偏见缓解论文趋势 2023–2026

主要结论

• 偏见缓解论文从 2023 年的 50 篇增长到 2026 年的 76 篇，三年间扩大到约 1.5 倍。
• ICLR 是发偏见缓解论文最多的会议，2023–2026 年共 85 篇，占四个会议总量的 30%。
• 交叉方向里涨得最快的是多模态大模型：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 15 篇。
• 在「因果与公平」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的因果发现是 2.4 倍，偏见缓解是 1.5 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Controlling for Omitted Variable Bias in Deep Neural Networks
• ICML 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
• ICLR 2026Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts
• CVPR 2026BAMI Training-Free Bias Mitigation in GUI Grounding
• NeurIPS 2025Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
• ICML 2025Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Stochastic Bias and Remedies
• ICLR 2025ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
• CVPR 2025Data Distributional Properties As Inductive Bias for Systematic Generalization
• NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
• ICML 2024Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
• ICLR 2024A 2-Dimensional State Space Layer for Spatial Inductive Bias
• CVPR 2024Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation
• NeurIPS 2023Fast Model DeBias with Machine Unlearning
• ICML 2023FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
• ICLR 2023Bias Propagation in Federated Learning
• CVPR 2023Bias Mimicking: A Simple Sampling Approach for Bias Mitigation
• NeurIPS 2026Empirical Bayes Rebiasing
• ICML 2026DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation
• ICLR 2026BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses
• CVPR 2026Bias at the End of the Score

全部偏见缓解论文

共 26 篇，第 1 / 1 页

1. ICLR 2023Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models
2. ICLR 2023Bias Propagation in Federated Learning
3. ICLR 2023Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
4. ICLR 2023Delving into Semantic Scale Imbalance
5. ICLR 2023Everybody Needs Good Neighbours: An Unsupervised Locality-based Method for Bias Mitigation
6. ICLR 2023Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks
7. ICLR 2023FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
8. ICLR 2023How gradient estimator variance and bias impact learning in neural networks
9. ICLR 2023How robust is unsupervised representation learning to distribution shift?
10. ICLR 2023How Sharpness-Aware Minimization Minimizes Sharpness?
11. ICLR 2023Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
12. ICLR 2023Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
13. ICLR 2023Interpretable Debiasing of Vectorized Language Representations with Iterative Orthogonalization
14. ICLR 2023Is Forgetting Less a Good Inductive Bias for Forward Transfer?
15. ICLR 2023LAVA: Data Valuation without Pre-Specified Learning Algorithms
16. ICLR 2023Learning Language Representations with Logical Inductive Bias
17. ICLR 2023Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
18. ICLR 2023Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent
19. ICLR 2023MEDFAIR: Benchmarking Fairness for Medical Imaging
20. ICLR 2023Mitigating Dataset Bias by Using Per-Sample Gradient
21. ICLR 2023Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability
22. ICLR 2023Sequential Gradient Coding For Straggler Mitigation
23. ICLR 2023TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
24. ICLR 2023The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks
25. ICLR 2023The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks
26. ICLR 2023Tuning Frequency Bias in Neural Network Training with Nonuniform Data

常见问题

2023–2026 年四大会议一共有多少篇偏见缓解论文？

共 284 篇，按年份分别是 2023 年 50 篇、2024 年 73 篇、2025 年 85 篇、2026 年 76 篇。

NeurIPS 2026 有多少篇偏见缓解论文？

NeurIPS 2026 有 14 篇偏见缓解论文，上一届是 38 篇。

ICLR 2026 有多少篇偏见缓解论文？

ICLR 2026 有 24 篇偏见缓解论文，上一届是 23 篇。

ICML 2026 有多少篇偏见缓解论文？

ICML 2026 有 24 篇偏见缓解论文，上一届是 17 篇。

CVPR 2026 有多少篇偏见缓解论文？

CVPR 2026 有 14 篇偏见缓解论文，上一届是 7 篇。

哪个会议的偏见缓解论文最多？

ICLR，2023–2026 年共 85 篇。