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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
Using higher-order Fourier analysis over general fields
Arnab Bhattacharyya, Abhishek Bhowmick · 2015-05-04 · via cs.DS updates on arXiv.org

Higher-order Fourier analysis, developed over prime fields, has been recently used in different areas of computer science, including list decoding, algorithmic decomposition and testing. We extend the tools of higher-order Fourier analysis to analyze functions over general fields. Using these new tools, we revisit the results in the above areas. * For any fixed finite field $\mathbb{K}$, we show that the list decoding radius of the generalized Reed Muller code over $\mathbb{K}$ equals the minimum distance of the code. Previously, this had been proved over prime fields [BL14] and for the case when $|\mathbb{K}|-1$ divides the order of the code [GKZ08]. * For any fixed finite field $\mathbb{K}$, we give a polynomial time algorithm to decide whether a given polynomial $P: \mathbb{K}^n \to \mathbb{K}$ can be decomposed as a particular composition of lesser degree polynomials. This had been previously established over prime fields [Bha14, BHT15]. * For any fixed finite field $\mathbb{K}$, we prove that all locally characterized affine-invariant properties of functions $f: \mathbb{K}^n \to \mathbb{K}$ are testable with one-sided error. The same result was known when $\mathbb{K}$ is prime [BFHHL13] and when the property is linear [KS08]. Moreover, we show that for any fixed finite field $\mathbb{F}$, an affine-invariant property of functions $f: \mathbb{K}^n \to \mathbb{F}$, where $\mathbb{K}$ is a growing field extension over $\mathbb{F}$, is testable if it is locally characterized by constraints of bounded weight.