Search Results for author:
Found 12 papers, 0 papers with code
Applications of Littlestone dimension to query learning and to compression
In this paper we give several applications of Littlestone dimension.
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
Reinforcement learning generalizes multi-armed bandit problems with additional difficulties of a longer planning horizon and unknown transition kernel.
A Unified Analysis of Dynamic Interactive Learning
In this paper we investigate the problem of learning evolving concepts over a combinatorial structure.
Communication-Aware Collaborative Learning
In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity.
On the Complexity of Learning from Label Proportions
In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given.
Statistical Queries and Statistical Algorithms: Foundations and Applications
We give a survey of the foundations of statistical queries and their many applications to other areas.
On Biased Random Walks, Corrupted Intervals, and Learning Under Adversarial Design
We tackle some fundamental problems in probability theory on corrupted random processes on the integer line.
On Learning a Hidden Directed Graph with Path Queries
In this paper, we consider the problem of reconstructing a directed graph using path queries.
Crowdsourced PAC Learning under Classification Noise
In this paper, we analyze PAC learnability from labels produced by crowdsourcing.
Sampling Without Compromising Accuracy in Adaptive Data Analysis
In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms.
Data Stability in Clustering: A Closer Look
Awasthi et al. (2010) consider center-based objectives, and Balcan and Liang (2011) analyze the $k$-median and min-sum objectives, giving efficient algorithms for instances resilient to certain constant multiplicative perturbations.
Non-Stochastic Bandit Slate Problems
We consider bandit problems, motivated by applications in online advertising and news story selection, in which the learner must repeatedly select a slate, that is, a subset of size s from K possible actions, and then receives rewards for just the selected actions.
