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Online Multivalid Learning: Means, Moments, and Prediction Intervals

Online Multivalid Learning: Means, Moments, and Prediction Intervals

Information Technology Convergence and Services (ITCS), 2021
5 January 2021
Varun Gupta
Christopher Jung
Georgy Noarov
Mallesh M. Pai
Aaron Roth
ArXiv (abs)PDFHTML

Papers citing "Online Multivalid Learning: Means, Moments, and Prediction Intervals"

34 / 34 papers shown
Efficient Swap Multicalibration of Elicitable Properties
Efficient Swap Multicalibration of Elicitable Properties
Lunjia Hu
Haipeng Luo
Spandan Senapati
Vatsal Sharan
143
1
0
07 Nov 2025
In Defense of Defensive Forecasting
In Defense of Defensive Forecasting
Juan Carlos Perdomo
Benjamin Recht
316
2
0
13 Jun 2025
Improved Bounds for Swap Multicalibration and Swap Omniprediction
Improved Bounds for Swap Multicalibration and Swap Omniprediction
Haipeng Luo
Spandan Senapati
Willie Neiswanger
438
2
0
27 May 2025
Improved and Oracle-Efficient Online $\ell_1$-Multicalibration
Improved and Oracle-Efficient Online ℓ1\ell_1ℓ1​-Multicalibration
Rohan Ghuge
Vidya Muthukumar
Sahil Singla
443
0
0
23 May 2025
Three Types of Calibration with Properties and their Semantic and Formal Relationships
Three Types of Calibration with Properties and their Semantic and Formal Relationships
Rabanus Derr
Jessie Finocchiaro
Robert C. Williamson
408
2
0
25 Apr 2025
Revisiting the Predictability of Performative, Social Events
Revisiting the Predictability of Performative, Social Events
Juan C. Perdomo
351
7
0
12 Mar 2025
Tractable Agreement Protocols
Tractable Agreement ProtocolsSymposium on the Theory of Computing (STOC), 2024
Natalie Collina
Surbhi Goel
Varun Gupta
Aaron Roth
309
10
0
29 Nov 2024
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Jessica Dai
Nika Haghtalab
Eric Zhao
345
3
0
18 Oct 2024
Calibrated Probabilistic Forecasts for Arbitrary Sequences
Calibrated Probabilistic Forecasts for Arbitrary Sequences
Charles Marx
Volodymyr Kuleshov
Stefano Ermon
AI4TS
331
3
0
27 Sep 2024
Fair Risk Control: A Generalized Framework for Calibrating Multi-group
  Fairness Risks
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness RisksInternational Conference on Machine Learning (ICML), 2024
Lujing Zhang
Aaron Roth
Linjun Zhang
FaML
332
11
0
03 May 2024
On Computationally Efficient Multi-Class Calibration
On Computationally Efficient Multi-Class Calibration
Parikshit Gopalan
Lunjia Hu
G. Rothblum
351
15
0
12 Feb 2024
Oracle Efficient Online Multicalibration and Omniprediction
Oracle Efficient Online Multicalibration and OmnipredictionACM-SIAM Symposium on Discrete Algorithms (SODA), 2023
Sumegha Garg
Christopher Jung
Omer Reingold
Aaron Roth
319
31
0
18 Jul 2023
Scalable Membership Inference Attacks via Quantile Regression
Scalable Membership Inference Attacks via Quantile RegressionNeural Information Processing Systems (NeurIPS), 2023
Martín Bertrán
Shuai Tang
Michael Kearns
Jamie Morgenstern
Aaron Roth
Zhiwei Steven Wu
MIACV
302
79
0
07 Jul 2023
HappyMap: A Generalized Multi-calibration Method
HappyMap: A Generalized Multi-calibration MethodInformation Technology Convergence and Services (ITCS), 2023
Zhun Deng
Cynthia Dwork
Linjun Zhang
583
23
0
08 Mar 2023
A Unifying Perspective on Multi-Calibration: Game Dynamics for
  Multi-Objective Learning
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective LearningNeural Information Processing Systems (NeurIPS), 2023
Nika Haghtalab
Michael I. Jordan
Eric Zhao
448
27
0
21 Feb 2023
The Scope of Multicalibration: Characterizing Multicalibration via
  Property Elicitation
The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation
Georgy Noarov
Aaron Roth
277
5
0
16 Feb 2023
Swap Agnostic Learning, or Characterizing Omniprediction via
  Multicalibration
Swap Agnostic Learning, or Characterizing Omniprediction via MulticalibrationNeural Information Processing Systems (NeurIPS), 2023
Parikshit Gopalan
Michael P. Kim
