Charlie Dickens

According to our database, Charlie Dickens authored at least 12 papers between 2018 and 2024.

Collaborative distances:

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

2024

Key Compression Limits for <i>k</i>-Minimum Value Sketches.

Charlie Dickens, Eric Bax, Alexander Saydakov

CoRR, 2024

2023

Matching Noisy Keys for Obfuscation.

Charlie Dickens, Eric Bax

Proceedings of the IEEE International Conference on Big Data, 2023

2022

(Nearly) All Cardinality Estimators Are Differentially Private.

Charlie Dickens, Justin Thaler, Daniel Ting

CoRR, 2022

Order-Invariant Cardinality Estimators Are Differentially Private.

Charlie Dickens, Justin Thaler, Daniel Ting

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

On the efficiency of finding and using tabular data summaries: scalability, accuracy, and hardness.

Charlie Dickens

PhD thesis, 2021

Subspace Exploration: Bounds on Projected Frequency Estimation.

Graham Cormode, Charlie Dickens, David P. Woodruff

Proceedings of the PODS'21: Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2021

2020

Ridge Regression with Frequent Directions: Statistical and Optimization Perspectives.

Charlie Dickens

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

Similarity of Neural Networks with Gradients.

Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas C. Damianou

CoRR, 2020

2019

Iterative Hessian Sketch in Input Sparsity Time.

Graham Cormode, Charlie Dickens

CoRR, 2019

2018

Leveraging Well-Conditioned Bases: Streaming \& Distributed Summaries in Minkowski p-Norms.

Graham Cormode, Charlie Dickens, David P. Woodruff

CoRR, 2018

Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms.

Graham Cormode, Charlie Dickens, David P. Woodruff

Proceedings of the 35th International Conference on Machine Learning, 2018