← Authors

Bo Waggoner

18 papers · 2015–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer πŸƒ Academic Marathon (10) 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (29) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸ† Keyword Champion (2) πŸ’Ž Century Club (18) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (5) πŸ—ƒοΈ Keyword Collector (88)

Conferences

NIPS (11) AAAI (2) COLT (2) AISTATS (1) ICML (1) JMLR (1)

Top co-authors

Rafael Frongillo (8) Aaron Roth (4) Jessie Finocchiaro (3) Rafael M. Frongillo (3) Tom Morgan (2) Steven Z. Wu (2) Jessica Finocchiaro (2) Dhamma Kimpara (2) Katrina Ligett (2) Jonathan Ullman (1)

Research topics

Differential Privacy (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
Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification NIPS 2024
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates JMLR 2024
Proper Losses for Discrete Generative Models ICML 2023
Unifying lower bounds on prediction dimension of convex surrogates NIPS 2021
Surrogate Regret Bounds for Polyhedral Losses NIPS 2021
Embedding Dimension of Polyhedral Losses COLT 2020
Preventing Arbitrage from Collusion When Eliciting Probabilities AAAI 2020
Toward a Characterization of Loss Functions for Distribution Learning NIPS 2019
An Embedding Framework for Consistent Polyhedral Surrogates NIPS 2019
Multi-Observation Regression AISTATS 2019
Equal Opportunity in Online Classification with Partial Feedback NIPS 2019
Local Differential Privacy for Evolving Data NIPS 2018
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem NIPS 2018
Bounded-Loss Private Prediction Markets NIPS 2018
Multi-Observation Elicitation COLT 2017
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM NIPS 2017
A Market Framework for Eliciting Private Data NIPS 2015