Lingxiao Wang

Orcid: 0000-0002-1654-4681

Affiliations:
  • Northwestern University, Department of Industrial Engineering and Management Sciences, Evanston, IL, USA


According to our database1, Lingxiao Wang authored at least 22 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., July, 2024

False Correlation Reduction for Offline Reinforcement Learning.
IEEE Trans. Pattern Anal. Mach. Intell., February, 2024

Pessimistic value iteration for multi-task data sharing in Offline Reinforcement Learning.
Artif. Intell., January, 2024

2023
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., August, 2023

Addressing Hindsight Bias in Multigoal Reinforcement Learning.
IEEE Trans. Cybern., 2023

Privileged Knowledge Distillation for Sim-to-Real Policy Generalization.
CoRR, 2023

Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models.
CoRR, 2022

Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency.
CoRR, 2022

Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2022

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning.
CoRR, 2021

Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach.
CoRR, 2021

Provably Efficient Causal Reinforcement Learning with Confounded Observational Data.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Dynamic Bottleneck for Robust Self-Supervised Exploration.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Principled Exploration via Optimistic Bootstrapping and Backward Induction.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning.
CoRR, 2020

Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

On the Global Optimality of Model-Agnostic Meta-Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Neural Policy Gradient Methods: Global Optimality and Rates of Convergence.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Statistical-Computational Tradeoff in Single Index Models.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019


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