Victor-Emmanuel Brunel
14 papers · 2017–2025 · 8 venues · across top CS/AI venues
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Machine Learning
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Bayesian & Probabilistic Β· Machine Learning
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Artificial Intelligence
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Mathematics & Optimization
(4)
Mathematics
(4)
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determinantal point process
(4)
concentration inequality
(3)
off-policy learning
(3)
method of moment
(2)
sample complexity
(2)
subset selection
(2)
metric space
(2)
information retrieval
(1)
scalable learning
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statistical inference
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importance sampling
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asymptotic variance
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kernel matrix
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maximum likelihood
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dimension reduction
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matrix factorization
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frechet mean
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maximum likelihood estimation
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Papers
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
AISTATS 2025
Concentration of empirical barycenters in metric spaces
ALT 2024
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
UAI 2024
Exponential Smoothing for Off-Policy Learning
ICML 2023
Geodesically convex M-estimation in metric spaces
COLT 2023
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
ICLR 2021
Statistical guarantees for generative models without domination
ALT 2021
A nonasymptotic law of iterated logarithm for general M-estimators
AISTATS 2020
Best Arm Identification for Contaminated Bandits
JMLR 2019
Learning Nonsymmetric Determinantal Point Processes
NeurIPS 2019
Learning rates for Gaussian mixtures under group action
COLT 2019
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
NeurIPS 2018
Learning Determinantal Point Processes with Moments and Cycles
ICML 2017
Rates of estimation for determinantal point processes
COLT 2017