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The ACM journal of experimental algorithmics
| The ACM journal of experimental algorithmics |
| Pubbl/distr/stampa | New York, : ACM |
| Disciplina | 004 |
| Soggetto topico |
Computer algorithms
Data structures (Computer science) Algorithms Algorismes Algorismes computacionals Estructures de dades (Informàtica) |
| Soggetto genere / forma |
Periodicals.
Revistes electròniques. |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Journal of experimental algorithmics
JEA ACM JEA Association for Computing Machinery journal of experimental algorithmics |
| Record Nr. | UNINA-9910376057303321 |
| New York, : ACM | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Bias and Fairness in Information Retrieval : 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers / / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
| Advances in Bias and Fairness in Information Retrieval : 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers / / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo |
| Autore | Boratto Ludovico |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (187 pages) |
| Disciplina | 025.524 |
| Altri autori (Persone) |
FaralliStefano
MarrasMirko StiloGiovanni |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Computer engineering
Computer networks Artificial intelligence Electronic commerce Computer Engineering and Networks Artificial Intelligence e-Commerce and e-Business Aprenentatge automàtic Xarxes neuronals (Informàtica) Intel·ligència artificial Algorismes |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031372490
3031372492 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations -- Measuring Bias in Multimodal Models: Multimodal Composite Association Score -- Evaluating Fairness Metrics -- Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems -- Preserving Utility in Fair Top-k Ranking with Intersectional Bias -- Mitigating Position Bias in Hotels Recommender Systems -- Improving Recommender System Diversity with Variational Autoencoders -- Addressing Biases in the Texts using an End-to-End Pipeline Approach -- Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation -- How do you feel? Information Retrieval in Psychotherapy and Fair Ranking Assessment -- Understanding Search Behavior Bias in Wikipedia -- Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations -- Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation -- What are we missing in algorithmic fairness? Discussing open challenges for fairness analysis in user profiling with Graph Neural Networks. |
| Record Nr. | UNINA-9910734876903321 |
Boratto Ludovico
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes
| Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes |
| Autore | Erciyes Kayhan |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (229 pages) |
| Disciplina | 511.5 |
| Collana | Undergraduate Topics in Computer Science |
| Soggetto topico |
Computer science
Computer science - Mathematics Discrete mathematics Theory of Computation Discrete Mathematics in Computer Science Mathematical Applications in Computer Science Python (Llenguatge de programació) Algorismes Àlgebra |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030878863
9783030878856 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Graphs, Matrices and Matroids -- 3. Parallel Matrix Algorithm Kernel -- 4. Basic Graph Algorithms -- 5. Connectivity, Matching and Matroids -- 6. Subgraph Search -- 7. Analysis of Large Graphs -- 8. Clustering in Complex Networks -- 9. Kronecker Graphs -- 10. Sample Algorithms for Complex Networks. |
| Record Nr. | UNINA-9910510574003321 |
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Erciyes Kayhan
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
| Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos |
| Autore | Souravlias Dimitris |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (xiv, 92 pages) : illustrations |
| Disciplina | 518.1 |
| Collana | SpringerBriefs in Optimization |
| Soggetto topico |
Operations research
Management science Algorithms Microprogramming Discrete mathematics Operations Research, Management Science Control Structures and Microprogramming Discrete Mathematics Algorismes Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-68514-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Metaheuristic optimization algorithms -- 2. Algorithm portfolios -- 3. Selection of constituent algorithms -- 4. Allocation of computation resources -- 5. Sequential and parallel models -- 6. Recent applications -- 7. Epilogue -- References. |
| Record Nr. | UNISA-996466562303316 |
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Souravlias Dimitris
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algorithm Portfolios : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
| Algorithm Portfolios : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos |
| Autore | Souravlias Dimitris |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (xiv, 92 pages) : illustrations |
| Disciplina | 518.1 |
| Collana | SpringerBriefs in Optimization |
| Soggetto topico |
Operations research
Management science Algorithms Microprogramming Discrete mathematics Operations Research, Management Science Control Structures and Microprogramming Discrete Mathematics Algorismes Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-68514-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Metaheuristic optimization algorithms -- 2. Algorithm portfolios -- 3. Selection of constituent algorithms -- 4. Allocation of computation resources -- 5. Sequential and parallel models -- 6. Recent applications -- 7. Epilogue -- References. |
| Record Nr. | UNINA-9910484845003321 |
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Souravlias Dimitris
