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Danica J. Sutherland

University of British Columbia

31 papers hereh-index 265.5k citations55 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author3
  • middle author17
  • last author9

Across the 30 of 31 papers where every author was matched, so the position is known.

fields
  • stat.ML18
  • cs.LG6
  • astro-ph.CO2
  • cs.CV2
  • astro-ph.IM1
  • cs.CY1
affiliations
  • University of British Columbia
Homepage
same name
  • Danica J. Sutherland — 15 papers, h 7
  • Danica J. Sutherland — 5 papers
  • Danica J. Sutherland — 5 papers, h 4
  • Danica J. Sutherland — 4 papers, h 2
  • Danica J. Sutherland — 2 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20122023
most citedA Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

81 citations · 540 across the 31 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CY2023★ 65 cited

Queer In AI: A Case Study in Community-Led Participatory AI

Organizers Of QueerInAI, :, Anaelia Ovalle +48

We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community…

cs.LG2023★ 7 cited

Exphormer: Sparse Transformers for Graphs

Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam +2

Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to sca…

cs.LG2023★ 2 cited

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation

Yilin Yang, Kamil Adamczewski, Danica J. Sutherland +2

Maximum mean discrepancy (MMD) is a particularly useful distance metric for differentially private data generation: when used with finite-dimensional features it allows us to summa…

cs.LG2023★ 3 cited

How to prepare your task head for finetuning

Yi Ren, Shangmin Guo, Wonho Bae +1

In deep learning, transferring information from a pretrained network to a downstream task by finetuning has many benefits. The choice of task head plays an important role in fine-t…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.