Mario Marchand
20 papers · 2002–2024 · 5 conferences · across top CS/AI conferences
Achievements
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🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🗺️ Taxonomy Completionist (10) 🌍 Conference Polyglot (5)
🏃
Academic Marathon
(22)
🧭
Keyword Pioneer
🌟
Keyword Trendsetter Combo
(5)
🤝
Dynamic Duo
(10)
🏆
Keyword Champion
🔬
Deep Specialist
(10)
🔥
Unstoppable
(5)
📈
Trend Setter
🚀
Conference Pioneer
🗃️
Keyword Collector
(91)
💎
Century Club
(20)
Conferences
NIPS (7)
JMLR (5)
AISTATS (4)
ICML (3)
ICLR (1)
Top co-authors
Keywords
pac-bayes bound
(4)
risk bound
(4)
ensemble learning
(4)
model selection
(3)
generalization error
(3)
support vector machine
(2)
set covering machine
(2)
sample compression
(2)
learning theory
(2)
local explanation
(2)
generalization bound
(2)
classifier ensemble
(2)
gibbs classifier
(2)
bayesian inference
(2)
structured output prediction
(2)
decision tree
(2)
convex optimization
(1)
catastrophic forgetting
(1)
classification
(1)
continual learning
(1)
Papers
Tackling the XAI Disagreement Problem with Regional Explanations
AISTATS 2024
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
JMLR 2023
On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm
NIPS 2023
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation
AISTATS 2023
Fooling SHAP with Stealthily Biased Sampling
ICLR 2023
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
NIPS 2021
Decision trees as partitioning machines to characterize their generalization properties
NIPS 2020
A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees
AISTATS 2016
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction
ICML 2015
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
NIPS 2014
Agnostic Bayesian Learning of Ensembles
ICML 2014
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction
ICML 2013
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets
AISTATS 2012
From PAC-Bayes Bounds to KL Regularization
NIPS 2009
Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data
JMLR 2007
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers
JMLR 2007
A PAC-Bayes Risk Bound for General Loss Functions
NIPS 2006
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier
NIPS 2006
Learning with Decision Lists of Data-Dependent Features
JMLR 2005
The Set Covering Machine
JMLR 2002