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6 papers · 2 filters
Robustness of Conditional GANs to Noisy Labels
Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin +1
We study the problem of learning conditional generators from noisy labeled samples, where the labels are corrupted by random noise. A standard training of conditional GANs will not…
Hierarchical Methods of Moments
Matteo Ruffini, Guillaume Rabusseau, Borja Balle
Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal, the applicability of these methods to r…
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. Machine learning (ML) systems, however, which depend strongly on prope…
Linear Bandits with Stochastic Delayed Feedback
Claire Vernade, Alexandra Carpentier, Tor Lattimore +3
Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and rec…
A Stochastic Decoder for Neural Machine Translation
Philip Schulz, Wilker Aziz, Trevor Cohn
The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence. This gives rise to significant variation in parallel cor- pora,…
Transformation Autoregressive Networks
Junier B. Oliva, Avinava Dubey, Manzil Zaheer +4
The fundamental task of general density estimation has been of keen interest to machine learning. In this work, we attempt to systematically characterize methods for density…