Omer Reingold
278
31
0
13 Feb 2023
From Pseudorandomness to Multi-Group Fairness and Back
From Pseudorandomness to Multi-Group Fairness and BackAnnual Conference Computational Learning Theory (COLT), 2023
Cynthia Dwork
Daniel Lee
Huijia Lin
Pranay Tankala
FaML
443
16
0
21 Jan 2023
Batch Multivalid Conformal Prediction
Batch Multivalid Conformal PredictionInternational Conference on Learning Representations (ICLR), 2022
Christopher Jung
Georgy Noarov
Ramya Ramalingam
Aaron Roth
592
69
0
30 Sep 2022
Fair admission risk prediction with proportional multicalibration
Fair admission risk prediction with proportional multicalibrationACM Conference on Health, Inference, and Learning (ACM CHIL), 2022
William La Cava
Elle Lett
Guangya Wan
252
11
0
29 Sep 2022
Multicalibrated Regression for Downstream Fairness
Multicalibrated Regression for Downstream FairnessAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022
Ira Globus-Harris
Varun Gupta
Christopher Jung
Michael Kearns
Jamie Morgenstern
Aaron Roth
FaML
292
13
0
15 Sep 2022
Reconciling Individual Probability Forecasts
Reconciling Individual Probability ForecastsConference on Fairness, Accountability and Transparency (FAccT), 2022
Aaron Roth
A. Tolbert
S. Weinstein
246
22
0
04 Sep 2022
Practical Adversarial Multivalid Conformal Prediction
Practical Adversarial Multivalid Conformal PredictionNeural Information Processing Systems (NeurIPS), 2022
Osbert Bastani
Varun Gupta
Christopher Jung
Georgy Noarov
Ramya Ramalingam
Aaron Roth
445
70
0
02 Jun 2022
Decision-Making under Miscalibration
Decision-Making under MiscalibrationInformation Technology Convergence and Services (ITCS), 2022
G. Rothblum
G. Yona
252
6
0
18 Mar 2022
Low-Degree Multicalibration
Low-Degree MulticalibrationAnnual Conference Computational Learning Theory (COLT), 2022
Parikshit Gopalan
Michael P. Kim
M. Singhal
Shengjia Zhao
FaMLUQCV
383
52
0
02 Mar 2022
An Algorithmic Framework for Bias Bounties
An Algorithmic Framework for Bias BountiesConference on Fairness, Accountability and Transparency (FAccT), 2022
Ira Globus-Harris
Michael Kearns
Aaron Roth
FedML
576
32
0
25 Jan 2022
Simple and near-optimal algorithms for hidden stratification and
  multi-group learning
Simple and near-optimal algorithms for hidden stratification and multi-group learningInternational Conference on Machine Learning (ICML), 2021
Abdoreza Asadpour
Daniel J. Hsu
343
28
0
22 Dec 2021
Scaffolding Sets
Scaffolding Sets
M. Burhanpurkar
Zhun Deng
Cynthia Dwork
Linjun Zhang
265
9
0
04 Nov 2021
Sample-Efficient Safety Assurances using Conformal Prediction
Sample-Efficient Safety Assurances using Conformal Prediction
Rachel Luo
Shengjia Zhao
Jonathan Kuck
Boris Ivanovic
Silvio Savarese
Edward Schmerling
Marco Pavone
624
70
0
28 Sep 2021
Online Minimax Multiobjective Optimization: Multicalibeating and Other
  Applications
Online Minimax Multiobjective Optimization: Multicalibeating and Other ApplicationsNeural Information Processing Systems (NeurIPS), 2021
Daniel Lee
Georgy Noarov
Mallesh M. Pai
Aaron Roth
317
24
0
09 Aug 2021
A Unified Approach to Fair Online Learning via Blackwell Approachability
A Unified Approach to Fair Online Learning via Blackwell Approachability
Evgenii Chzhen
Christophe Giraud
Jean-Michel Poggi
FaML
262
15
0
23 Jun 2021
Multi-group Agnostic PAC Learnability
Multi-group Agnostic PAC LearnabilityInternational Conference on Machine Learning (ICML), 2021
G. Rothblum
G. Yona
FaML
331
48
0
20 May 2021
Lexicographically Fair Learning: Algorithms and Generalization
Lexicographically Fair Learning: Algorithms and GeneralizationSymposium on Foundations of Responsible Computing (FRC), 2021
Emily Diana
Wesley Gill
Ira Globus-Harris
Michael Kearns
Aaron Roth
Saeed Sharifi-Malvajerdi
FedMLFaML
246
9
0
16 Feb 2021
Private Prediction Sets
Private Prediction Sets
Anastasios Nikolas Angelopoulos
Stephen Bates
Tijana Zrnic
Sai Li
338
17
0
11 Feb 2021
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