Dave Zachariah
14 papers · 2017–2026 · 5 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (28)
🧭
Keyword Pioneer
🗃️
Keyword Collector
(70)
💎
Century Club · Bronze
🔥
Unstoppable · Silver
🌈
Renaissance Researcher
(5)
Top co-authors
Research topics
Machine Learning
(13)
Optimization & Theory
(9)
Core Methods
(7)
Artificial Intelligence
(6)
Core AI
(5)
Mathematics & Optimization
(3)
Application Areas
(3)
Learning Types
(3)
Knowledge & Reasoning
(2)
Reasoning
(2)
Data Science & Analytics
(1)
Deep Learning
(1)
Healthcare & Medicine
(1)
Bayesian & Probabilistic
(1)
Clinical
(1)
Keywords
uncertainty quantification
(3)
causal inference
(2)
causal effect inference
(2)
adversarial training
(2)
ridge regression
(2)
robust optimization
(2)
observational data
(2)
conformal prediction
(2)
pareto efficiency
(1)
expectation maximization
(1)
variational inference
(1)
model calibration
(1)
linear classification
(1)
risk management
(1)
multi-class classification
(1)
convex optimization
(1)
gaussian process regression
(1)
cramér-rao bound
(1)
decision policy
(1)
domain adaptation
(1)
Papers
Learning plug-in surrogate endpoints for randomized experiments
UAI 2026
Efficient Optimization Algorithms for Linear Adversarial Training
AISTATS 2025
Adaptive Robust Learning using Latent Bernoulli Variables
ICML 2024
Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data
NeurIPS 2024
Regularization properties of adversarially-trained linear regression
NeurIPS 2023
Learning Pareto-Efficient Decisions with Confidence
AISTATS 2022
Calibration tests beyond classification
ICLR 2021
Inference of causal effects when control variables are unknown
UAI 2021
Learning Robust Decision Policies from Observational Data
NeurIPS 2020
Calibration tests in multi-class classification: A unifying framework
NeurIPS 2019
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
ICML 2019
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
NeurIPS 2019
Learning Localized Spatio-Temporal Models From Streaming Data
ICML 2018
Prediction Performance After Learning in Gaussian Process Regression
AISTATS 2017