approximation

Algorithms and Hardness for Active Learning on Graphs ICML 2025 An uncertainty principle for Linear Recurrent Neural Networks COLT 2025 Attention-Level Speculation ICML 2025 COKE: Core Kernel for More Efficient Approximation of Kernel Weights in Multiple Kernel Clustering ICML 2025 Depth Separations in Neural Networks: Separating the Dimension from the Accuracy COLT 2025 Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model ICML 2025 Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis ICML 2025 Faster Approximation Algorithms for k-Center via Data Reduction ICML 2025 Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator ICML 2025 Fully Dynamic Euclidean Bi-Chromatic Matching in Sublinear Update Time ICML 2025 Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency ICLR 2025 On Fine-Grained Distinct Element Estimation ICML 2025 Position: Political Neutrality in AI Is Impossible — But Here Is How to Approximate It ICML 2025 Relative Error Fair Clustering in the Weak-Strong Oracle Model ICML 2025 Simple Randomized Rounding for Max-Min Eigenvalue Augmentation ICML 2025 Softmax is not Enough (for Sharp Size Generalisation) ICML 2025 Consistent Submodular Maximization ICML 2024 Dynamic Correlation Clustering in Sublinear Update Time ICML 2024 Dynamic Facility Location in High Dimensional Euclidean Spaces ICML 2024 Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs ICML 2024 Near-Linear Time Approximation Algorithms for k-means with Outliers ICML 2024 Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues ICML 2024