From Shapley Values to Generalized Additive Models and back.
|
AISTATS |
2023 |
0 |
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension.
|
NIPS/NeurIPS |
2023 |
0 |
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation.
|
JMLR |
2023 |
0 |
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees.
|
JMLR |
2023 |
0 |
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference.
|
AISTATS |
2022 |
2 |
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity.
|
AISTATS |
2022 |
0 |
Interpolation and Regularization for Causal Learning.
|
NIPS/NeurIPS |
2022 |
0 |
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models.
|
AISTATS |
2021 |
2 |
NetGAN without GAN: From Random Walks to Low-Rank Approximations.
|
ICML |
2020 |
12 |
Too Relaxed to Be Fair.
|
ICML |
2020 |
36 |
Explaining the Explainer: A First Theoretical Analysis of LIME.
|
AISTATS |
2020 |
69 |
Boosting for Comparison-Based Learning.
|
IJCAI |
2019 |
0 |
Foundations of Comparison-Based Hierarchical Clustering.
|
NIPS/NeurIPS |
2019 |
0 |
Measures of distortion for machine learning.
|
NIPS/NeurIPS |
2018 |
14 |
Comparison-Based Random Forests.
|
ICML |
2018 |
22 |
Practical Methods for Graph Two-Sample Testing.
|
NIPS/NeurIPS |
2018 |
26 |
When do random forests fail?
|
NIPS/NeurIPS |
2018 |
48 |
Design and Analysis of the NIPS 2016 Review Process.
|
JMLR |
2018 |
0 |
Two-Sample Tests for Large Random Graphs Using Network Statistics.
|
COLT |
2017 |
33 |
Comparison-Based Nearest Neighbor Search.
|
AISTATS |
2017 |
29 |
Kernel functions based on triplet comparisons.
|
NIPS/NeurIPS |
2017 |
0 |
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis.
|
JMLR |
2017 |
0 |
Dimensionality estimation without distances.
|
AISTATS |
2015 |
22 |
Density-preserving quantization with application to graph downsampling.
|
COLT |
2014 |
12 |
The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation.
|
ICML |
2014 |
8 |
Uniqueness of Ordinal Embedding.
|
COLT |
2014 |
58 |
Local Ordinal Embedding.
|
ICML |
2014 |
71 |
Hitting and commute times in large random neighborhood graphs.
|
JMLR |
2014 |
100 |
Density estimation from unweighted k-nearest neighbor graphs: a roadmap.
|
NIPS/NeurIPS |
2013 |
35 |
Shortest path distance in random k-nearest neighbor graphs.
|
ICML |
2012 |
39 |
Pruning nearest neighbor cluster trees.
|
ICML |
2011 |
51 |
Phase transition in the family of p-resistances.
|
NIPS/NeurIPS |
2011 |
59 |
Risk-Based Generalizations of f-divergences.
|
ICML |
2011 |
9 |
Preface.
|
COLT |
2011 |
0 |
Getting lost in space: Large sample analysis of the resistance distance.
|
NIPS/NeurIPS |
2010 |
69 |
Multi-agent Random Walks for Local Clustering on Graphs.
|
ICDM |
2010 |
58 |
Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions.
|
JMLR |
2009 |
68 |
Relating Clustering Stability to Properties of Cluster Boundaries.
|
COLT |
2008 |
60 |
Influence of graph construction on graph-based clustering measures.
|
NIPS/NeurIPS |
2008 |
169 |
Consistent Minimization of Clustering Objective Functions.
|
NIPS/NeurIPS |
2007 |
16 |
Graph Laplacians and their Convergence on Random Neighborhood Graphs.
|
JMLR |
2007 |
0 |
A Sober Look at Clustering Stability.
|
COLT |
2006 |
234 |
From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians.
|
COLT |
2005 |
339 |
A Compression Approach to Support Vector Model Selection.
|
JMLR |
2004 |
60 |
Limits of Spectral Clustering.
|
NIPS/NeurIPS |
2004 |
123 |
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case.
|
COLT |
2004 |
53 |
Distance-Based Classification with Lipschitz Functions.
|
JMLR |
2004 |
192 |
Distance-Based Classification with Lipschitz Functions.
|
COLT |
2003 |
0 |