探索利用论文趋势 2023–2026

主要结论

• 探索利用论文从 2023 年的 47 篇增长到 2026 年的 71 篇，三年间扩大到约 1.5 倍。
• NeurIPS 是发探索利用论文最多的会议，2023–2026 年共 93 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是推理模型：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 10 篇。
• 在「推荐与广告」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的点击率预测是 2.6 倍，探索利用是 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 2026Decentralized Q-Learning in Markov Potential Games
• ICML 2026Bandit Social Leaning Dynamics with Exploration Episodes
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Underground Plant Exploration Non-Destructive 3D Root Assessment with GPR Based
• NeurIPS 2025DUET: Dual-Perspective Pseudo Labeling and Uncertainty-aware Exploration & Exploitation Training for Source-Free Domain Adaptation
• ICML 2025Clustering Items through Bandit Feedback: Finding the Right Feature out of Many
• ICLR 2025B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
• CVPR 2025Dynamic Pseudo Labeling via Gradient Cutting for High-Low Entropy Exploration
• NeurIPS 2024Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
• ICML 2024A Contextual Combinatorial Bandit Approach to Negotiation
• ICLR 2024Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
• CVPR 2024Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation
• NeurIPS 2023Adaptive Algorithms for Relaxed Pareto Set Identification
• ICML 2023Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
• ICLR 2023Combinatorial Pure Exploration of Causal Bandits
• CVPR 2023Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
• NeurIPS 2026Hit Expansion via Localized Exploration of Synthesizable Chemical Space
• ICML 2026Exploration-free Algorithms for Multi-group Mean Estimation
• ICLR 2026DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration
• CVPR 2026From Exploration to Exploitation A Two-Stage Entropy RLVR Approach for

全部探索利用论文

共 47 篇，第 1 / 1 页

1. CVPR 2023Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
2. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
3. ICLR 2023Guiding Safe Exploration with Weakest Preconditions
4. ICLR 2023Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward
5. ICLR 2023Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics
6. ICLR 2023Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning
7. ICLR 2023Replicable Bandits
8. ICLR 2023Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments
9. ICLR 2023Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL
10. ICLR 2023Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks
11. ICML 2023An Information-Theoretic Analysis of Nonstationary Bandit Learning
12. ICML 2023Anti-Exploration by Random Network Distillation
13. ICML 2023Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
14. ICML 2023Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
15. ICML 2023Deep Laplacian-based Options for Temporally-Extended Exploration
16. ICML 2023Fast Rates for Maximum Entropy Exploration
17. ICML 2023FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
18. ICML 2023Incentivizing Exploration with Linear Contexts and Combinatorial Actions
19. ICML 2023Layered State Discovery for Incremental Autonomous Exploration
20. ICML 2023LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
21. ICML 2023Leveraging Demonstrations to Improve Online Learning: Quality Matters
22. ICML 2023Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
23. ICML 2023Moderately Distributional Exploration for Domain Generalization
24. ICML 2023Multiplier Bootstrap-based Exploration
25. ICML 2023Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
26. ICML 2023On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm
27. ICML 2023Online Restless Bandits with Unobserved States
28. ICML 2023Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
29. ICML 2023Stochastic Gradient Succeeds for Bandits
30. NeurIPS 2023Accelerating Exploration with Unlabeled Prior Data
31. NeurIPS 2023Adaptive Algorithms for Relaxed Pareto Set Identification
32. NeurIPS 2023An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits
33. NeurIPS 2023Anytime Model Selection in Linear Bandits
34. NeurIPS 2023Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits
35. NeurIPS 2023Cascading Contextual Assortment Bandits
36. NeurIPS 2023Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing
37. NeurIPS 2023Double Auctions with Two-sided Bandit Feedback
38. NeurIPS 2023ELDEN: Exploration via Local Dependencies
39. NeurIPS 2023High-dimensional Contextual Bandit Problem without Sparsity
40. NeurIPS 2023Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision
41. NeurIPS 2023Latent exploration for Reinforcement Learning
42. NeurIPS 2023Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits
43. NeurIPS 2023Nearest Neighbour with Bandit Feedback
44. NeurIPS 2023Non-Stationary Bandits with Auto-Regressive Temporal Dependency
45. NeurIPS 2023On the Exploration of Local Significant Differences For Two-Sample Test
46. NeurIPS 2023Optimal Rates for Bandit Nonstochastic Control
47. NeurIPS 2023Successor-Predecessor Intrinsic Exploration

常见问题

2023–2026 年四大会议一共有多少篇探索利用论文？

共 235 篇，按年份分别是 2023 年 47 篇、2024 年 63 篇、2025 年 54 篇、2026 年 71 篇。

NeurIPS 2026 有多少篇探索利用论文？

NeurIPS 2026 有 25 篇探索利用论文，上一届是 20 篇。

ICLR 2026 有多少篇探索利用论文？

ICLR 2026 有 23 篇探索利用论文，上一届是 10 篇。

ICML 2026 有多少篇探索利用论文？

ICML 2026 有 19 篇探索利用论文，上一届是 21 篇。

CVPR 2026 有多少篇探索利用论文？

CVPR 2026 有 4 篇探索利用论文，上一届是 3 篇。

哪个会议的探索利用论文最多？

NeurIPS，2023–2026 年共 93 篇。