Search Results for author:
Found 6 papers, 3 papers with code
Learning with Relaxed Supervision
For weakly-supervised problems with deterministic constraints between the latent variables and observed output, learning necessitates performing inference over latent variables conditioned on the output, which can be intractable no matter how simple the model family is.
Calibrated Structured Prediction
In user-facing applications, displaying calibrated confidence measures---probabilities that correspond to true frequency---can be as important as obtaining high accuracy.
Simple MAP Inference via Low-Rank Relaxations
We focus on the problem of maximum a posteriori (MAP) inference in Markov random fields with binary variables and pairwise interactions.
Identifiability and Unmixing of Latent Parse Trees
This paper explores unsupervised learning of parsing models along two directions.
Asymptotically Optimal Regularization in Smooth Parametric Models
Many types of regularization schemes have been employed in statistical learning, each one motivated by some assumption about the problem domain.
A Probabilistic Approach to Language Change
We present a probabilistic approach to language change in which word forms are represented by phoneme sequences that undergo stochastic edits along the branches of a phylogenetic tree.