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithmic Game Theory : 17th International Symposium, SAGT 2024, Amsterdam, The Netherlands, September 3–6, 2024, Proceedings / / edited by Guido Schäfer, Carmine Ventre
| Algorithmic Game Theory : 17th International Symposium, SAGT 2024, Amsterdam, The Netherlands, September 3–6, 2024, Proceedings / / edited by Guido Schäfer, Carmine Ventre |
| Autore | Schäfer Guido |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (549 pages) |
| Disciplina | 003.3 |
| Altri autori (Persone) | VentreCarmine |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer simulation
Data structures (Computer science) Information theory Application software Artificial intelligence Algorithms Computer networks Computer Modelling Data Structures and Information Theory Computer and Information Systems Applications Artificial Intelligence Design and Analysis of Algorithms Computer Communication Networks Algorismes Programari d'aplicació Intel·ligència artificial Xarxes d'ordinadors Simulació per ordinador Estructures de dades (Informàtica) |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-71033-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | -- The Computational Complexity of the Housing Market. -- Ex-post Stability under Two-Sided Matching: Complexity and Characterization. -- Approval-Based Committee Voting under Uncertainty. -- Matching. -- Structural and Algorithmic Results for Stable Cycles and Partitions in the Roommates Problem. -- Online Matching with High Probability. -- The Team Order Problem: Maximizing the Probability of Matching Being Large Enough. -- Fair Division and Resource Allocation. -- Fair Division of Chores with Budget Constraints. -- Fair Division with Interdependent Values. -- Fair Division with Bounded Sharing: Binary and Non-Degenerate Valuations. -- Incentives in Dominant Resource Fair Allocation under Dynamic Demands. -- Mechanism Design. -- Agent-Constrained Truthful Facility Location Games. -- The k-Facility Location Problem Via Optimal Transport: A Bayesian Study of the Percentile Mechanisms. -- Discrete Single-Parameter Optimal Auction Design. -- Estimating the Expected Social Welfare and Cost of Random Serial Dictatorship. -- Game Theory and Repeated Games. -- Swim Till You Sink: Computing the Limit of a Game. -- The Investment Management Game: Extending the Scope of the Notion of Core. -- Edge-Dominance Games on Graphs. -- Playing Repeated Games with Sublinear Randomness. -- Pricing, Revenue, and Regulation. -- Mind the Revenue Gap: On the Performance of Approximation Mechanisms under Budget Constraints. -- Sublogarithmic Approximation for Tollbooth Pricing on a Cactus. -- To Regulate or Not to Regulate: Using Revenue Maximization Tools to Maximize Consumer Utility. -- Balancing Participation and Decentralization in Proof-of-Stake Cryptocurrencies. -- Matroid Theory in Game Theory. -- Price of Anarchy in Paving Matroid Congestion Games. -- Price of Anarchy for Graphic Matroid Congestion Games. -- Non-Adaptive Matroid Prophet Inequalities. -- Matroid Bayesian Online Selection. -- Information Sharing and Decision Making. -- Prediction-Sharing During Training and Inference. -- Calibrated Recommendations for Users with Decaying Attention. -- Matrix Rationalization via Partial Orders. -- Computational Complexity and Resource Allocation. -- k-Times Bin-Packing and its Application to Fair Electricity Distribution. -- Condorcet Markets. -- Complexity of Round-Robin Allocation with Potentially Noisy Queries. |
| Record Nr. | UNINA-9910886077403321 |
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Schäfer Guido
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer
| Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer |
| Autore | Vovk Vladimir <1960-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (490 pages) |
| Disciplina | 518.1 |
| Soggetto topico |
Algorithms
Algorithms - Study and teaching Teoria de la predicció Algorismes Processos estocàstics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-06649-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Notation and Abbreviations -- Sets, Bags, and Sequences -- Stochastics -- Machine Learning -- Programming -- Confidence Prediction -- Other Notations -- Abbreviations -- 1 Introduction -- 1.1 Machine Learning -- 1.1.1 Learning Under Randomness -- 1.1.2 Learning Under Unconstrained Randomness -- 1.2 A Shortcoming of Statistical Learning Theory -- 1.2.1 The Hold-Out Estimate of Confidence -- 1.2.2 The Contribution of This Book -- 1.3 The Online Framework -- 1.3.1 Online Learning -- 1.3.2 Online/Offline Compromises -- 1.3.3 One-Off and Offline Learning -- 1.3.4 Induction, Transduction, and the Online Framework -- 1.4 Conformal Prediction -- 1.4.1 Nested Prediction Sets -- 1.4.2 Validity -- 1.4.3 Efficiency -- 1.4.4 Conditionality -- 1.4.5 Flexibility of Conformal Predictors -- 1.5 Probabilistic Prediction Under Unconstrained Randomness -- 1.5.1 Universally Consistent Probabilistic Predictor -- 1.5.2 Probabilistic Prediction Using a Finite Dataset -- 1.5.3 Venn Prediction -- 1.5.4 Conformal Predictive Distributions -- 1.6 Beyond Randomness -- 1.6.1 Testing Randomness -- 1.6.2 Online Compression Models -- 1.7 Context -- Part I Set Prediction -- 2 Conformal Prediction: General Case and Regression -- 2.1 Confidence Predictors -- 2.1.1 Assumptions -- 2.1.2 Simple Predictors and Confidence Predictors -- 2.1.3 Validity -- 2.1.4 Randomized Confidence Predictors -- 2.1.5 Confidence Predictors Over a Finite Horizon -- 2.1.6 One-Off and Offline Confidence Predictors -- 2.2 Conformal Predictors -- 2.2.1 Bags -- 2.2.2 Nonconformity and Conformity -- 2.2.3 p-Values -- 2.2.4 Definition of Conformal Predictors -- 2.2.5 Validity -- 2.2.6 Smoothed Conformal Predictors -- 2.2.7 Finite-Horizon Conformal Prediction -- 2.2.8 One-Off and Offline Conformal Predictors.
