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 Additional Paper List for ML based Optimisation

Additional papers on optimisation in Computer Systems for extended reading. When you get free time, pick any interesting paper to read!

A1. G. Venkates et al.: Accelerating Deep Convolutional Networks using low-precision and sparsity,  ICASSP, 2017.

A2. V. Mnih et al.: Asynchronous Methods for Deep Reinforcement Learning, ICML, 2016.

A3. B. Bodin, L. Nardi, MZ Zia et al.: Integrating Algorithmic Parameters into Benchmarking and Design Space Exploration in 3D Scene Understanding, PACT, 2016. 

A4. V. Mnih et al.: Playing Atari with Deep Reinforcement Learning, NIPS, 2013.

A5.. S. Palkar, J. Thomas, A. Shanbhagy, D. Narayanan, H. Pirky, M. Schwarzkopfy, S. Amarasinghey, and M. Zaharia:
Weld: A Common Runtime for High Performance Data Analytics, CIDR, 2017.

A6. D. Kingma, J. Ba: Adam: A Method for Stochastic Optimization, ICLR, 2015.

A7. Z. Jia, S. Lin, R. Ying, J. You, J. Leskovec,  A. Aiken: Redundancy-Free Computation Graphs for Graph Neural Networks: ArXiv, 2019.

A8. N. K. Ahmed, et al.: On Sampling from Massive Graph Streams, VLDB, 2017.

A9. G. Malkomes, B. Cheng, E. Hans Lee, and M. McCourt: Beyond the Pareto Efficient Frontier: Constraint Active Search for Multi-objective Experimental Design, PLMR, 2021.  

A10. J. Maronas, O. Hamelijnck, J. Knoblauch, and T. Damoula: Transforming Gaussian Processes With Normalizing Flows, , AISTATS, 2021.  

A11. F. Yang et al.: LFTF: A Framework for Efficient Tensor Analytics at Scale, VLDB, 2017.

A12. H. Mao et al.: Neural Adaptive Video Streaming with Pensieve, SIGCOMM, 2017.

A13.. K. LaCurts et al.: Cicada: Introducing Predictive Guarantees for Cloud Networks, HOTCLOUD, 2014.

A14. H. Hoffmann et al.: Dynamic Knobs for Responsive Power-Aware Computing, Asplos, 2011.

A15. N.J. Yadwadkar, B. Hariharan, J. Gonzalez and R. Katz: Faster Jobs in Distributed Data Processing using Multi-Task Learning, SDM, 2015.

A16. X. Dutreih et al.: Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: Towards a Fully Automated Workflow, ICAS, 2011.

A17. J. Eastep et al.: Smart Data Structures: An Online Machine Learning Approach to Multicore Data Structures, ICAC, 2011.

A18. E. Ipek et al.: Self-Optimizing Memory Controllers: A Reinforcement Learning Approach, ISCA, 2008.

A19. S. Teerapittayanon et al.: Distributed Deep Neural Networks over the Cloud, the Edge and End Devices, ICDCS, 2017.

A20. D. Baylor et al.: TFX: A TensorFlow-Based Production-Scale Machine Learning Platform, KDD, 2017.

A21. H. Mao et al.: Resource Management with Deep Reinforcement Learning, HotNets, 2016.

A22. M. Raghu et al.: On the Expressive Power of Deep Neural Networks, PMLR, 2017. 

A23. E. Lambart et al.: Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning, IEEE Robotics and Automation Letters, 2019.

A24. Y. Kang et al.: Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge, ASPLOS, 2017.

A25. Y. You et al.: Scaling Deep Learning on GPU and Knights Landing clusters, SC, 2017.

A26. Kunjir, M. and Babu, S.: Black or White? How to Develop an AutoTuner for Memory-based Analytics, SIGMOD, 2020.

A27. K. Tzoumas, A. Deshpande, and C. S. Jensen: Efficiently adapting graphical models for selectivity estimation, VLDB, 2013.

A28. L. Spiegelberg, R. Yesantharao, M. Schwarzkopf, T. Kraska: Tuplex: Data Science in Python at Native Code Speed, SIGMOD, 2021., SIGMOD, 2021.

A29. U. MisraRichard, L. Dunlap et al.: RubberBand: Cloud-based Hyperparameter Tuning, EuroSys, 2021.

