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Found 8 papers, 0 papers with code
Near-Optimal Two-Pass Streaming Algorithm for Sampling Random Walks over Directed Graphs
In addition, we show a similar $\tilde{\Theta}(n \cdot \sqrt{L})$ bound on the space complexity of any algorithm (with any number of passes) for the related problem of sampling an $L$-step random walk from every vertex in the graph.
Data Structures and Algorithms Computational Complexity
On Distributed Differential Privacy and Counting Distinct Elements
We study the setup where each of $n$ users holds an element from a discrete set, and the goal is to count the number of distinct elements across all users, under the constraint of $(\epsilon, \delta)$-differentially privacy: - In the non-interactive local setting, we prove that the additive error of any protocol is $\Omega(n)$ for any constant $\epsilon$ and for any $\delta$ inverse polynomial in $n$.
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
We provide a novel instance-wise lower bound for the sample complexity of the problem, as well as a nontrivial sampling algorithm, matching the lower bound up to a factor of $\ln|\mathcal{F}|$.
Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection
In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each associated with an unknown reward distribution.
Towards Instance Optimal Bounds for Best Arm Identification
$H(I)=\sum_{i=2}^n\Delta_{[i]}^{-2}$ is the complexity of the instance.
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture
The best arm identification problem (BEST-1-ARM) is the most basic pure exploration problem in stochastic multi-armed bandits.
Pure Exploration of Multi-armed Bandit Under Matroid Constraints
In a Best-Basis instance, we are given $n$ stochastic arms with unknown reward distributions, as well as a matroid $\mathcal{M}$ over the arms.
On the Optimal Sample Complexity for Best Arm Identification
The $i$th arm has a reward distribution $D_i$ with an unknown mean $\mu_{i}$.
