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 93 篇，第 2 / 2 页

51. NeurIPS 2024Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
52. NeurIPS 2024First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
53. NeurIPS 2024Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation
54. NeurIPS 2024Hierarchical Uncertainty Exploration via Feedforward Posterior Trees
55. NeurIPS 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
56. NeurIPS 2024Improved Analysis for Bandit Learning in Matching Markets
57. NeurIPS 2024Improved Regret of Linear Ensemble Sampling
58. NeurIPS 2024Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
59. NeurIPS 2024Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
60. NeurIPS 2024NeoRL: Efficient Exploration for Nonepisodic RL
61. NeurIPS 2024No-Regret Bandit Exploration based on Soft Tree Ensemble Model
62. NeurIPS 2024Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff
63. NeurIPS 2024Online Learning of Delayed Choices
64. NeurIPS 2024Online Non-convex Learning in Dynamic Environments
65. NeurIPS 2024Optimal Clustering with Bandit Feedback
66. NeurIPS 2024Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits
67. NeurIPS 2024Preference-based Pure Exploration
68. NeurIPS 2024PRODuctive bandits: Importance Weighting No More
69. NeurIPS 2024Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images
70. NeurIPS 2024Provably Efficient Interactive-Grounded Learning with Personalized Reward
71. NeurIPS 2024Query-Efficient Correlation Clustering with Noisy Oracle
72. NeurIPS 2024Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
73. NeurIPS 2024Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus
74. NeurIPS 2024Self-Guiding Exploration for Combinatorial Problems
75. NeurIPS 2024Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates
76. NeurIPS 2023Accelerating Exploration with Unlabeled Prior Data
77. NeurIPS 2023Adaptive Algorithms for Relaxed Pareto Set Identification
78. NeurIPS 2023An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits
79. NeurIPS 2023Anytime Model Selection in Linear Bandits
80. NeurIPS 2023Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits
81. NeurIPS 2023Cascading Contextual Assortment Bandits
82. NeurIPS 2023Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing
83. NeurIPS 2023Double Auctions with Two-sided Bandit Feedback
84. NeurIPS 2023ELDEN: Exploration via Local Dependencies
85. NeurIPS 2023High-dimensional Contextual Bandit Problem without Sparsity
86. NeurIPS 2023Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision
87. NeurIPS 2023Latent exploration for Reinforcement Learning
88. NeurIPS 2023Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits
89. NeurIPS 2023Nearest Neighbour with Bandit Feedback
90. NeurIPS 2023Non-Stationary Bandits with Auto-Regressive Temporal Dependency
91. NeurIPS 2023On the Exploration of Local Significant Differences For Two-Sample Test
92. NeurIPS 2023Optimal Rates for Bandit Nonstochastic Control
93. 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。