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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Probabilistic methods of bypassing the maze using stones ...
E. G. Kondakova, A. Ya. Kanel-Belov · 2019-11-24 · via cs.DS updates on arXiv.org

In this paper, some open questions that are posed in Ajans' dissertation continue to be addressed: a robot bypass with a generator of random bits of integer spaces in the presence of a stone and a subspace of flags. This work is devoted to bypassing the maze with a finite state machine with a random bit generator. This task is part of the rapidly evolving theme of bypassing the maze by various finite state machines. or their teams, which is closely related to problems from the theory of computational complexity and probability theory. In this paper, it is shown at what dimensions a robot with a random bit generator and a stone can bypass integer space with a subspace of flags. In this paper, we will study the behavior of a finite state machine with a random bit generator on integer spaces. In particular, it was proved that the robot bypasses $ \ zs ^ 2 $ and cannot bypass $ \ zs ^ 3 $; a robot with a stone bypasses $ \ zs ^ 4 $ and cannot bypass $ \ zs ^ 5 $; a robot with a stone and a flag bypasses $ \ zs ^ 6 $ and cannot bypass $ \ zs ^ 7 $; a robot with a stone and a plane of flags bypasses $ \ zs ^ 8 $ and cannot bypass $ \ zs ^ 9 $.