Yi Ding

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 19

Top Topics

Computer Systems

Top Venues

Proc. ACM Program. Lang.

Publications

Alex Renda, Yi Ding, Michael Carbin
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. Proc. ACM Program. Lang. 7 (OOPSLA2) (2023)
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
CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms. ASPLOS (1) (2023)
Alex Renda, Yi Ding, Michael Carbin
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. CoRR (2023)
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
CAFQA: Clifford Ansatz For Quantum Accuracy. CoRR (2022)
Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction. CoRR (2022)
Yi Ding, Aijia Gao, Thibaud Ryden, Kaushik Mitra, Sukumar Kalmanje, Yanai Golany, Michael Carbin, Henry Hoffmann
Acela: Predictable Datacenter-level Maintenance Job Scheduling. CoRR (2022)
Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression. CoRR (2022)
Hyunji Kim, Ahsan Pervaiz, Henry Hoffmann, Michael Carbin, Yi Ding
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization. CoRR (2022)
Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction. MLSys (2022)
Alex Renda, Yi Ding, Michael Carbin
Programming with Neural Surrogates of Programs. CoRR (2021)
Yi Ding, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
Generalizable and interpretable learning for configuration extrapolation. ESEC/SIGSOFT FSE (2021)
Alex Renda, Yi Ding, Michael Carbin
Programming with neural surrogates of programs. Onward! (2021)
Ming Gao, Yi Ding, Bryon Aragam
A polynomial-time algorithm for learning nonparametric causal graphs. CoRR (2020)
Yi Ding, Panos Toulis
Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods. AISTATS (2020)
Ming Gao, Yi Ding, Bryon Aragam
A polynomial-time algorithm for learning nonparametric causal graphs. NeurIPS (2020)
Yi Ding, Nikita Mishra, Henry Hoffmann
Generative and multi-phase learning for computer systems optimization. ISCA (2019)
Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler
Multiresolution Kernel Approximation for Gaussian Process Regression. NIPS (2017)
Yi Ding, Chenghao Liu, Peilin Zhao, Steven C. H. Hoi
Large Scale Kernel Methods for Online AUC Maximization. ICDM (2017)
Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong
Adaptive Subgradient Methods for Online AUC Maximization. CoRR (2016)
Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong
An Adaptive Gradient Method for Online AUC Maximization. AAAI (2015)
Pengcheng Wu, Yi Ding, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi
Learning Relative Similarity by Stochastic Dual Coordinate Ascent. AAAI (2014)