2.2.9 General Schemes for Defining Nonconformity -- Conformity to a Bag -- Conformity to a Property -- 2.2.10 Deleted Conformity Measures -- 2.3 Conformalized Ridge Regression -- 2.3.1 Least Squares and Ridge Regression -- 2.3.2 Basic CRR -- 2.3.3 Two Modifications -- 2.3.4 Dual Form Ridge Regression -- 2.4 Conformalized Nearest Neighbours Regression -- 2.5 Efficiency of Conformalized Ridge Regression -- 2.5.1 Hard and Soft Models -- 2.5.2 Bayesian Ridge Regression -- 2.5.3 Efficiency of CRR -- 2.6 Are There Other Ways to Achieve Validity? -- 2.7 Conformal Transducers -- 2.7.1 Definitions and Properties of Validity -- 2.7.2 Normalized Confidence Predictors and Confidence Transducers -- 2.8 Proofs -- 2.8.1 Proof of Theorem 2.2 -- 2.8.2 Proof of Theorem 2.7 -- Regularizing the Rays in Upper CRR -- Proof Proper -- 2.8.3 Proof of Theorem 2.10 -- 2.9 Context -- 2.9.1 Exchangeability vs Randomness -- 2.9.2 Conformal Prediction -- 2.9.3 Two Equivalent Definitions of Nonconformity Measures -- 2.9.4 The Two Meanings of Conformity in Conformal Prediction -- 2.9.5 Examples of Nonconformity Measures -- 2.9.6 Kernel Methods -- 2.9.7 Burnaev-Wasserman Programme -- 2.9.8 Completeness Results -- 3 Conformal Prediction: Classification and General Case -- 3.1 Criteria of Efficiency for Conformal Prediction -- 3.1.1 Basic Criteria -- 3.1.2 Other Prior Criteria -- 3.1.3 Observed Criteria -- 3.1.4 Idealised Setting -- 3.1.5 Conditionally Proper Criteria of Efficiency -- 3.1.6 Criteria of Efficiency that Are not Conditionally Proper -- 3.1.7 Discussion -- 3.2 More Ways of Computing Nonconformity Scores -- 3.2.1 Nonconformity Scores from Nearest Neighbours -- 3.2.2 Nonconformity Scores from Support Vector Machines -- 3.2.3 Reducing Classification Problems to the Binary Case -- 3.3 Weak Teachers -- 3.3.1 Imperfectly Taught Predictors -- 3.3.2 Weak Validity. 3.3.3 Strong Validity -- 3.3.4 Iterated Logarithm Validity -- 3.3.5 Efficiency -- 3.4 Proofs -- 3.4.1 Proofs for Sect.3.1 -- Proof of Theorem 3.1 -- Proof of Theorem 3.2 -- Proof of Theorem 3.3 -- Proof of Theorem 3.4 -- 3.4.2 Proofs for Sect.3.3 -- Proof of Theorem 3.7, Part I -- Proof of Theorem 3.7, Part II -- Proof of Theorem 3.9 -- Proof of Theorem 3.13 -- 3.5 Context -- 3.5.1 Criteria of Efficiency -- 3.5.2 Examples of Nonconformity Measures -- 3.5.3 Universal Predictors -- 3.5.4 Weak Teachers -- 4 Modifications of Conformal Predictors -- 4.1 The Topics of This Chapter -- 4.2 Inductive Conformal Predictors -- 4.2.1 Inductive Conformal Predictors in the Online Mode -- 4.2.2 Inductive Conformal Predictors in the Offline and Semi-Online Modes -- 4.2.3 The General Scheme for Defining Nonconformity -- 4.2.4 Normalization and Hyper-Parameter Selection -- 4.3 Further Ways of Computing Nonconformity Scores -- 4.3.1 Nonconformity Measures Considered Earlier -- 4.3.2 De-Bayesing -- 4.3.3 Neural Networks and Other Multiclass Scoring Classifiers -- 4.3.4 Decision Trees and Random Forests -- 4.3.5 Binary Scoring Classifiers -- 4.3.6 Logistic Regression -- 4.3.7 Regression and Bootstrap -- 4.3.8 Training Inductive Conformal Predictors -- 4.4 Cross-Conformal Prediction -- 4.4.1 Definition of Cross-Conformal Predictors -- 4.4.2 