A30. F. Hutter, H.H. Hoos, and K. Leyton-Brown: Sequential model-based optimization for general algorithm configuration, International conference on learning and intelligent optimization, 2011.

A31. J. Bergstra, Y. Bengio: Random search for hyper-parameter optimization, Journal of machine learning research, 2012.

A32. B. Teabe et al.: Application-specific quantum for multi-core platform scheduler, EuroSys, 2016.

A33. L. Ma, W. Zhang, J. Jiao, W. Wang, M. Butrovich, W.S. Lim, P. Menon, and A. Pavlo: MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems, SIGMOD, 2021.  

A34. R. Krishna, M.S. Iqbal, M.A. Javidian, B. Ray, and P. Jamshidi: CADET: Debugging and Fixing Misconfigurations using Counterfactual Reasoning, UAI, 2021.  

A35. I. Gog, M. Schwarzkopf, A. Gleave, R. Watson, S. Hand: Firmament: fast, centralized cluster scheduling at scale, OSDI, 2016.

A36. B. Zoph et al.: Learning Transferable Architectures for Scalable Image Recognition, arXiv, 2017.

A37. D. Golovin et al.: Google Vizier: A Service for Black-Box Optimization, KDD, 2017.

A38. M. Carvalho et al.: Long-term SLOs for reclaimed cloud computing resources, SOCC, 2014.

A39. A. Ratner, S. Bach, H. Ehrenberg, J. Fries, S. Wu, and C. Ré: Snorkel: Rapid Training Data Creation with Weak Supervision, VLDB, 2017.

A40. A. Ratner, B. Hancock, J. Dunnmon, R. Goldman, and C. Ré: Snorkel MeTaL: Weak Supervision for Multi-Task Learning, DEEM, 2018.

A41. A. Koliousis, P. Watcharapichat, M. Weidlich, L. Mai, P. Costa, P. Pietzuch: CROSSBOW: Scaling Deep Learning with Small Batch Sizes on MultiGPU Servers, VLDB, 2019.

A42. Ł. Kaiser et al.: Model Based Reinforcement Learning for Atari, arXiv, 2019.  

A43. H. Liu, K. Simonyan, and Y. Yang: DARTS: Differentiable Architecture Search, arXiv, 2018.  

A44.. M. Jaderberg, V. Dalibard, S. Osindero, W.M. Czarnecki: Population based training of neural networks, arXiv, 2017.  

A45. L. Li te al.: A System for Massively Parallel Hyperparameter Tuning, MLSys, 2020. 

A46. H. Dai, E. Khalil, Y. Zhang, B. Dilkina, L. Song: Learning Combinatorial Optimization Algorithms over Graphs, NIPS, 2017.

A47. J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, A. Ng.: Large scale distributed deep networks. NIPS, 2012.

A48. F. Hutter, et al.: An evaluation of sequential model-based optimization for expensive blackbox functions, GECCO, 2013.  

A49. Chu, C. et al.: Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning, PMLR, 2019.

A50. N. Goodman, V. Mansinghka, D. Roy, K. onawitz, J. Tenenbaum: Church: a language for generative models. In Proceedings of the Conference on Uncertainty in Arti cial Intelligence, UAI, 2008.

A51. T. Rainforth et al.: Bayesian Optimization for Probabilistic Programs, NIPS, 2016. 

A52. G. Tesauro et al.: A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation, ICAC, 2006.

A53.T. Domhan, J. T. Springenberg, F. Hutter: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves, IJCAI, 2015.

A54. F. Hutter et al.: Algorithm runtime prediction: Methods&evaluation, Elsevier J. AI, 2014.

A55.. S. Palkar, J. Thomas, D. Narayanan, P. Thaker, R. Palamuttam, P. Negi, A. Shanbhag, M. Schwarzkopf, H. Pirk, S. Amarasinghe, S. Madden, M. Zaharia: Evaluating End-to-End Optimization for Data Analytics Applications in Weld, VLDB, 2018.

A56. H. Zhang et al.: Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters, ATC, 2017.

A57. S. Venkataraman et al.: Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics, NSDI, 2016.

A58. H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, M. Alizadeh: Learning Scheduling Algorithms for Data Processing Clusters, SIGCOMM, 2019.

A59. C. Delimitrou et al.: Quasar: Resource-Efficient and QoS-Aware Cluster Management, ASPLOS, 2014.