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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
Technical Report: CSVM Ecosystem
2012-09-11 · via cs.DS updates on arXiv.org

The CSVM format is derived from CSV format and allows the storage of tabular like data with a limited but extensible amount of metadata. This approach could help computer scientists because all information needed to uses subsequently the data is included in the CSVM file and is particularly well suited for handling RAW data in a lot of scientific fields and to be used as a canonical format. The use of CSVM has shown that it greatly facilitates: the data management independently of using databases; the data exchange; the integration of RAW data in dataflows or calculation pipes; the search for best practices in RAW data management. The efficiency of this format is closely related to its plasticity: a generic frame is given for all kind of data and the CSVM parsers don't make any interpretation of data types. This task is done by the application layer, so it is possible to use same format and same parser codes for a lot of purposes. In this document some implementation of CSVM format for ten years and in different laboratories are presented. Some programming examples are also shown: a Python toolkit for using the format, manipulating and querying is available. A first specification of this format (CSVM-1) is now defined, as well as some derivatives such as CSVM dictionaries used for data interchange. CSVM is an Open Format and could be used as a support for Open Data and long term conservation of RAW or unpublished data.