Bo An
Professor
Studies multi-agent systems, computational game theory and reinforcement learning, with applications to security and e-commerce.
Bo An's lab at NTU works on reasoning & neuro-symbolic AI, reinforcement learning and planning, control & multi-robot systems. 146 papers since 2023, 120 at top venues, mostly NeurIPS and ICML.
At a glance
- papers, 2023–2026
- 38/ year
- at T1 venues
- 82%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 52 papers2026 · T1 41 · collecting
- 32 papers2025 · T1 30
- 32 papers2024 · T1 27
- 30 papers2023 · T1 22
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, AAAI, IJCAI
Research focuspaper titles that use each topic's words
- Reinforcement learning55 papers
- Planning, control & multi-robot systems24 papers
- Reasoning & neuro-symbolic AI4 papers
- studies multi-agent0 papers
- computational game0 papers
Paper list
From the lab homepage.
202652 papers
- TMLR (T1)A tighter bound for reward learning in reinforcement learning from human feedback
- ICML (T1)AdverMCTS: Combating pseudo-correctness in code generation via adversarial Monte Carlo tree search
- ICML (T1)Adversarial dual on-policy distillation from expressive teacher
- NeurIPS (T1)AgentBrew: Offline tool-use agent learning from raw real-world trajectories
- ACL (T1)AgentOCR: Reimagining agent history via optical self-compression
- Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and DataAlphaForgeBench: Benchmarking end-to-end trading strategy design with large language models
- AAAI (T1)ArchetypeTrader: Reinforcement learning for selecting and refining learnable strategic archetypes in quantitative trading
- NeurIPS (T1)Backtracking with linear-in-depth search-space growth: Width-limited tree search for large language models
- Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and DataBayesian robust financial trading with adversarial synthetic market data
- ICML (T1)Beyond trajectory-level attribution: Graph-based credit assignment for agentic reinforcement learning
- ACL (T1)C2PO: Diagnosing and disentangling bias shortcuts in LLMs
- ICML (T1)Covariance volume maximization for embodied latent exploration in deep reinforcement learning
202532 papers
- ICML (T1)A closer look at backdoor attacks on CLIP
- ICLR (T1)AgentStudio: A toolkit for building general virtual agents
- WWW (T1)AURO: Reinforcement learning for adaptive user retention optimization in recommender systems
- ICML (T1)Cradle: Empowering foundation agents towards general computer control
- NeurIPS (T1)Deciphering the extremes: A novel approach for pathological long-tailed recognition in scientific discovery
- TIP (T1)Double oracle neural architecture search for game theoretic deep learning models
- Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and DataDual-head knowledge distillation: Enhancing logits utilization with an auxiliary head
- NeurIPS (T1)EconGym: A scalable AI testbed with diverse economic tasks
- NeurIPS (T1)Empirical study on robustness and resilience in cooperative multi-agent reinforcement learning
- Proceedings of the International Joint Conference on Autonomous Agents and Multi-Agent SystemsEnhancing sub-optimal trajectory stitching: Spatial composition RvS for offline RL
- NeurIPS (T1)Establishing linear surrogate regret bounds for convex smooth losses via convolutional Fenchel–Young losses
- WWW (T1)GAS: Generative auto-bidding with post-training search
202432 papers
- Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and DataA multimodal foundation agent for financial trading: Tool-augmented, diversified, and generalist
- ICML (T1)Configurable mirror descent: Towards a unification of decision making
- AISTATS (T1)Consistent hierarchical classification with a generalized metric
- ICLR (T1)Consistent Multi-class classification from multiple unlabeled datasets
- ICML (T1)DAG-based column generation for adversarial team games
- AAAI (T1)EarnHFT: Efficient hierarchical reinforcement learning for high frequency trading
- Proceedings of the International Joint Conference on Autonomous Agents and Multi-Agent SystemsGrasper: A generalist pursuer for pursuit-evasion problems
- ICLR (T1)Greedy sequential execution: Solving homogeneous and heterogeneous cooperative tasks with a unified framework
- IJCAI (T1)IMM: An imitative reinforcement learning approach with predictive representation learning for automatic market making
- CVPR (T1)Improving unsupervised hierarchical representation with reinforcement learning
- ICML (T1)Latent logic tree extraction for event sequence explanation from LLMs
- Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and DataMacroHFT: Memory augmented context-aware reinforcement learning on high frequency trading
202330 papers
- IJCAI (T1)A learning approach to complex contagion influence maximization
- JAAMASClassifying ambiguous identities in hidden-role Stochastic games with multi-agent reinforcement learning
- NeurIPS (T1)Computing optimal Nash equilibria in multiplayer games
- AISTATS (T1)Consistent complementary-label learning via order-preserving losses
- ICML (T1)Controlling type confounding in ad hoc teamwork with instance-wise teammate feedback rectification
- AAAI (T1)Deep FTRL-ORW: An efficient FTRL-based deep reinforcement learning algorithm for solving imperfect information extensive-form game
- TNNLS (T1)Deep learning from multiple noisy annotators as a union
- IJCAI (T1)Exploring leximin principle for fair core-selecting combinatorial auctions: Payment rule design and implementation
- NeurIPS (T1)Few-shot generation via recalling the episodic-semantic memory like human being
- Proceedings of the International Joint Conference on Autonomous Agents and Multi-Agent SystemsFinding optimal nash equilibria in multiplayer games via correlation plans
- NeurIPS (T1)In defense of softmax parametrization for calibrated and consistent learning to defer
- Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and DataMastering stock markets with efficient mixture of diversified trading experts
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NTUFall 2027
Not announced yet
Computing and Data Science PhD
- Round
- Main
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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