Bo Waggoner
18 papers · 2015–2025 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (29) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
π§
Keyword Pioneer
π
Keyword Champion
(2)
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Trend Setter
π₯
Unstoppable Β· Bronze
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Century Club Β· Bronze
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(89)
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory
(13)
Core Methods
(10)
Mathematics & Optimization
(7)
Optimization
(7)
Application Areas
(5)
Learning Types
(5)
Artificial Intelligence
(4)
Core AI
(4)
Deep Learning
(1)
Reinforcement Learning
(1)
Security & Privacy
(1)
Bayesian & Probabilistic
(1)
Differential Privacy
(1)
Methods
(1)
Keywords
polyhedral loss
(4)
mechanism design
(4)
convex surrogate
(3)
empirical risk minimization
(3)
convex optimization
(2)
incentive compatibility
(2)
polyhedral surrogate
(2)
proper loss
(2)
online learning
(2)
link function
(2)
privacy preservation
(2)
convex surrogate loss
(2)
prediction market
(2)
property elicitation
(2)
differential privacy
(2)
surrogate loss
(2)
loss function
(2)
online classification
(1)
structured prediction
(1)
regret analysis
(1)
Papers
Forecasting Competitions with Correlated Events
AAAI 2025
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates
JMLR 2024
Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification
NeurIPS 2024
Proper Losses for Discrete Generative Models
ICML 2023
Surrogate Regret Bounds for Polyhedral Losses
NeurIPS 2021
Unifying lower bounds on prediction dimension of convex surrogates
NeurIPS 2021
Embedding Dimension of Polyhedral Losses
COLT 2020
Preventing Arbitrage from Collusion When Eliciting Probabilities
AAAI 2020
An Embedding Framework for Consistent Polyhedral Surrogates
NeurIPS 2019
Equal Opportunity in Online Classification with Partial Feedback
NeurIPS 2019
Multi-Observation Regression
AISTATS 2019
Toward a Characterization of Loss Functions for Distribution Learning
NeurIPS 2019
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
NeurIPS 2018
Bounded-Loss Private Prediction Markets
NeurIPS 2018
Local Differential Privacy for Evolving Data
NeurIPS 2018
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
NeurIPS 2017
Multi-Observation Elicitation
COLT 2017
A Market Framework for Eliciting Private Data
NeurIPS 2015