Andrew Jesson
14 papers · 2020–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (5) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (4) π Cross-Pollinator (9)
πΊοΈ
Taxonomy Completionist
(26)
π
Interdisciplinary Bridge
π§
Keyword Pioneer
π€
Dynamic Duo
(10)
β
The Questioner
(2)
ποΈ
Keyword Collector
(64)
π₯
Unstoppable
(6)
π
Century Club
(14)
Conferences
NIPS (6)
ICML (5)
ICLR (2)
UAI (1)
Top co-authors
Keywords
causal inference
(5)
hidden confounding
(3)
sensitivity analysis
(3)
uncertainty quantification
(2)
neural network
(2)
large language model
(2)
covariate shift
(1)
gradient-based optimization
(1)
domain adaptation
(1)
neural network analysis
(1)
model uncertainty
(1)
bayesian model
(1)
model behavior
(1)
hypothesis testing
(1)
causal discovery
(1)
bayesian optimization
(1)
bayesian active learning
(1)
causal effect estimation
(1)
posterior predictive
(1)
experimental design
(1)
Papers
Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective
ICLR 2025
Estimating the Hallucination Rate of Generative AI
NIPS 2024
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
ICML 2024
Hypothesis Testing the Circuit Hypothesis in LLMs
NIPS 2024
Partial identification of dose responses with hidden confounders
UAI 2023
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design
ICML 2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
ICML 2023
Differentiable Multi-Target Causal Bayesian Experimental Design
ICML 2023
Interventions, Where and How? Experimental Design for Causal Models at Scale
NIPS 2022
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
ICLR 2022
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
NIPS 2022
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
ICML 2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
NIPS 2021
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
NIPS 2020