Inductive Learning
Learning latent variable structured prediction models with Gaussian perturbations
We extend this work by including latent variables. We study a new family of loss functions under Gaussian perturbations and analyze the effect of the latent space on the generalization bounds.
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Bruno Korbar, Du Tran, Lorenzo Torresani
There is a natural correlation between the visual and auditive elements of a video.
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Semi-supervised learning has been reported to deliver encouraging results in various settings, e.g. for object detection in computer vision (Rasmus et al., 2015), protein function prediction from sequence data (Weston et al., 2005) or prediction of cancer recurrence (Shi & Zhang, 2011) in the
A Structured Prediction Approach for Label Ranking
Anna Korba, Alexandre Garcia, Florence d'Alché-Buc
Neural Information Processing Systems http://nips.cc/
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
The labeled data is used to learn this assignment.
A Smoother Way to Train Structured Prediction Models
Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaid Harchaoui
Neural Information Processing Systems http://nips.cc/
Toddler-Inspired Visual Object Learning
Sven Bambach, David Crandall, Linda Smith, Chen Yu
The visual properties of images from the first-person and third-person views are very different [34].
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
Haitian Sun, William W. Cohen, Lidong Bing
Neural Information Processing Systems http://nips.cc/
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation
Harikrishna Narasimhan, Shivani Agarwal
Neural Information Processing Systems http://nips.cc/
Effective Targeted Attacks for Adversarial Self-Supervised Learning Minseon Kim
To achieve robustness in these frameworks, Kim et al.