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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
Conditional Lower Bound for Subgraph Isomorphism with a T...
Robert Krauthgamer, Ohad Trabelsi · 2017-08-25 · via cs.DS updates on arXiv.org

The kTree problem is a special case of Subgraph Isomorphism where the pattern graph is a tree, that is, the input is an $n$-node graph $G$ and a $k$-node tree $T$, and the goal is to determine whether $G$ has a subgraph isomorphic to $T$. We provide evidence that this problem cannot be computed significantly faster than $2^{k} \textsf{poly}(n)$, which matches the fastest algorithm known for this problem by Koutis and Williams [ICALP 2009 and TALG 2016]. Specifically, we show that if kTree can be solved in time $(2-\varepsilon)^k \textsf{poly}(n)$ for some constant $\varepsilon>0$, then Set Cover with $n'$ elements and $m'$ sets can be solved in time $(2-δ)^{n'} \textsf{poly}(m')$ for a constant $δ(\varepsilon) > 0$, which would refute the Set Cover Conjecture by Cygan et al. [CCC 2012 and TALG 2016]. Our techniques yield a new algorithm for the p-Partial Cover problem, a parameterized version of Set Cover that requires covering at least $p$ elements (rather than all elements). Its running time is $(2+\varepsilon)^p (m')^{O(1/\varepsilon)}$ for any fixed $\varepsilon>0$, which improves the previous $2.597^p \textsf{poly}(m')$-time algorithm by Zehavi [ESA 2015]. Our running time is nearly optimal, as a $(2-\varepsilon')^p \textsf{poly}(m')$-time algorithm would refute the Set Cover Conjecture.