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 25 篇，第 1 / 1 页

1. NeurIPS 2024A distributional simplicity bias in the learning dynamics of transformers
2. NeurIPS 2024Addressing Bias in Online Selection with Limited Budget of Comparisons
3. NeurIPS 2024Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families
4. NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
5. NeurIPS 2024Bias Detection via Signaling
6. NeurIPS 2024Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training
7. NeurIPS 2024Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization
8. NeurIPS 2024Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise
9. NeurIPS 2024Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias
10. NeurIPS 2024CultureLLM: Incorporating Cultural Differences into Large Language Models
11. NeurIPS 2024DeNetDM: Debiasing by Network Depth Modulation
12. NeurIPS 2024Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement
13. NeurIPS 2024FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation
14. NeurIPS 2024How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks
15. NeurIPS 2024Implicit Bias of Mirror Flow on Separable Data
16. NeurIPS 2024Improved Algorithms for Contextual Dynamic Pricing
17. NeurIPS 2024IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing
18. NeurIPS 2024Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?
19. NeurIPS 2024Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints
20. NeurIPS 2024Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz
21. NeurIPS 2024Temporally Consistent Atmospheric Turbulence Mitigation with Neural Representations
22. NeurIPS 2024The Implicit Bias of Adam on Separable Data
23. NeurIPS 2024The Implicit Bias of Gradient Descent on Separable Multiclass Data
24. NeurIPS 2024Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis
25. NeurIPS 2024Unveiling the Bias Impact on Symmetric Moral Consistency of Large Language Models

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。