Improving the energy efficiency of relational and NoSQL databases via query optimizations. - Divya Mahajan 001; Cody Blakeney; Ziliang Zong (Sustain. Comput. Informatics Syst., 2019)

Craft Distillation: Layer-wise Convolutional Neural Network Distillation. - Cody Blakeney; Xiaomin Li; Yan Yan 002; Ziliang Zong (CSCloud/EdgeCom, 2020)

Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity. - Cody Blakeney; Yan Yan 002; Ziliang Zong (WACV, 2020)

Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation. - Cody Blakeney; Nathaniel Huish; Yan Yan 002; Ziliang Zong (CoRR, 2021)

Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks. - Cody Blakeney; Gentry Atkinson; Nathaniel Huish; Yan Yan 002; Vangelis Metsis; Ziliang Zong (CoRR, 2021)

Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression. - Cody Blakeney; Xiaomin Li; Yan Yan 002; Ziliang Zong (IEEE Trans. Parallel Distributed Syst., 2021)

Measuring Bias and Fairness in Multiclass Classification. - Cody Blakeney; Gentry Atkinson; Nathaniel Huish; Yan Yan 002; Vangelis Metsis; Ziliang Zong (NAS, 2022)

Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation. - Cody Blakeney; Jessica Zosa Forde; Jonathan Frankle; Ziliang Zong; Matthew L. Leavitt (CoRR, 2022)

Does your data spark joy? Performance gains from domain upsampling at the end of training. - Cody Blakeney; Mansheej Paul; Brett W. Larsen; Sean Owen; Jonathan Frankle (CoRR, 2024)

LoRA Learns Less and Forgets Less. - Dan Biderman; Jacob P. Portes; Jose Javier Gonzalez Ortiz; Mansheej Paul; Philip Greengard; Connor Jennings; Daniel King; Sam Havens; Vitaliy Chiley; Jonathan Frankle; Cody Blakeney; John Patrick Cunningham (Trans. Mach. Learn. Res., 2024)

Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models. - Zachary Ankner; Cody Blakeney; Kartik Sreenivasan; Max Marion; Matthew L. Leavitt; Mansheej Paul (ICLR, 2025)

BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining. - Pratyush Maini; Vineeth Dorna; Parth Doshi; Aldo Carranza; Fan Pan; Jack Urbanek; Paul Burstein; Alex Fang; Alvin Deng; Amro Abbas; Brett W. Larsen; Cody Blakeney; et al. (CoRR, 2025)