-of-distribution Detection
by: Fang, Zhen, et al.
Published: (2024)
by: Fang, Zhen, et al.
Published: (2024)
Towards Understanding Valuable Preference Data for Large Language Model Alignment
by: Zhang, Zizhuo, et al.
Published: (2025)
by: Zhang, Zizhuo, et al.
Published: (2025)
What Is Preference Optimization Doing, and Why?
by: Wang, Yue, et al.
Published: (2025)
by: Wang, Yue, et al.
Published: (2025)
BOOD: Boundary-based Out-Of-Distribution Data Generation
by: Liao, Qilin, et al.
Published: (2025)
by: Liao, Qilin, et al.
Published: (2025)
Improving Graph Out-of-distribution Generalization Beyond Causality
by: Xu, Can, et al.
Published: (2024)
by: Xu, Can, et al.
Published: (2024)
Reasoned Safety Alignment: Ensuring Jailbreak Defense via Answer-Then-Check
by: Cao, Chentao, et al.
Published: (2025)
by: Cao, Chentao, et al.
Published: (2025)
Dual Test-time Training for Out-of-distribution Recommender System
by: Yang, Xihong, et al.
Published: (2024)
by: Yang, Xihong, et al.
Published: (2024)
FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
by: Liao, Xinting, et al.
Published: (2024)
by: Liao, Xinting, et al.
Published: (2024)
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
by: Liu, Yibing, et al.
Published: (2023)
by: Liu, Yibing, et al.
Published: (2023)
Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data
by: Zhang, Jiaming, et al.
Published: (2026)
by: Zhang, Jiaming, et al.
Published: (2026)
Mixture of Link Predictors on Graphs
by: Ma, Li, et al.
Published: (2024)
by: Ma, Li, et al.
Published: (2024)
Concept Matching with Agent for Out-of-Distribution Detection
by: Lee, Yuxiao, et al.
Published: (2024)
by: Lee, Yuxiao, et al.
Published: (2024)
Spurious Feature Diversification Improves Out-of-distribution Generalization
by: Lin, Yong, et al.
Published: (2023)
by: Lin, Yong, et al.
Published: (2023)
Feature Protection For Out-of-distribution Generalization
by: Tan, Lu, et al.
Published: (2024)
by: Tan, Lu, et al.
Published: (2024)
SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models
by: Tang, Anke, et al.
Published: (2024)
by: Tang, Anke, et al.
Published: (2024)
Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering
by: Kwok, Tung Sum Thomas, et al.
Published: (2025)
by: Kwok, Tung Sum Thomas, et al.
Published: (2025)
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models
by: Belenki, Lior, et al.
Published: (2025)
by: Belenki, Lior, et al.
Published: (2025)
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
by: Wang, Qizhou, et al.
Published: (2024)
by: Wang, Qizhou, et al.
Published: (2024)
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
by: Huang, Zhuo, et al.
Published: (2026)
by: Huang, Zhuo, et al.
Published: (2026)
A Sober Look at the Robustness of CLIPs to Spurious Features
by: Wang, Qizhou, et al.
Published: (2024)
by: Wang, Qizhou, et al.
Published: (2024)
Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
by: Yang, Puning, et al.
Published: (2026)
by: Yang, Puning, et al.
Published: (2026)
$ϕ$-Balancing for Mixture-of-Experts Training
by: Chen, Lizhang, et al.
Published: (2026)
by: Chen, Lizhang, et al.
Published: (2026)
ManiBox: Enhancing Embodied Spatial Generalization via Scalable Simulation Data Generations
by: Tan, Hengkai, et al.
Published: (2024)
by: Tan, Hengkai, et al.
Published: (2024)
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
by: Wang, Jinyan, et al.
Published: (2025)
by: Wang, Jinyan, et al.
Published: (2025)
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
by: Yang, Puning, et al.
Published: (2025)
by: Yang, Puning, et al.
Published: (2025)
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
by: Pan, Xinglin, et al.
Published: (2025)
by: Pan, Xinglin, et al.
Published: (2025)
Out-of-distribution Generalization for Total Variation based Invariant Risk Minimization
by: Wang, Yuanchao, et al.
Published: (2025)
by: Wang, Yuanchao, et al.
Published: (2025)
Can In-context Learning Really Generalize to Out-of-distribution Tasks?
by: Wang, Qixun, et al.
Published: (2024)
by: Wang, Qixun, et al.
Published: (2024)
Principled Out-of-Distribution Generalization via Simplicity
by: Ge, Jiawei, et al.
Published: (2025)
by: Ge, Jiawei, et al.
Published: (2025)
Learning and Generalization with Mixture Data
by: Vardhan, Harsh, et al.
Published: (2025)
by: Vardhan, Harsh, et al.
Published: (2025)
Early Period of Training Impacts Adaptation for Out-of-Distribution Generalization: An Empirical Study
by: Liu, Chen Cecilia, et al.
Published: (2024)
by: Liu, Chen Cecilia, et al.
Published: (2024)
LLM Unlearning with LLM Beliefs
by: Li, Kemou, et al.
Published: (2025)
by: Li, Kemou, et al.
Published: (2025)
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
by: Chi, Haoang, et al.
Published: (2025)
by: Chi, Haoang, et al.
Published: (2025)
Model Reprogramming Outperforms Fine-tuning on Out-of-distribution Data in Text-Image Encoders
by: Geng, Andrew, et al.
Published: (2024)
by: Geng, Andrew, et al.
Published: (2024)
Similar Items
-
ShiftKD: Benchmarking Knowledge Distillation under Distribution Shift
by: Zhang, Songming, et al.
Published: (2023) -
Causal Representation Learning with Optimal Compression under Complex Treatments
by: Liang, Wanting, et al.
Published: (2026) -
Adaptive-Boundary-Clipping GRPO: Ensuring Bounded Ratios for Stable and Generalizable Training
by: Liu, Chi, et al.
Published: (2026) -
AEGIS: Adversarial Target-Guided Retention-Data-Free Robust Concept Erasure from Diffusion Models
by: Li, Fengpeng, et al.
Published: (2026) -
M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling
by: Luo, Yuxiao, et al.
Published: (2025)