Jean-Francois Ton
20 papers · 2019–2025 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
π Academic Marathon (6) π§ Keyword Pioneer π Interdisciplinary Bridge π Conference Polyglot (7) π Cross-Pollinator (13)
π
Cross-Pollinator
(13)
π
Renaissance Researcher
(7)
πΊοΈ
Taxonomy Completionist
(33)
π
Grand Slam
π
Century Club
(20)
β‘
Prolific Year
(5)
ποΈ
Keyword Collector
(72)
π₯
Unstoppable
(7)
Conferences
NIPS (6)
ICML (5)
ICLR (4)
AISTATS (2)
AAAI (1)
JMLR (1)
NAACL (1)
Top co-authors
Keywords
uncertainty quantification
(3)
off-policy evaluation
(2)
kernel mean embedding
(2)
kernel methods
(2)
contextual bandit
(2)
bayesian inference
(2)
random fourier feature
(2)
leverage score
(2)
average treatment effect
(2)
ridge regression
(2)
causal inference
(2)
out-of-distribution generalization
(1)
variational inference
(1)
reinforcement learning from human feedback
(1)
language model alignment
(1)
high-dimensional inference
(1)
conformal prediction
(1)
reinforcement learning
(1)
minimax bounds
(1)
epistemic uncertainty
(1)
Papers
Rethinking Reward Modeling in Preference-based Large Language Model Alignment
ICLR 2025
ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration
ICLR 2025
Understanding Chain-of-Thought in LLMs through Information Theory
ICML 2025
Active Reward Modeling: Adaptive Preference Labeling for Large Language Model Alignment
ICML 2025
Mitigating Reward Overoptimization via Lightweight Uncertainty Estimation
NIPS 2024
Fair Classifiers that Abstain without Harm
ICLR 2024
Achievable Fairness on Your Data With Utility Guarantees
NIPS 2024
Invariant Learning via Probability of Sufficient and Necessary Causes
NIPS 2023
Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits
NIPS 2023
Regularized Training of Nearest Neighbor Language Models
NAACL 2022
Conformal Off-Policy Prediction in Contextual Bandits
NIPS 2022
Grassmann Stein Variational Gradient Descent
AISTATS 2022
BayesIMP: Uncertainty Quantification for Causal Data Fusion
NIPS 2021
Meta Learning for Causal Direction
AAAI 2021
Towards a Unified Analysis of Random Fourier Features
JMLR 2021
Robust Pruning at Initialization
ICLR 2021
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings
AISTATS 2021
MetaFun: Meta-Learning with Iterative Functional Updates
ICML 2020
Automated Model Selection with Bayesian Quadrature
ICML 2019
Towards a Unified Analysis of Random Fourier Features
ICML 2019