Stéphan Clémençon
40 papers · 2007–2026 · 9 conferences · across top CS/AI conferences
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Conferences
AISTATS (12)
ICML (9)
NIPS (7)
JMLR (4)
ACML (3)
ALT (2)
AAAI (1)
EMNLP (1)
ICLR (1)
Top co-authors
Research topics
Keywords
empirical risk minimization
(7)
anomaly detection
(6)
bipartite ranking
(6)
roc curve
(5)
scoring function
(4)
robust statistics
(3)
multivariate analysis
(3)
ranking aggregation
(3)
metric learning
(3)
consensus ranking
(3)
learning rate
(3)
extreme value theory
(3)
preference learning
(3)
binary classification
(2)
risk estimation
(2)
stochastic optimization
(2)
unsupervised learning
(2)
dimension reduction
(2)
density estimation
(2)
active learning
(2)
Papers
Best Arm Identification with Biased Contexts
AAAI 2026
Active Bipartite Ranking with Smooth Posterior Distributions
AISTATS 2025
On Ranking-based Tests of Independence
AISTATS 2024
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks
EMNLP 2024
Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition
ICLR 2024
Active Bipartite Ranking
NIPS 2023
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
ICML 2023
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
ICML 2022
Empirical Risk Minimization under Random Censorship
JMLR 2022
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
AISTATS 2022
What are the best Systems? New Perspectives on NLP Benchmarking
NIPS 2022
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints
AISTATS 2021
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
ICML 2021
Learning from Biased Data: A Semi-Parametric Approach
ICML 2021
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
AISTATS 2021
Functional Isolation Forest
ACML 2019
Autoencoding any Data through Kernel Autoencoders
AISTATS 2019
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
ALT 2019
On Medians of (Randomized) Pairwise Means
ICML 2019
On Binary Classification in Extreme Regions
NIPS 2018
Profitable Bandits
ACML 2018
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods
AISTATS 2018
Ranking Median Regression: Learning to Order through Local Consensus
ALT 2018
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
ICML 2018
Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere
AISTATS 2017
Ranking Data with Continuous Labels through Oriented Recursive Partitions
NIPS 2017
A Learning Theory of Ranking Aggregation
AISTATS 2017
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability
NIPS 2016
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
ICML 2016
Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers
ACML 2016
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
AISTATS 2016
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
JMLR 2016
Extending Gossip Algorithms to Distributed Estimation of U-statistics
NIPS 2015
On Anomaly Ranking and Excess-Mass Curves
AISTATS 2015
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
NIPS 2015
MRA-based Statistical Learning from Incomplete Rankings
ICML 2015
Anomaly Ranking as Supervised Bipartite Ranking
ICML 2014
Ranking Forests
JMLR 2013
Scoring anomalies: a M-estimation formulation
AISTATS 2013
Ranking the Best Instances
JMLR 2007