Melanie Schmidt

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #7,204 of 35,345 by trust here, #11,327 by citations
• Computational Theory and Mathematics — 23 citations into it; #1,948 of 12,182 by trust here, #2,629 by citations
• Signal Processing — 17 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Computer Networks and Communications — 14 citations into it
• Computer Graphics and Computer-Aided Design — 12 citations into it
• Algorithms & Complexity — 11 citations into it
• Computational Mechanics — 10 citations into it; #2,305 of 10,001 by trust here, #3,190 by citations

Whom Melanie Schmidt trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David Arthur
2. Dan Feldman
3. Flavio Chierichetti
4. Sudipto Guha
5. Teofilo F. Gonzalez
6. Sergei Vassilvitskii
7. Anthony W. Flores
8. Sariel Har-Peled
9. Michael B. Cohen
10. Hendrik Fichtenberger

Who trusts Melanie Schmidt overall

1. David P. Woodruff
2. Sina Baharlouei
3. Dan Feldman
4. Suman Kalyan Bera
5. Trevor Campbell
6. Maher Nouiehed
7. Mario Lucic
8. Meisam Razaviyayn
9. Ioana Orianna Bercea
10. Christos Boutsidis

Papers from Melanie Schmidt's perspective

Ordered by how strongly the researchers Melanie Schmidt trusts endorse each paper. Same corpus, a different order for every researcher.

1. On coresets for k-means and k-median clustering
2. Certifying and Removing Disparate Impact
3. k-means++: the advantages of careful seeding
4. Least squares quantization in PCM
5. Clustering to Minimize the Maximum Intercluster Distance
6. Equality of Opportunity in Supervised Learning
7. Algorithmic Decision Making and the Cost of Fairness
8. Approximating extent measures of points (JACM)
9. Distinctive Image Features from Scale-Invariant Keypoints
10. Clustering Data Streams: Theory and Practice

Their papers

• On the cost of essentially fair clusterings (2018) — 120 citations
• Fair Coresets and Streaming Algorithms for Fair k-Means Clustering (2018) — 42 citations
• Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering (2013) — 591 citations
• Privacy preserving clustering with constraints (2018) — 122 citations
• BICO: BIRCH Meets Coresets for k-Means Clustering (2013) — 79 citations