Andrew Jesson
14 papers · 2020–2025 · 4 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (30) π Academic Marathon Β· Sprint π Conference Polyglot Β· Bronze π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
π€
Dynamic Duo
(10)
π
Keyword Champion
(3)
π
Renaissance Researcher
(5)
π₯
Unstoppable Β· Bronze
β
The Questioner
(2)
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(78)
Venues
Top co-authors
Research topics
Machine Learning
(11)
Artificial Intelligence
(10)
Core AI
(10)
Optimization & Theory
(7)
Learning Types
(6)
Knowledge & Reasoning
(3)
Bayesian & Probabilistic
(3)
Reasoning
(3)
Healthcare & Medicine
(2)
Natural Language Processing
(2)
Core Methods
(2)
Resources & Methods
(2)
Deep Learning
(1)
Reinforcement Learning
(1)
Application Areas
(1)
Keywords
causal inference
(5)
sensitivity analysis
(3)
hidden confounding
(3)
large language model
(3)
in-context learning
(2)
uncertainty quantification
(2)
neural network
(2)
experimental design
(2)
bayesian inference
(1)
causal effect estimation
(1)
domain adaptation
(1)
model behavior
(1)
neural network analysis
(1)
model uncertainty
(1)
neural network pruning
(1)
benchmark suite
(1)
hypothesis testing
(1)
gradient-based optimization
(1)
covariate shift
(1)
bayesian model
(1)
Papers
Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective
ICLR 2025
Estimating the Hallucination Rate of Generative AI
NeurIPS 2024
Hypothesis Testing the Circuit Hypothesis in LLMs
NeurIPS 2024
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
ICML 2024
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
ICML 2023
Differentiable Multi-Target Causal Bayesian Experimental Design
ICML 2023
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design
ICML 2023
Partial identification of dose responses with hidden confounders
UAI 2023
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
ICLR 2022
Interventions, Where and How? Experimental Design for Causal Models at Scale
NeurIPS 2022
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
NeurIPS 2022
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
NeurIPS 2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
ICML 2021
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
NeurIPS 2020