偏见缓解论文趋势 2023–2026
去偏算法
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 284 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 7 | 26 | 8 | 9 | 50 |
| 2024 | 25 | 12 | 23 | 13 | 73 |
| 2025 | 38 | 23 | 17 | 7 | 85 |
| 2026 | 14 | 24 | 24 | 14 | 76 |
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 283 篇，第 6 / 6 页
- ICLR 2023How robust is unsupervised representation learning to distribution shift?
- ICLR 2023How Sharpness-Aware Minimization Minimizes Sharpness?
- ICLR 2023Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
- ICLR 2023Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
- ICLR 2023Interpretable Debiasing of Vectorized Language Representations with Iterative Orthogonalization
- ICLR 2023Is Forgetting Less a Good Inductive Bias for Forward Transfer?
- ICLR 2023LAVA: Data Valuation without Pre-Specified Learning Algorithms
- ICLR 2023Learning Language Representations with Logical Inductive Bias
- ICLR 2023Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
- ICLR 2023Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent
- ICLR 2023MEDFAIR: Benchmarking Fairness for Medical Imaging
- ICLR 2023Mitigating Dataset Bias by Using Per-Sample Gradient
- ICLR 2023Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability
- ICLR 2023Sequential Gradient Coding For Straggler Mitigation
- ICLR 2023TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
- ICLR 2023The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks
- ICLR 2023The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks
- ICLR 2023Tuning Frequency Bias in Neural Network Training with Nonuniform Data
- ICML 2023FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
- ICML 2023Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
- ICML 2023Improving Fair Training under Correlation Shifts
- ICML 2023Men Also Do Laundry: Multi-Attribute Bias Amplification
- ICML 2023Naive imputation implicitly regularizes high-dimensional linear models
- ICML 2023Near-Optimal Quantum Coreset Construction Algorithms for Clustering
- ICML 2023Statistical Indistinguishability of Learning Algorithms
- ICML 2023Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias
- NeurIPS 2023Fast Model DeBias with Machine Unlearning
- NeurIPS 2023Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
- NeurIPS 2023Mitigating Source Bias for Fairer Weak Supervision
- NeurIPS 2023Reproducibility Study of “Quantifying Societal Bias Amplification in Image Captioning”
- NeurIPS 2023Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
- NeurIPS 2023Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples
- NeurIPS 2023The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查偏见缓解在 ICLR 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "bias-mitigation"}
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。