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Found 9 papers, 1 papers with code
Projection-free Adaptive Regret with Membership Oracles
The most common approach is based on the Frank-Wolfe method, that uses linear optimization computation in lieu of projections.
A Characterization of Multiclass Learnability
This work resolves this problem: we characterize multiclass PAC learnability through the DS dimension, a combinatorial dimension defined by Daniely and Shalev-Shwartz (2014).
Multiclass Boosting and the Cost of Weak Learning
Here, we focus on an especially natural formulation in which the weak hypotheses are assumed to belong to an ''easy-to-learn'' base class, and the weak learner is an agnostic PAC learner for that class with respect to the standard classification loss.
A Boosting Approach to Reinforcement Learning
Reducing reinforcement learning to supervised learning is a well-studied and effective approach that leverages the benefits of compact function approximation to deal with large-scale Markov decision processes.
Online Boosting with Bandit Feedback
We consider the problem of online boosting for regression tasks, when only limited information is available to the learner.
Online Agnostic Boosting via Regret Minimization
Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules.
Boosting for Control of Dynamical Systems
We study the question of how to aggregate controllers for dynamical systems in order to improve their performance.
Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control.
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Here we focus on capturing cardinality constraints in such models.
