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Found 5 papers, 0 papers with code
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
We demonstrate that the optimal mistake bound under bandit feedback is at most $O(k)$ times higher than the optimal mistake bound in the full information case, where $k$ represents the number of labels.
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
We prove an analogous result for randomized learners: we show that the optimal expected mistake bound in learning a class $\mathcal{H}$ equals its randomized Littlestone dimension, which is the largest $d$ for which there exists a tree shattered by $\mathcal{H}$ whose average depth is $2d$.
A Resilient Distributed Boosting Algorithm
Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize.
Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Bounds
In this work we revisit the complexity analysis of CBS to provide tighter bounds on the algorithm's run-time in the worst-case.
Twenty (simple) questions
An optimal strategy for the "20 questions" game is given by a Huffman code for $\pi$: Bob's questions reveal the codeword for $x$ bit by bit.
