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Addressing the Long-term Impact of ML Decisions via Policy Regret

Addressing the Long-term Impact of ML Decisions via Policy Regret

2 June 2021
David Lindner
Hoda Heidari
Andreas Krause
    OffRL
ArXivPDFHTML

Papers citing "Addressing the Long-term Impact of ML Decisions via Policy Regret"

5 / 5 papers shown
Title
Learning in Markov Games with Adaptive Adversaries: Policy Regret,
  Fundamental Barriers, and Efficient Algorithms
Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
Thanh Nguyen-Tang
Raman Arora
74
1
0
01 Nov 2024
Weighted Tallying Bandits: Overcoming Intractability via Repeated
  Exposure Optimality
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
Dhruv Malik
Conor Igoe
Yuanzhi Li
Aarti Singh
OffRL
16
1
0
04 May 2023
Mitigating Disparity while Maximizing Reward: Tight Anytime Guarantee
  for Improving Bandits
Mitigating Disparity while Maximizing Reward: Tight Anytime Guarantee for Improving Bandits
Vishakha Patil
V. Nair
Ganesh Ghalme
Arindam Khan
12
4
0
19 Aug 2022
Complete Policy Regret Bounds for Tallying Bandits
Complete Policy Regret Bounds for Tallying Bandits
Dhruv Malik
Yuanzhi Li
Aarti Singh
OffRL
15
2
0
24 Apr 2022
How Algorithmic Confounding in Recommendation Systems Increases
  Homogeneity and Decreases Utility
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
A. Chaney
Brandon M Stewart
Barbara E. Engelhardt
CML
169
312
0
30 Oct 2017
1