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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
Optimal Document Exchange and New Codes for Insertions an...
Bernhard Haeupler · 2018-04-10 · via cs.DS updates on arXiv.org

We give the first communication-optimal document exchange protocol. For any $n$ and $k < n$ our randomized scheme takes any $n$-bit file $F$ and computes a $Θ(k \log \frac{n}{k})$-bit summary from which one can reconstruct $F$, with high probability, given a related file $F'$ with edit distance $ED(F,F') \leq k$. The size of our summary is information-theoretically order optimal for all values of $k$, giving a randomized solution to a longstanding open question of [Orlitsky; FOCS'91]. It also is the first non-trivial solution for the interesting setting where a small constant fraction of symbols have been edited, producing an optimal summary of size $O(H(δ)n)$ for $k=δn$. This concludes a long series of better-and-better protocols which produce larger summaries for sub-linear values of $k$ and sub-polynomial failure probabilities. In particular, the recent break-through of [Belazzougui, Zhang; FOCS'16] assumes that $k < n^ε$, produces a summary of size $O(k\log^2 k + k\log n)$, and succeeds with probability $1-(k \log n)^{-O(1)}$. We also give an efficient derandomized document exchange protocol with summary size $O(k \log^2 \frac{n}{k})$. This improves, for any $k$, over a deterministic document exchange protocol by Belazzougui with summary size $O(k^2 + k \log^2 n)$. Our deterministic document exchange directly provides new efficient systematic error correcting codes for insertions and deletions. These (binary) codes correct any $δ$ fraction of adversarial insertions/deletions while having a rate of $1 - O(δ\log^2 \frac{1}δ)$ and improve over the codes of Guruswami and Li and Haeupler, Shahrasbi and Vitercik which have rate $1 - Θ\left(\sqrtδ \log^{O(1)} \frac{1}ε\right)$.