Bias Mitigation papers, 2023–2026

Key findings

• Bias Mitigation went from 50 papers in 2023 to 76 in 2026, roughly 1.5x in three years.
• ICLR publishes the most Bias Mitigation work: 85 papers from 2023 to 2026, 30% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 0 in 2023 to 15 in 2026.
• Among the 5 topics under Causal and Fairness, it ranks #4 by 2026 paper count; the fastest grower in the group, Causal Discovery, is up 2.4x versus 1.5x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Bias Mitigation papers

24 papers, page 1 of 1

1. ICLR 2026BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
2. ICLR 2026Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMs
3. ICLR 2026Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts
4. ICLR 2026BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Models
5. ICLR 2026BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses
6. ICLR 2026Directional Convergence, Benign Overfitting of Gradient Descent in leaky ReLU two-layer Neural Networks
7. ICLR 2026Discovering alternative solutions beyond the simplicity bias in recurrent neural networks
8. ICLR 2026GRADIEND: Feature Learning within Neural Networks Exemplified through Biases
9. ICLR 2026How does the optimizer implicitly bias the model merging loss landscape?
10. ICLR 2026Hyperbolic Aware Minimization: Implicit Bias for Sparsity
11. ICLR 2026Implicit Bias of Per-sample Adam on Separable Data: Departure from the Full-batch Regime
12. ICLR 2026Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
13. ICLR 2026Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
14. ICLR 2026Minor First, Major Last: A Depth-Induced Implicit Bias of Sharpness-Aware Minimization
15. ICLR 2026Never Saddle: Reparameterized Steepest Descent as Mirror Flow
16. ICLR 2026Noise Stability of Transformer Models
17. ICLR 2026OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
18. ICLR 2026Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures
19. ICLR 2026Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape
20. ICLR 2026The First Impression Problem: Internal Bias Triggers Overthinking in Reasoning Models
21. ICLR 2026Transformers with Endogenous In-Context Learning: Bias Characterization and Mitigation
22. ICLR 2026Trapped by simplicity: When Transformers fail to learn from noisy features
23. ICLR 2026Variational Deep Learning via Implicit Regularization
24. ICLR 2026WRING Out The Bias: A Rotation-Based Alternative To Projection Debiasing

FAQ

How many Bias Mitigation papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

284 in total: 50 in 2023, 73 in 2024, 85 in 2025, 76 in 2026.

How many Bias Mitigation papers are at NeurIPS 2026?

NeurIPS 2026 has 14 Bias Mitigation papers, versus 38 the year before.

How many Bias Mitigation papers are at ICLR 2026?

ICLR 2026 has 24 Bias Mitigation papers, versus 23 the year before.

How many Bias Mitigation papers are at ICML 2026?

ICML 2026 has 24 Bias Mitigation papers, versus 17 the year before.

How many Bias Mitigation papers are at CVPR 2026?

CVPR 2026 has 14 Bias Mitigation papers, versus 7 the year before.

Which conference publishes the most Bias Mitigation papers?

ICLR, with 85 papers from 2023 to 2026.