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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
Potent Tree Codes and their applications: Coding for Inte...
Ran Gelles, Amit Sahai · 2011-04-05 · via cs.DS updates on arXiv.org

We study the fundamental problem of reliable interactive communication over a noisy channel. In a breakthrough sequence of papers published in 1992 and 1993, Schulman gave non-constructive proofs of the existence of general methods to emulate any two-party interactive protocol such that: (1) the emulation protocol takes a constant-factor longer than the original protocol, and (2) if the emulation protocol is executed over a noisy channel, then the probability that the emulation protocol fails is exponentially small in the total length of the protocol. Unfortunately, Schulman's emulation procedures either only work in a model with a large amount of shared randomness, or are non-constructive in that they rely on the existence of good tree codes. The only known proofs of the existence of good tree codes are non-constructive, and finding an explicit construction remains an important open problem. Indeed, randomly generated tree codes are not good tree codes with overwhelming probability. In this work, we revisit the problem of reliable interactive communication, and obtain the following results: We introduce a new notion of goodness for a tree code, and define the notion of a potent tree code. We believe that this notion is of independent interest. We prove the correctness of an explicit emulation procedure based on any potent tree code. We show that a randomly generated tree code (with suitable constant alphabet size) is a potent tree code with overwhelming probability. Furthermore we are able to partially derandomize this result using only O(n) random bits, where $n$ is the depth of the tree. These results allow us to obtain the first fully explicit emulation procedure for reliable interactive communication over noisy channels with a constant communication overhead, and exponentially small failure probability.