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Found 10 papers, 2 papers with code
Predicting and Explaining French Grammatical Gender
Grammatical gender may be determined by semantics, orthography, phonology, or could even be arbitrary.
Improved pronunciation prediction accuracy using morphology
Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems.
Efficiently Learning the Graph for Semi-supervised Learning
We further show how to approximately learn the best graphs from the sparse families efficiently using the conjugate gradient method.
Provably tuning the ElasticNet across instances
We consider the problem of tuning the regularization parameters of Ridge regression, LASSO, and the ElasticNet across multiple problem instances, a setting that encompasses both cross-validation and multi-task hyperparameter optimization.
Output-sensitive ERM-based techniques for data-driven algorithm design
Data-driven algorithm design is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters.
Robustly-reliable learners under poisoning attacks
Remarkably, we provide a complete characterization of learnability in this setting, in particular, nearly-tight matching upper and lower bounds on the region that can be certified, as well as efficient algorithms for computing this region given an ERM oracle.
Learning-to-learn non-convex piecewise-Lipschitz functions
We analyze the meta-learning of the initialization and step-size of learning algorithms for piecewise-Lipschitz functions, a non-convex setting with applications to both machine learning and algorithms.
Data driven semi-supervised learning
Over the past decades, several elegant graph-based semi-supervised learning algorithms for how to infer the labels of the unlabeled examples given the graph and a few labeled examples have been proposed.
An Analysis of Robustness of Non-Lipschitz Networks
Despite significant advances, deep networks remain highly susceptible to adversarial attack.
Learning piecewise Lipschitz functions in changing environments
We consider the class of piecewise Lipschitz functions, which is the most general online setting considered in the literature for the problem, and arises naturally in various combinatorial algorithm selection problems where utility functions can have sharp discontinuities.
