• Publications
  • Influence
Estimating the Support of a High-Dimensional Distribution
TLDR
We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. Expand
  • 4,074
  • 596
  • PDF
Canonical Correlation Analysis: An Overview with Application to Learning Methods
TLDR
We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text based on only their content from a text query. Expand
  • 2,332
  • 435
  • PDF
Large Margin DAGs for Multiclass Classification
TLDR
We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. Expand
  • 1,823
  • 171
  • PDF
On Kernel-Target Alignment
TLDR
We introduce the notion of kernel-alignment, a measure of similarity between two kernel functions or between a kernel and a target function, and show that adapting the kernel to improve alignment on labelled data significantly increases the alignment on the test set. Expand
  • 972
  • 115
  • PDF
Challenges in representation learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning(1) focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodalExpand
  • 452
  • 87
  • PDF
Two view learning: SVM-2K, Theory and Practice
TLDR
Kernel Canonical Correlation Analysis (KCCA) has been shown to be an effective preprocessing step that can improve the performance of classification algorithms such as the Support Vector Machine (SVM). Expand
  • 314
  • 61
  • PDF
Structural Risk Minimization Over Data-Dependent Hierarchies
TLDR
The paper introduces some generalizations of Vapnik's (1982) method of structural risk minimization (SRM). Expand
  • 572
  • 52
  • PDF
Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis
TLDR
The problem of learning a semantic representation of a text document from data is addressed, in the situation where a corpus of unlabeled paired documents is available, each pair being formed by a short English document and its French translation. Expand
  • 268
  • 24
  • PDF
Empirical Risk Minimization under Fairness Constraints
TLDR
We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of a classifier. Expand
  • 125
  • 24
  • PDF
Kernel-Based Learning of Hierarchical Multilabel Classification Models
TLDR
We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. Expand
  • 278
  • 19
  • PDF