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 84 篇，第 2 / 2 页

51. ICML 2024Handling Heterogeneous Curvatures in Bandit LQR Control
52. ICML 2024Imitation Learning in Discounted Linear MDPs without exploration assumptions
53. ICML 2024Online Learning with Bounded Recall
54. ICML 2024Online Resource Allocation with Non-Stationary Customers
55. ICML 2024Optimal Batched Linear Bandits
56. ICML 2024Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
57. ICML 2024Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
58. ICML 2024Random Latent Exploration for Deep Reinforcement Learning
59. ICML 2024ReconBoost: Boosting Can Achieve Modality Reconcilement
60. ICML 2024Safe and Robust Subgame Exploitation in Imperfect Information Games
61. ICML 2024Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants
62. ICML 2024Scalable Online Exploration via Coverability
63. ICML 2024Stochastic Bandits with ReLU Neural Networks
64. ICML 2024Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm
65. ICML 2024Testing the Feasibility of Linear Programs with Bandit Feedback
66. ICML 2023An Information-Theoretic Analysis of Nonstationary Bandit Learning
67. ICML 2023Anti-Exploration by Random Network Distillation
68. ICML 2023Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
69. ICML 2023Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
70. ICML 2023Deep Laplacian-based Options for Temporally-Extended Exploration
71. ICML 2023Fast Rates for Maximum Entropy Exploration
72. ICML 2023FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
73. ICML 2023Incentivizing Exploration with Linear Contexts and Combinatorial Actions
74. ICML 2023Layered State Discovery for Incremental Autonomous Exploration
75. ICML 2023LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
76. ICML 2023Leveraging Demonstrations to Improve Online Learning: Quality Matters
77. ICML 2023Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
78. ICML 2023Moderately Distributional Exploration for Domain Generalization
79. ICML 2023Multiplier Bootstrap-based Exploration
80. ICML 2023Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
81. ICML 2023On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm
82. ICML 2023Online Restless Bandits with Unobserved States
83. ICML 2023Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
84. ICML 2023Stochastic Gradient Succeeds for Bandits

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。