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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
The DAG Visit approach for Pebbling and I/O Lower Bounds
Gianfranco Bilardi, Lorenzo De Stefani · 2022-10-05 · via cs.DS updates on arXiv.org

We introduce the notion of an $r$-visit of a Directed Acyclic Graph DAG $G=(V,E)$, a sequence of the vertices of the DAG complying with a given rule $r$. A rule $r$ specifies for each vertex $v\in V$ a family of $r$-enabling sets of (immediate) predecessors: before visiting $v$, at least one of its enabling sets must have been visited. Special cases are the $r^{(top)}$-rule (or, topological rule), for which the only enabling set is the set of all predecessors and the $r^{(sin)}$-rule (or, singleton rule), for which the enabling sets are the singletons containing exactly one predecessor. The $r$-boundary complexity of a DAG $G$, $b_{r}\left(G\right)$, is the minimum integer $b$ such that there is an $r$-visit where, at each stage, for at most $b$ of the vertices yet to be visited an enabling set has already been visited. By a reformulation of known results, it is shown that the boundary complexity of a DAG $G$ is a lower bound to the pebbling number of the reverse DAG, $G^R$. Several known pebbling lower bounds can be cast in terms of the $r^{(sin)}$-boundary complexity. A visit partition technique for I/O lower bounds, which generalizes the $S$-partition I/O technique introduced by Hong and Kung in their classic paper "I/O complexity: The Red-Blue pebble game". The visit partition approach yields tight I/O bounds for some DAGs for which the $S$-partition technique can only yield an $Ω(1)$ lower bound.