Yi Ding

According to our database, Yi Ding authored at least 17 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs.

Alex Renda, Yi Ding, Michael Carbin

Proc. ACM Program. Lang., October, 2023

CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms.

Gokul Subramanian Ravi, Pranav Gokhale, Yi Ding, William M. Kirby, Kaitlin N. Smith, Jonathan M. Baker, Peter J. Love, Henry Hoffmann, Kenneth R. Brown, Frederic T. Chong

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2023

2022

Acela: Predictable Datacenter-level Maintenance Job Scheduling.

Yi Ding, Aijia Gao, Thibaud Ryden, Kaushik Mitra, Sukumar Kalmanje, Yanai Golany, Michael Carbin, Henry Hoffmann

CoRR, 2022

SCOPE: Safe Exploration for Dynamic Computer Systems Optimization.

Hyunji Kim, Ahsan Pervaiz, Henry Hoffmann, Michael Carbin, Yi Ding

CoRR, 2022

Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression.

Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann

CoRR, 2022

CAFQA: Clifford Ansatz For Quantum Accuracy.

Gokul Subramanian Ravi, Pranav Gokhale, Yi Ding, William M. Kirby, Kaitlin N. Smith, Jonathan M. Baker, Peter J. Love, Henry Hoffmann, Kenneth R. Brown, Frederic T. Chong

CoRR, 2022

NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction.

Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann

Proceedings of Machine Learning and Systems 2022, 2022

2021

Generalizable and interpretable learning for configuration extrapolation.

Yi Ding, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann

Proceedings of the ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021

Programming with neural surrogates of programs.

Alex Renda, Yi Ding, Michael Carbin

Proceedings of the Onward! 2021: Proceedings of the 2021 ACM SIGPLAN International Symposium on New Ideas, 2021

2020

A polynomial-time algorithm for learning nonparametric causal graphs.

Ming Gao, Yi Ding, Bryon Aragam

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods.

Yi Ding, Panos Toulis

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Generative and multi-phase learning for computer systems optimization.

Yi Ding, Nikita Mishra, Henry Hoffmann

Proceedings of the 46th International Symposium on Computer Architecture, 2019

2017

Multiresolution Kernel Approximation for Gaussian Process Regression.

Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Large Scale Kernel Methods for Online AUC Maximization.

Yi Ding, Chenghao Liu, Peilin Zhao, Steven C. H. Hoi

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

2016

Adaptive Subgradient Methods for Online AUC Maximization.

Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong

CoRR, 2016

2015

An Adaptive Gradient Method for Online AUC Maximization.

Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Learning Relative Similarity by Stochastic Dual Coordinate Ascent.

Pengcheng Wu, Yi Ding, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014