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Found 5 papers, 0 papers with code
Replicable Learning of Large-Margin Halfspaces
Departing from the requirement of polynomial time algorithms, using the DP-to-Replicability reduction of Bun, Gaboardi, Hopkins, Impagliazzo, Lei, Pitassi, Sorrell, and Sivakumar [STOC, 2023], we show how to obtain a replicable algorithm for large-margin halfspaces with improved sample complexity with respect to the margin parameter $\tau$, but running time doubly exponential in $1/\tau^2$ and worse sample complexity dependence on $\epsilon$ than one of our previous algorithms.
Statistical Indistinguishability of Learning Algorithms
When two different parties use the same learning rule on their own data, how can we test whether the distributions of the two outcomes are similar?
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
Second, we consider the problem of multiclass classification with structured data (such as data lying on a low dimensional manifold or satisfying margin conditions), a setting which is captured by partial concept classes (Alon, Hanneke, Holzman and Moran, FOCS '21).
Replicable Bandits
Similarly, for stochastic linear bandits (with finitely and infinitely many arms) we develop replicable policies that achieve the best-known problem-independent regret bounds with an optimal dependency on the replicability parameter.
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches
In this paper, we study the problem of regret minimization for episodic Reinforcement Learning (RL) both in the model-free and the model-based setting.
