Multi-label zero-shot learning with structured knowledge graphs CW Lee, W Fang, CK Yeh, YCF Wang Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 329 | 2018 |
A new algorithm for non-stationary contextual bandits: Efficient, optimal and parameter-free Y Chen, CW Lee, H Luo, CY Wei Conference on Learning Theory, 696-726, 2019 | 128 | 2019 |
Linear Last-iterate Convergence in Constrained Saddle-point Optimization CY Wei, CW Lee, M Zhang, H Luo International Conference on Learning Representations, 2021 | 109 | 2021 |
Last-iterate convergence of decentralized optimistic gradient descent/ascent in infinite-horizon competitive markov games CY Wei, CW Lee, M Zhang, H Luo Conference on learning theory, 4259-4299, 2021 | 97 | 2021 |
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs CW Lee, H Luo, CY Wei, M Zhang Advances in Neural Information Processing Systems 33, 15522-15533, 2020 | 57 | 2020 |
Policy optimization in adversarial mdps: Improved exploration via dilated bonuses H Luo, CY Wei, CW Lee Advances in Neural Information Processing Systems 34, 22931-22942, 2021 | 46 | 2021 |
Achieving near instance-optimality and minimax-optimality in stochastic and adversarial linear bandits simultaneously CW Lee, H Luo, CY Wei, M Zhang, X Zhang International Conference on Machine Learning, 6142-6151, 2021 | 46 | 2021 |
Last-iterate convergence in extensive-form games CW Lee, C Kroer, H Luo Advances in Neural Information Processing Systems 34, 14293-14305, 2021 | 40 | 2021 |
Near-optimal no-regret learning for general convex games G Farina, I Anagnostides, H Luo, CW Lee, C Kroer, T Sandholm Advances in Neural Information Processing Systems 35, 2022 | 30 | 2022 |
Achieving optimal dynamic regret for non-stationary bandits without prior information P Auer, Y Chen, P Gajane, CW Lee, H Luo, R Ortner, CY Wei Conference on Learning Theory, 159-163, 2019 | 30 | 2019 |
Uncoupled Learning Dynamics with Swap Regret in Multiplayer Games I Anagnostides, G Farina, C Kroer, CW Lee, H Luo, T Sandholm Advances in Neural Information Processing Systems 35, 2022 | 29 | 2022 |
Kernelized multiplicative weights for 0/1-polyhedral games: Bridging the gap between learning in extensive-form and normal-form games G Farina, CW Lee, H Luo, C Kroer International Conference on Machine Learning, 6337-6357, 2022 | 27 | 2022 |
A closer look at small-loss bounds for bandits with graph feedback CW Lee, H Luo, M Zhang Conference on Learning Theory, 2516-2564, 2020 | 22 | 2020 |
Linear last-iterate convergence for matrix games and stochastic games CW Lee, H Luo, CY Wei, M Zhang arXiv e-prints, arXiv: 2006.09517, 2020 | 16 | 2020 |
Clairvoyant Regret Minimization: Equivalence with Nemirovski's Conceptual Prox Method and Extension to General Convex Games G Farina, C Kroer, CW Lee, H Luo NeurIPS 2022 Workshop on Optimization for Machine Learning, 2022 | 6 | 2022 |
Regret matching+:(in) stability and fast convergence in games G Farina, J Grand-Clément, C Kroer, CW Lee, H Luo Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
Context-lumpable stochastic bandits CW Lee, Q Liu, Y Abbasi Yadkori, C Jin, T Lattimore, C Szepesvári Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms Y Cai, G Farina, J Grand-Clément, C Kroer, CW Lee, H Luo, W Zheng arXiv preprint arXiv:2406.10631, 2024 | | 2024 |
Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games Y Cai, G Farina, J Grand-Clément, C Kroer, CW Lee, H Luo, W Zheng arXiv preprint arXiv:2311.00676, 2023 | | 2023 |
Practical Knowledge Distillation: Using DNNs to Beat DNNs CW Lee, PA Apostolopulos, IL Markov arXiv preprint arXiv:2302.12360, 2023 | | 2023 |