- Lange, R.T., Schaul, T., Chen, Y., Zahavy, T., Dallibard, V., Lu, C., Singh, S. and Flennerhag, S., 2023. Discovering evolution strategies via meta-black-box optimization. International Conference on Learning Representations. [ www | pdf ] ( ES )
- Pourchot, A. and Sigaud, O., 2019. CEM-RL: Combining evolutionary and gradient-based methods for policy search. In International Conference on Learning Representations. [ www | pdf ] ( ES )
Amin, A.A. 2024. Discrete Natural Evolution Strategies, In *The Second Tiny Papers Track at ICLR *. [ www | pdf ]
Peng, Y., Song, A., Fayek, H.M., Ciesielski, V. and Chang, X., 2024. SWAP-NAS: Sample-Wise Activation Patterns For Ultra-Fast NAS. In The Twelfth International Conference on Learning Representations. [ www | pdf ]
Bradley, H., Dai, A., Teufel, H., Zhang, J., Oostermeijer, K., Bellagente, M., Clune, J., Stanley, K., Schott, G. and Lehman, J., 2024. Quality-diversity through AI feedback. In The Twelfth International Conference on Learning Representations. [ www | pdf ]
Xue, K., Wang, R.J., Li, P., Li, D., Jianye, H.A.O. and Qian, C., 2024, October. Sample-efficient quality-diversity by cooperative coevolution. In The Twelfth International Conference on Learning Representations. [ www | pdf ]
Ma, Y.J., Liang, W., Wang, G., Huang, D.A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L. and Anandkumar, A., 2024. Eureka: Human-level reward design via coding large language models. In The Twelfth International Conference on Learning Representations. [ www | pdf ]
Guo, Q., Wang, R., Guo, J., Li, B., Song, K., Tan, X., Liu, G., Bian, J. and Yang, Y., 2024. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. In The Twelfth International Conference on Learning Representations. [ www | pdf ]
Yan, C., Chang, X., Li, Z., Yao, L., Luo, M. and Zheng, Q., 2024, October. Masked Distillation Advances Self-Supervised Transformer Architecture Search. In The Twelfth International Conference on Learning Representations. [ www | pdf ]
Hao et al., 2023. ERL-Re^2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation. In International Conference on Learning Representations. [ www | pdf ]
Ohsawa, 20223. Truthful Self-Play. In International Conference on Learning Representations. [ www | pdf ]
Boelrijk, Ensing and Forré, 2023. Multi-objective optimization via equivariant deep hypervolume approximation. In International Conference on Learning Representations. [ www | pdf]
Fong, Wongso and Motani, 2023. Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms. In International Conference on Learning Representations. [ www | pdf]
Maile, George Wilson and Forré, 2023. Equivariance-aware Architectural Optimization of Neural Networks. In International Conference on Learning Representations. [ www | pdf]
Wang et al., 2023. Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition. In International Conference on Learning Representations. [ www | pdf ]
Wang, Y., Xue, K. and Qian, C., 2022. Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning. In International Conference on Learning Representations. [ www | pdf ]
Yang, Y., Jiang, J., Zhou, T., Ma, J. and Shi, Y., 2022. Pareto Policy Pool for Model-based Offline Reinforcement Learning. In International Conference on Learning Representations. [ www | pdf ]
Berliner, A., Rotman, G., Adi, Y., Reichart, R. and Hazan, T., 2022. Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies. In International Conference on Learning Representations. [ www | pdf ]
Ding, L. and Spector, L., 2022. Optimizing neural networks with gradient lexicase selection. In International Conference on Learning Representations. [ www | pdf ]
Ferreira, F., Nierhoff, T., Saelinger, A. and Hutter, F., 2022. Learning Synthetic Environments and Reward Networks for Reinforcement Learning. In International Conference on Learning Representations. [ www | pdf ]
Liu, S., Marris, L., Hennes, D., Merel, J., Heess, N. and Graepel, T., 2022. NeuPL: Neural Population Learning. In International Conference on Learning Representations. [ www | pdf ]
Lin, X., Yang, Z. and Zhang, Q., 2022. Pareto Set Learning for Neural Multi-objective Combinatorial Optimization. In International Conference on Learning Representations. [ www | pdf ]
Vo, V.Q., Abbasnejad, E. and Ranasinghe, D.C., 2022. Query efficient decision based sparse attacks against black-box deep learning models. In International Conference on Learning Representations. [ www | pdf ]
Co-Reyes, J.D., Miao, Y., Peng, D., Real, E., Levine, S., Le, Q.V., Lee, H. and Faust, A., 2021. Evolving reinforcement learning algorithms. In International Conference on Learning Representations. [ www | pdf ]
