International-Conference-on-Machine-Learning-(ICML).md

ICML (International Conference on Machine Learning)

• Archer, A., Fahrbach, M., Liu, K. and Prabhu, P., 2024. Practical performance guarantees for pipelined DNN inference. In International Conference on Machine Learning.
  • Google + MIT
  • "We give a fast and practical pipeline partitioning algorithm called SliceGraph that combines dynamic programming with a biased random-key GA."
• Baluja, S. and Caruana, R., 1995. Removing the genetics from the standard genetic algorithm. In International Conference on Machine Learning (pp. 38-46). Morgan Kaufmann.
  • This is a landmark paper for EDA from Carnegie Mellon University.
• Miconi, T., 2023, July. Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning. In International Conference on Machine Learning (pp. 24756-24774). PMLR. [ www | pdf | openreview | Python ] ( ES | Continuous Optimization #)
• Real, E., Liang, C., So, D. and Le, Q., 2020, November. AutoML-zero: Evolving machine learning algorithms from scratch. In International Conference on Machine Learning (pp. 8007-8019). PMLR. [ www | pdf | C++ ] ( GP | AutoML )
• Wang, R., Lehman, J., Rawal, A., Zhi, J., Li, Y., Clune, J. and Stanley, K., 2020, November. Enhanced POET: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions. In International Conference on Machine Learning (pp. 9940-9951). PMLR. [ www | pdf ] ( ERL )
• Majumdar, S., Khadka, S., Miret, S., Mcaleer, S. and Tumer, K., 2020, November. Evolutionary reinforcement learning for sample-efficient multiagent coordination. In International Conference on Machine Learning (pp. 6651-6660). PMLR. [ www | pdf | Python ] ( ERL )
• Khadka, S., Majumdar, S., Nassar, T., Dwiel, Z., Tumer, E., Miret, S., Liu, Y. and Tumer, K., 2019, May. Collaborative evolutionary reinforcement learning. In International Conference on Machine Learning (pp. 3341-3350). PMLR. [ www | pdf | Python ] ( ERL )
• Metz, L., Maheswaranathan, N., Nixon, J., Freeman, D. and Sohl-Dickstein, J., 2019, May. Understanding and correcting pathologies in the training of learned optimizers. In International Conference on Machine Learning (pp. 4556-4565). PMLR. ( ES | Continuous Optimization #)
• Ilyas, A., Engstrom, L., Athalye, A. and Lin, J., 2018, July. Black-box adversarial attacks with limited queries and information. In International Conference on Machine Learning (pp. 2137-2146). PMLR.
• Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.L., Tan, J., Le, Q.V. and Kurakin, A., 2017, July. Large-scale evolution of image classifiers. In International Conference on Machine Learning (pp. 2902-2911). PMLR. [ www | pdf ] ( NE )
• Akrour, R., Schoenauer, M., Souplet, J.C. and Sebag, M., 2014, June. Programming by feedback. In Proceedings of International Conference on Machine Learning (pp. 1503-1511). [ www ] ( CMA-ES | Continuous Optimization )
• Stulp, F. and Sigaud, O., 2012, June. Path integral policy improvement with covariance matrix adaptation. In Proceedings of International Coference on International Conference on Machine Learning (pp. 1547-1554). [ www | pdf ] ( CMA-ES | Continuous Optimization )
• Yi, S., Wierstra, D., Schaul, T. and Schmidhuber, J., 2009, June. Stochastic search using the natural gradient. In International Conference on Machine Learning (pp. 1161-1168). ACM. [ www ] ( NES )
• Heidrich-Meisner, V. and Igel, C., 2009, June. Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search. In International Conference on Machine Learning (pp. 401-408). ACM. [ www ] ( CMA-ES )
• Strens, M., 2003. Evolutionary MCMC sampling and optimization in discrete spaces. In International Conference on Machine Learning (pp. 736-743). AAAI. [ www | pdf ] ( GA )
• Krawiec, K. and Bhanu, B., 2003, August. Visual learning by evolutionary feature synthesis. In International Conference on Machine Learning (pp. 376-383). AAAI. [ www | pdf ] ( GP + COEA )
• Johnson, J., Tsioutsiouliklis, K. and Giles, C.L., 2003. Evolving strategies for focused web crawling. In International Conference on Machine Learning (pp. 298-305). AAAI. [ www | pdf ] ( GA )
• Fan, J., Lau, R. and Miikkulainen, R., 2003. Utilizing domain knowledge in neuroevolution. In International Conference on Machine Learning (pp. 170-177). AAAI. [ www | pdf ] ( NE )
• Moriarty, D.E. and Miikkulainen, R., 1995. Efficient learning from delayed rewards through symbiotic evolution. In International Conference on Machine Learning (pp. 396-404). Morgan Kaufmann. [ www ] ( NE )
• Gambardella, L.M. and Dorigo, M., 1995. Ant-Q: A reinforcement learning approach to the traveling salesman problem. In International Conference on Machine Learning (pp. 252-260). Morgan Kaufmann. [ www ] ( ACO )
• Lang, K.J., 1995. Hill climbing beats genetic search on a boolean circuit synthesis problem of koza's. In International Conference on Machine Learning (pp. 340-343). Morgan Kaufmann. [ www ] ( RHC )
• Kimura, H., Yamamura, M. and Kobayashi, S., 1995. Reinforcement learning by stochastic hill climbing on discounted reward. In International Conference on Machine Learning (pp. 295-303). Morgan Kaufmann. [ www ] ( RHC )
• Opitz, D.W. and Shavlik, J.W., 1994. Using genetic search to refine knowledge-based neural networks. In International Conference on Machine Learning (pp. 208-216). Morgan Kaufmann. [ www ] ( GA )
• Rosca, J.P. and Ballard, D.H., 1994. Hierarchical self-organization in genetic programming. In International Conference on Machine Learning (pp. 251-258). Morgan Kaufmann. [ www ]
• Baluja, S., 1993, July. The evolution of genetic algorithms: Towards massive parallelism. In International Conference on Machine Learning (pp. 1-8). Morgan Kaufmann. [ www ] ( GA | Parallel )
• De Garis, H., 1990. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In International Conference on Machine Learning 1990 (pp. 132-139). Morgan Kaufmann. [ www ] ( GP )
• Quinlan, J.R., 1988. An empirical comparison of genetic and decision-tree classifiers. In International Conference on Machine Learning (pp. 135-141). Morgan Kaufmann. [ www ] ( GA )
• Robertson, G.G., 1988. Population size in classifier systems. In International Conference on Machine Learning (pp. 142-152). Morgan Kaufmann. [ www ] ( GA )
• Caruana, R.A. and Schaffer, J.D., 1988. Representation and hidden bias: Gray vs. binary coding for genetic algorithms. In International Conference on Machine Learning (pp. 153-161). Morgan Kaufmann. [ www ] ( GA )

