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Smoothed analysis
The Smoothed Possibility of Social Choice
Smoothed Complexity Theory And, Thus, to Embed Smoothed Analysis Into Compu- Tational Complexity
Smoothed Analysis of the 2-Opt Heuristic for the TSP: Polynomial Bounds for Gaussian Noise∗
Smoothed Analysis of the TSP Algorithms
Smoothed Analysis of Local Search Algorithms
Smoothed Analysis of Algorithms, We Measure the Expected Performance of Algorithms Under Slight Random Perturbations of Worst-Case Inputs
Exploring Parameter Spaces in Coping with Computational Intractability
Approximation Algorithms Under the Worst-Case Analysis and the Smoothed Analysis
Beyond Worst-Case Analysis Lecture #1: Three Motivating Examples∗
Professor Shang-Hua Teng
1 Introduction
Patterns, Predictions, and Actions
Beyond Worst-Case Analysis
The Smoothed Complexity of Computing Kemeny and Slater Rankings
Smoothed Analysis with Applications in Machine Learning Contents
Smoothed Analysis of Online and Differentially Private Learning
Smoothed Analysis: Analysis of Algorithms Beyond Worst Case
Smoothed Analysis of Linear Programming
Top View
Smoothed Analysis of Binary Search Trees and Quicksort Under Additive Noise
Smoothed Analysis: an Attempt to Explain the Behavior of Algorithms in Practice∗
Smoothed Analysis of Algorithms: Why the Simplex Algorithm Usually Takes Polynomial Time
Smoothed Analysis of Local Search for the Maximum-Cut Problem∗
K-Means Requires Exponentially Many Iterations Even in the Plane
A Friendly Smoothed Analysis of the Simplex Method∗
Smoothed Analysis of Termination of Linear Programming Algorithms