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cs.DS updates on arXiv.org

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
On Coloring Random Subgraphs of a Fixed Graph
Igor Shinkar · 2016-12-14 · via cs.DS updates on arXiv.org

Given an arbitrary graph $G$ we study the chromatic number of a random subgraph $G_{1/2}$ obtained from $G$ by removing each edge independently with probability $1/2$. Studying $χ(G_{1/2})$ has been suggested by Bukh~\cite{Bukh}, who asked whether $\mathbb{E}[χ(G_{1/2})] \geq Ω( χ(G)/\log(χ(G)))$ holds for all graphs $G$. In this paper we show that for any graph $G$ with chromatic number $k = χ(G)$ and for all $d \leq k^{1/3}$ it holds that $\Pr[χ(G_{1/2}) \leq d] < \exp \left(- Ω\left(\frac{k(k-d^3)}{d^3}\right)\right)$. In particular, $\Pr[G_{1/2} \text{ is bipartite}] < \exp \left(- Ω\left(k^2 \right)\right)$. The later bound is tight up to a constant in $Ω(\cdot)$, and is attained when $G$ is the complete graph on $k$ vertices. As a technical lemma, that may be of independent interest, we prove that if in \emph{any} $d^3$ coloring of the vertices of $G$ there are at least $t$ monochromatic edges, then $\Pr[χ(G_{1/2}) \leq d] < e^{- Ω\left(t\right)}$. We also prove that for any graph $G$ with chromatic number $k = χ(G)$ and independence number $α(G) \leq O(n/k)$ it holds that $\mathbb{E}[χ(G_{1/2})] \geq Ω\left( k/\log(k) \right)$. This gives a positive answer to the question of Bukh for a large family of graphs.