2024

Jiang, Y., Yan, R., Yao, X., Zhou, Y., Chen, B. and Yuan, B., HexGen: Generative Inference of Large Language Model over Heterogeneous Environment. In Forty-first International Conference on Machine Learning. [ www | pdf ]

Li, P., Zheng, Y., Tang, H., Fu, X. and Jianye, H.A.O., EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search. In Forty-first International Conference on Machine Learning. [ www | pdf ]

Liu, F., Xialiang, T., Yuan, M., Lin, X., Luo, F., Wang, Z., Lu, Z. and Zhang, Q., 2024, May. Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model. In Forty-first International Conference on Machine Learning. [ www | pdf ]

Dao, M.C., Le Nguyen, P., Truong, T.N. and Hoang, T.N., 2024. Boosting Offline Optimizers with Surrogate Sensitivity. In Forty-first International Conference on Machine Learning. [ www | pdf ]

Hoang, M., Fadhel, A., Deshwal, A., Doppa, J. and Hoang, T.N., 2024. Learning Surrogates for Offline Black-Box Optimization via Gradient Matching. In Forty-first International Conference on Machine Learning. [ www | pdf ]

Song, X., Tian, Y., Lange, R.T., Lee, C., Tang, Y. and Chen, Y., Position: Leverage Foundational Models for Black-Box Optimization. In Forty-first International Conference on Machine Learning. [ www | pdf ]

