Approximation Guarantees for Adaptive Sampling

By Eric Balkanski, Yaron Singer. 2018

30 citations recorded by the source corpus.

Subfields: Statistical Learning Theory, Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical Learning Theory

1. Sharon Qian
2. Yaron Singer
3. Adam Breuer
4. Eric Balkanski
5. Yonatan Belinkov
6. Daniel Nevo

In Algorithms & Complexity

1. Sharon Qian
2. Yaron Singer
3. Stuart M. Shieber
4. Jesse Vig
5. Yonatan Belinkov
6. Sebastian Gehrmann
7. Daniel Nevo
8. Adam Breuer

Citations in the corpus

• Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias (2020, methodology): “Alternative methods using adaptive sampling techniques have been proposed to speed-up G REEDY for submodular functions under cardinality constraints (Ene and Nguyen, 2019; Fahrbach et al., 2019b; Balkanski and Singer, 2018a,b).”
• Fast Parallel Algorithms for Feature Selection (2019, methodology): “…for submodular maximization under varying constraints Balkanski et al. (2019); Balkanski & Singer (2018b); Chekuri & Quanrud (2019, 2018); Balkanski et al. (2018a); Chen et al. (2018); Ene et al. (2018a); Fahrbach et al. (2019); Balkanski et al. (2018b); Ene & Nguyen (2019); Ene et al. (2018b).”
• Non-monotone Submodular Maximization in Exponentially Fewer Iterations (2018, methodology): “For ease of presentation and notation, we analyze the idealized version of the algorithm, which easily extends to the algorithm with estimates and guesses as in [BS18a, BS18b, BRS18].”