PAC-Bayesian Collective Stability

By Ben London, Bert Huang, B. Taskar, Lise Getoor. 2014

21 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ben London
2. Pascal Germain
3. François Laviolette
4. Bert Huang
5. Lingxiao Huang
6. Lise Getoor
7. Alexandre Lacasse
8. Nisheeth K. Vishnoi

Citations in the corpus

• Stable and Fair Classification (2019, background): “, feature selection [60] and structured prediction [51, 52, 50].”
• The Benefits of Learning with Strongly Convex Approximate Inference (2015, background): “The other related line of work is from London et al. (2013; 2014), who derived PAC learning bounds for structured prediction.”
• Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm (2015, methodology): “…Density Estimation (Seldin and Tishby, 2009; Higgs and Shawe-Taylor, 2010), Structured output Prediction (McAllester, 2007; Giguère et al., 2013; London et al., 2014), Co-clustering (Seldin and Tishby, 2009, 2010), Martingales (Seldin et al., 2012), U-Statistics of higher order (Lever et al.,…”
• Stability and Generalization in Structured Prediction (2016, methodology): “In prior work (London et al., 2013, 2014), we used the term collective stability to describe the stability of the predictions, guaranteeing collective stability for predictors whose inference objectives are strongly convex.”