Zeng, J., Li, C., Sun, Z., Zhao, Q. and Zhou, G., tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs). In Forty-first International Conference on Machine Learning. [ www | pdf ]

Ding, L., Zhang, J., Clune, J., Spector, L. and Lehman, J., Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization. In Forty-first International Conference on Machine Learning. [ www | pdf ]

2020

Angermueller, C., Belanger, D., Gane, A., Mariet, Z., Dohan, D., Murphy, K., Colwell, L. and Sculley, D., 2020, November. Population-based black-box optimization for biological sequence design. In International Conference on Machine Learning (pp. 324-334). PMLR. [ www | pdf ] (Ensemble)

Pacchiano, A., Parker-Holder, J., Tang, Y., Choromanski, K., Choromanska, A. and Jordan, M., 2020, November. Learning to score behaviors for guided policy optimization. In International Conference on Machine Learning (pp. 7445-7454). PMLR. [ www | pdf | Python ]

Goyal, A. and Deng, J., 2020, November. Packit: A virtual environment for geometric planning. In International Conference on Machine Learning (pp. 3700-3710). PMLR. [ www | pdf | Python ] (GA)

Li, C. and Sun, Z., 2020, November. Evolutionary topology search for tensor network decomposition. In International Conference on Machine Learning (pp. 5947-5957). PMLR. [ www | pdf | Python ] (Distributed GA on a Cluster of GPUs)

Xu, J., Tian, Y., Ma, P., Rus, D., Sueda, S. and Matusik, W., 2020, November. Prediction-guided multi-objective reinforcement learning for continuous robot control. In International Conference on Machine Learning (pp. 10607-10616). PMLR. [ www | pdf | Python ]

2019

So, D., Le, Q. and Liang, C., 2019, May. The evolved transformer. In International Conference on Machine Learning (pp. 5877-5886). PMLR. [ www | pdf | Python ]

Brookes, D., Park, H. and Listgarten, J., 2019, May. Conditioning by adaptive sampling for robust design. In International Conference on Machine Learning (pp. 773-782). PMLR.

Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W., Perolat, J., Jaderberg, M. and Graepel, T., 2019, May. Open-ended learning in symmetric zero-sum games. In International Conference on Machine Learning (pp. 434-443). PMLR. [ www | pdf ]

Maheswaranathan, N., Metz, L., Tucker, G., Choi, D. and Sohl-Dickstein, J., 2019, May. Guided evolutionary strategies: Augmenting random search with surrogate gradients. In International Conference on Machine Learning (pp. 4264-4273). PMLR. [ www | pdf | Python ]

Ho, D., Liang, E., Chen, X., Stoica, I. and Abbeel, P., 2019, May. Population based augmentation: Efficient learning of augmentation policy schedules. In International Conference on Machine Learning (pp. 2731-2741). PMLR. [ www | pdf | Python ]

2018

Choromanski, K., Rowland, M., Sindhwani, V., Turner, R. and Weller, A., 2018, July. Structured evolution with compact architectures for scalable policy optimization. In International Conference on Machine Learning (pp. 970-978). PMLR. [ www | pdf ]

Miconi, T., Stanley, K. and Clune, J., 2018, July. Differentiable plasticity: Training plastic neural networks with backpropagation. In International Conference on Machine Learning (pp. 3559-3568). PMLR. [ www | pdf ]

Suganuma, M., Ozay, M. and Okatani, T., 2018, July. Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search. In International Conference on Machine Learning (pp. 4771-4780). PMLR. [ www | pdf | Python ]

Pham, H., Guan, M., Zoph, B., Le, Q. and Dean, J., 2018, July. Efficient neural architecture search via parameters sharing. In International Conference on Machine Learning (pp. 4095-4104). PMLR. [ www | pdf ]

Colas, C., Sigaud, O. and Oudeyer, P.Y., 2018, July. Gep-pg: Decoupling exploration and exploitation in deep reinforcement learning algorithms. In International Conference on Machine Learning (pp. 1039-1048). PMLR. [ www | pdf | Python ]

Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J. and Song, L., 2018, July. Adversarial attack on graph structured data. In International Conference on Machine Learning (pp. 1115-1124). PMLR. [ www | pdf ]