Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance.
Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance.
Forward Preface 1. Introduction Tim Roughgarden Part I. Refinements of Worst-Case Analysis: 2. Parameterized algorithms Fedor Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi 3. From adaptive analysis to instance optimality Jérémy Barbay 4. Resource augmentation Tim Roughgarden Part II. Deterministic Models of Data: 5. Perturbation resilience Konstantin Makarychev and Yury Makarychev 6. Approximation stability and proxy objectives Avrim Blum 7. Sparse recovery Eric Price Part III. Semi-Random Models: 8. Distributional analysis Tim Roughgarden 9. Introduction to semi-random models Uriel Feige 10. Semi-random stochastic block models Ankur Moitra 11. Random-order models Anupam Gupta and Sahil Singla 12. Self-improving algorithms C. Seshadhri Part IV. Smoothed Analysis: 13. Smoothed analysis of local search Bodo Manthey 14. Smoothed analysis of the simplex method Daniel Dadush and Sophie Huiberts 15. Smoothed analysis of Pareto curves in multiobjective optimization Heiko Röglin Part V. Applications in Machine Learning and Statistics: 16. Noise in classification Maria-Florina Balcan and Nika Haghtalab 17. Robust high-dimensional statistics Ilias Diakonikolas and Daniel Kane 18. Nearest-neighbor classification and search Sanjoy Dasgupta and Samory Kpotufe 19. Efficient tensor decomposition Aravindan Vijayaraghavan 20. Topic models and nonnegative matrix factorization Rong Ge and Ankur Moitra 21. Why do local methods solve nonconvex problems? Tengyu Ma 22. Generalization in overparameterized models Moritz Hardt 23. Instance-optimal distribution testing and learning Gregory Valiant and Paul Valiant Part VI. Further Applications: 24. Beyond competitive analysis Anna R. Karlin and Elias Koutsoupias 25. On the unreasonable effectiveness of satisfiability solvers Vijay Ganesh and Moshe Vardi 26. When simple hash functions suffice Kai-Min Chung, Michael Mitzenmacher and Salil Vadhan 27. Prior-independent auctions Inbal Talgam-Cohen 28. Distribution-free models of social networks Tim Roughgarden and C. Seshadhri 29. Data-driven algorithm design Maria-Florina Balcan 30. Algorithms with predictions Michael Mitzenmacher and Sergei Vassilvitskii.
Forward Preface 1. Introduction Tim Roughgarden Part I. Refinements of Worst-Case Analysis: 2. Parameterized algorithms Fedor Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi 3. From adaptive analysis to instance optimality Jérémy Barbay 4. Resource augmentation Tim Roughgarden Part II. Deterministic Models of Data: 5. Perturbation resilience Konstantin Makarychev and Yury Makarychev 6. Approximation stability and proxy objectives Avrim Blum 7. Sparse recovery Eric Price Part III. Semi-Random Models: 8. Distributional analysis Tim Roughgarden 9. Introduction to semi-random models Uriel Feige 10. Semi-random stochastic block models Ankur Moitra 11. Random-order models Anupam Gupta and Sahil Singla 12. Self-improving algorithms C. Seshadhri Part IV. Smoothed Analysis: 13. Smoothed analysis of local search Bodo Manthey 14. Smoothed analysis of the simplex method Daniel Dadush and Sophie Huiberts 15. Smoothed analysis of Pareto curves in multiobjective optimization Heiko Röglin Part V. Applications in Machine Learning and Statistics: 16. Noise in classification Maria-Florina Balcan and Nika Haghtalab 17. Robust high-dimensional statistics Ilias Diakonikolas and Daniel Kane 18. Nearest-neighbor classification and search Sanjoy Dasgupta and Samory Kpotufe 19. Efficient tensor decomposition Aravindan Vijayaraghavan 20. Topic models and nonnegative matrix factorization Rong Ge and Ankur Moitra 21. Why do local methods solve nonconvex problems? Tengyu Ma 22. Generalization in overparameterized models Moritz Hardt 23. Instance-optimal distribution testing and learning Gregory Valiant and Paul Valiant Part VI. Further Applications: 24. Beyond competitive analysis Anna R. Karlin and Elias Koutsoupias 25. On the unreasonable effectiveness of satisfiability solvers Vijay Ganesh and Moshe Vardi 26. When simple hash functions suffice Kai-Min Chung, Michael Mitzenmacher and Salil Vadhan 27. Prior-independent auctions Inbal Talgam-Cohen 28. Distribution-free models of social networks Tim Roughgarden and C. Seshadhri 29. Data-driven algorithm design Maria-Florina Balcan 30. Algorithms with predictions Michael Mitzenmacher and Sergei Vassilvitskii.
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