Hejna III, D.J., Abbeel, P. and Pinto, L., 2021. Task-agnostic morphology evolution. In International Conference on Learning Representations. [ www | pdf ]
Marchesini, E., Corsi, D. and Farinelli, A., 2021. Genetic soft updates for policy evolution in deep reinforcement learning. In International Conference on Learning Representations. [ www | pdf ]
Khadka, S., Aflalo, E., Marder, M., Ben-David, A., Miret, S., Mannor, S., Hazan, T., Tang, H. and Majumdar, S., 2021. Optimizing memory placement using evolutionary graph reinforcement learning. In International Conference on Learning Representations. [ www | pdf ]
Chen, B.H., Wang, T.Z., Li, C.T., Dai, H.J. and Song, L., 2021. Molecule optimization by explainable evolution. In International Conference on Learning Representations. [ www | pdf | Python ]
Moskovitz, T., Arbel, M., Huszar, F. and Gretton, A., 2021. Efficient Wasserstein natural gradients for reinforcement learning. In International Conference on Learning Representations. [ www | pdf | Python ] (ES)
Liu, P., Zhang, G., Wang, B., Xu, H., Liang, X., Jiang, Y. and Li, Z., 2021. Loss function discovery for object detection via convergence-simulation driven search. In International Conference on Learning Representations. [ www | pdf ]
Hottung, A., Bhandari, B. and Tierney, K., 2021. Learning a latent search space for routing problems using variational autoencoders. [ www | pdf ]
Kirsch, L., van Steenkiste, S. and Schmidhuber, J., 2020. Improving generalization in meta reinforcement learning using learned objectives. In International Conference on Learning Representations. [ www | pdf ]
Alet, F., Schneider, M.F., Lozano-Perez, T. and Kaelbling, L.P., 2020. Meta-learning curiosity algorithms. In International Conference on Learning Representations. [ www | pdf | Python ]
Paliwal, A., Gimeno, F., Nair, V., Li, Y., Lubin, M., Kohli, P. and Vinyals, O., 2019. Reinforced genetic algorithm learning for optimizing computation graphs. In International Conference on Learning Representations. [ www | pdf ]
Song, X., Gao, W., Yang, Y., Choromanski, K., Pacchiano, A. and Tang, Y., 2020. ES-MAML: Simple Hessian-free meta learning. In International Conference on Learning Representations. [ www | pdf ]
Long, Q., Zhou, Z., Gupta, A., Fang, F., Wu, Y. and Wang, X., 2020. Evolutionary population curriculum for scaling multi-agent reinforcement learning. In International Conference on Learning Representations. [ www | pdf ]
Jung, W., Park, G. and Sung, Y., 2020. Population-guided parallel policy search for reinforcement learning. In International Conference on Learning Representations. [ www | pdf ]
Nigam, A., Friederich, P., Krenn, M. and Aspuru-Guzik, A., 2020. Augmenting genetic algorithms with deep neural networks for exploring the chemical space. In International Conference on Learning Representations. [ www | pdf ]
La Cava, W., Singh, T.R., Taggart, J., Suri, S. and Moore, J.H., 2019. Learning concise representations for regression by evolving networks of trees. In International Conference on Learning Representations. [ www | pdf ]
Wang, T., Zhou, Y., Fidler, S. and Ba, J., 2019. Neural graph evolution: Towards efficient automatic robot design. In International Conference on Learning Representations. [ www | pdf ]
Pourchot, A. and Sigaud, O., 2019. CEM-RL: Combining evolutionary and gradient-based methods for policy search. In International Conference on Learning Representations. [ www | pdf ]
Elsken, T., Metzen, J.H. and Hutter, F., 2019. Efficient multi-objective neural architecture search via Lamarckian evolution. In International Conference on Learning Representations. [ www | pdf ]
Liu, H., Simonyan, K., Vinyals, O., Fernando, C. and Kavukcuoglu, K., 2018. Hierarchical representations for efficient architecture search. In International Conference on Learning Representations. [ www | pdf ]
Plappert, M., Houthooft, R., Dhariwal, P., Sidor, S., Chen, R.Y., Chen, X., Asfour, T., Abbeel, P. and Andrychowicz, M., 2018. Parameter space noise for exploration. In International Conference on Learning Representations. [ www | pdf ]
Elsken, T., Metzen, J.H. and Hutter, F., 2018. Efficient multi-objective neural architecture search via Lamarckian evolution. In International Conference on Learning Representations. [ www | pdf ]
Wen, Y., Vicol, P., Ba, J., Tran, D. and Grosse, R., 2018. Flipout: Efficient pseudo-independent weight perturbations on mini-batches. In International Conference on Learning Representations. [ www | pdf ]
Gangwani, T. and Peng, J., 2018. Policy optimization by genetic distillation. In International Conference on Learning Representations. [ www | pdf ]