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researcher

Anastasios Nikolas Angelopoulos

University of California, Berkeley

32 papers hereh-index 246.1k citations43 works total

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

author position
  • sole author1
  • first author16
  • middle author14

Across the 31 of 32 papers where every author was matched, so the position is known.

fields
  • cs.LG15
  • stat.ML5
  • cs.CV4
  • stat.ME3
  • cs.AI1
  • cs.GR1
affiliations
  • University of California, Berkeley
HomepageORCID 0000-0001-9787-0579

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedGaze-Contingent Ocular Parallax Rendering for Virtual Reality

75 citations · 347 across the 29 of their papers we have counts for

collaborators
Showing 2023 · stat.MLShow all

4 papers · 2 filters

stat.ML2023★ 9 cited

PPI++: Efficient Prediction-Powered Inference

Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic

We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning pr…

stat.ML2023

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy +2

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitou…

stat.ML2023★ 12 cited

Class-Conditional Conformal Prediction with Many Classes

Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates +2

Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a u…

stat.ML2023

Prediction-Powered Inference

Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang +2

Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.…

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