Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms. - Graham Cormode; Charlie Dickens; David P. Woodruff (ICML, 2018)

Leveraging Well-Conditioned Bases: Streaming & Distributed Summaries in Minkowski p-Norms. - Graham Cormode; Charlie Dickens; David P. Woodruff (CoRR, 2018)

Iterative Hessian Sketch in Input Sparsity Time. - Graham Cormode; Charlie Dickens (CoRR, 2019)

Similarity of Neural Networks with Gradients. - Shuai Tang; Wesley J. Maddox; Charlie Dickens; Tom Diethe; Andreas C. Damianou (CoRR, 2020)

Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams. - Charlie Dickens; Eric Meissner; Pablo G. Moreno; Tom Diethe (CoRR, 2020)

Ridge Regression with Frequent Directions: Statistical and Optimization Perspectives. - Charlie Dickens (CoRR, 2020)

Subspace Exploration: Bounds on Projected Frequency Estimation. - Graham Cormode; Charlie Dickens; David P. Woodruff (PODS, 2021)

On the efficiency of finding and using tabular data summaries: scalability, accuracy, and hardness. - Charlie Dickens (2021)

Order-Invariant Cardinality Estimators Are Differentially Private. - Charlie Dickens; Justin Thaler; Daniel Ting (NeurIPS, 2022)

(Nearly) All Cardinality Estimators Are Differentially Private. - Charlie Dickens; Justin Thaler; Daniel Ting (CoRR, 2022)

Matching Noisy Keys for Obfuscation. - Charlie Dickens; Eric Bax (IEEE Big Data, 2023)

Key Compression Limits for k-Minimum Value Sketches. - Charlie Dickens; Eric Bax; Alexander Saydakov (IEEE Big Data, 2024)

Key Compression Limits for k-Minimum Value Sketches. - Charlie Dickens; Eric Bax; Alexander Saydakov (CoRR, 2024)