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 72 篇，第 1 / 2 页

1. ICML 2026ABCD: All Biases Come Disguised
2. ICML 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
3. ICML 2026Breaking the Reversal Curse in Autoregressive Language Models via Identity Bridge
4. ICML 2026Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation
5. ICML 2026DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation
6. ICML 2026FedScar: Correcting Geometric Bias for Flatness-Consistent Federated Learning
7. ICML 2026FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction
8. ICML 2026Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation
9. ICML 2026LLM Watermark Evasion via Bias Inversion
10. ICML 2026Logit-Attention Divergence: Mitigating Position Bias in Multi-Image Retrieval via Attention-Guided Calibration
11. ICML 2026Mitigating Bias in Locally Constrained Decoding via Tractable Proposals
12. ICML 2026Mitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneck
13. ICML 2026Omitted Variable Bias in Language Models Under Distribution Shift
14. ICML 2026Romberg-Extrapolated Zeroth-Order Gradient Estimator: Higher-Order Bias Reduction with Preserved Leading Directional Variance
15. ICML 2026Self-correcting for Debiasing Large Language Models
16. ICML 2026Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation
17. ICML 2026Taming the Recent-Data Bias: Towards Robust Time Series Forecasting with Global Context
18. ICML 2026The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks
19. ICML 2026The Implicit Bias of Depth: From Neural Collapse to Softmax Codes
20. ICML 2026The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
21. ICML 2026Weak-to-Strong Generalization via Bregman Bias–Variance Decomposition
22. ICML 2026When Generalized Zero-Shot Learning Meets PU Learning: A Plug-and-Play Framework for Seen-Class Bias Mitigation
23. ICML 2026When Sample Selection Bias Precipitates Model Collapse
24. ICML 2026You Need Better Attention Priors
25. ICML 2025ADDQ: Adaptive distributional double Q-learning
26. ICML 2025Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
27. ICML 2025Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Stochastic Bias and Remedies
28. ICML 2025Controlling Underestimation Bias in Constrained Reinforcement Learning for Safe Exploration
29. ICML 2025Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
30. ICML 2025Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
31. ICML 2025Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations
32. ICML 2025Mirror, Mirror of the Flow: How Does Regularization Shape Implicit Bias?
33. ICML 2025NeuronTune: Towards Self-Guided Spurious Bias Mitigation
34. ICML 2025On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback
35. ICML 2025On the Emergence of Position Bias in Transformers
36. ICML 2025Parametric Scaling Law of Tuning Bias in Conformal Prediction
37. ICML 2025Position: Political Neutrality in AI Is Impossible — But Here Is How to Approximate It
38. ICML 2025Resolving Lexical Bias in Model Editing
39. ICML 2025Rethinking the Bias of Foundation Model under Long-tailed Distribution
40. ICML 2025SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
41. ICML 2025Subgroups Matter for Robust Bias Mitigation
42. ICML 2024A Universal Class of Sharpness-Aware Minimization Algorithms
43. ICML 2024Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate
44. ICML 2024Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning
45. ICML 2024Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
46. ICML 2024Can Implicit Bias Imply Adversarial Robustness?
47. ICML 2024Convergence of Some Convex Message Passing Algorithms to a Fixed Point
48. ICML 2024Differentially Private Bias-Term Fine-tuning of Foundation Models
49. ICML 2024Discovering Bias in Latent Space: An Unsupervised Debiasing Approach
50. ICML 2024Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning

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。