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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
Predictable universally unique identification of sequenti...
2021-09-13 · via cs.DS updates on arXiv.org

Universal identifiers and hashing have been widely adopted in computer science from distributed financial transactions to data science. This is a consequence of their capability to avoid many shortcomings of relative identifiers, such as limited scope and the need for central management. However, the current identifiers in use are isolated entities which cannot provide much information about the relationship between objects. As an example, if one has both the identifiers of an object and its transformed version, no information about how they are related can be obtained without resorting to some kind of authority or additionally appended information. Moreover, given an input object and an arbitrarily long sequence of costly steps, one cannot currently predict the identifier of the outcome without actually processing the entire sequence. The capability of predicting the resulting identifier and avoiding redundant calculations is highly desirable in an efficient unmanaged system. In this paper, we propose a new kind of unique identifier that is calculated from the list of events that can produce an object, instead of directly identifying it by content. This provides an identification scheme regardless of the object's current existence, thus allowing to inexpensively check for its content in a database and retrieve it when it has already been calculated before. These identifiers are proposed in the context of abstract algebra, where objects are represented by elements that can be operated according to useful properties, such as associativity, order sensitivity when desired, and reversibility, while simplicity of implementation and strong guarantees are given by well known group theory results.