Computational Efficiency -- 4.4.3 Validity and Lack Thereof for Cross-Conformal Predictors -- 4.5 Transductive Conformal Predictors -- 4.5.1 Definition -- 4.5.2 Validity -- 4.6 Conditional Conformal Predictors -- 4.6.1 One-Off Conditional Conformal Predictors -- 4.6.2 Mondrian Conformal Predictors and Transducers -- 4.6.3 Using Mondrian Conformal Transducers for Prediction -- 4.6.4 Generality of Mondrian Taxonomies -- 4.6.5 Conformal Prediction -- 4.6.6 Inductive Conformal Prediction -- 4.6.7 Label-Conditional Conformal Prediction. 4.6.8 Object-Conditional Conformal Prediction -- 4.7 Training-Conditional Validity -- 4.7.1 Conditional Validity -- 4.7.2 Training-Conditional Validity of Inductive Conformal Predictors -- 4.8 Context -- 4.8.1 Computationally Efficient Hedged Prediction -- 4.8.2 Specific Learning Algorithms and Nonconformity Measures -- 4.8.3 Training Conformal Predictors -- 4.8.4 Cross-Conformal Predictors and Alternative Approaches -- 4.8.5 Transductive Conformal Predictors -- 4.8.6 Conditional Conformal Predictors -- Part II Probabilistic Prediction -- 5 Impossibility Results -- 5.1 Introduction -- 5.2 Diverse Datasets -- 5.3 Impossibility of Estimation of Probabilities -- 5.3.1 Binary Case -- 5.3.2 Multiclass Case -- 5.4 Proof of Theorem 5.2 -- 5.4.1 Probability Estimators and Statistical Tests -- 5.4.2 Complete Statistical Tests -- 5.4.3 Restatement of the Theorem in Terms of Statistical Tests -- 5.4.4 The Proof of the Theorem -- 5.5 Context -- 5.5.1 More Advanced Results -- 5.5.2 Density Estimation, Regression Estimation, and Regression with Deterministic Objects -- 5.5.3 Universal Probabilistic Predictors -- 5.5.4 Algorithmic Randomness Perspective -- 6 Probabilistic Classification: Venn Predictors -- 6.1 Introduction -- 6.2 Venn Predictors -- 6.2.1 Validity of One-Off Venn Predictors -- 6.2.2 Are There Other Ways to Achieve Perfect Calibration? -- 6.2.3 Venn Prediction with Binary Labels and No Objects -- 6.3 A Universal Venn Predictor -- 6.4 Venn-Abers Predictors -- 6.4.1 Full Venn-Abers Predictors -- 6.4.2 Inductive Venn-Abers Predictors -- 6.4.3 Probabilistic Predictors Derived from Venn Predictors -- 6.4.4 Cross Venn-Abers Predictors -- 6.4.5 Merging Multiprobability Predictions into a Probabilistic Prediction -- 6.5 Proofs -- 6.5.1 Proof of Theorem 6.4 -- 6.5.2 PAVA and the Proof of Lemma 6.6 -- 6.5.3 Proof of Proposition 6.7 -- 6.6 Context. 6.6.1 Risk and Uncertainty -- 6.6.2 John Venn, Frequentist Probability, and the Problem of the Reference Class -- 6.6.3 Online Venn Predictors Are Calibrated -- 6.6.4 Isotonic Regression -- 7 Probabilistic Regression: Conformal Predictive Systems -- 7.1 Introduction -- 7.2 Conformal Predictive Systems -- 7.2.1 Basic Definitions -- 7.2.2 Properties of Validity -- 7.2.3 Simplest Example: Monotonic Conformity Measures -- 7.2.4 Criterion of Being a CPS -- 7.3 Least Squares Prediction Machine -- 7.3.1 Three Kinds of LSPM -- 7.3.2 The Studentized LSPM in an Explicit Form -- 7.3.3 The Offline Version of the Studentized LSPM -- 7.3.4 The Ordinary LSPM -- 7.3.5 Asymptotic Efficiency of the LSPM -- 7.3.6 Illustrations -- 7.4 Kernel Ridge Regression Prediction Machine -- 7.4.1 Explicit Forms of the KRRPM -- 7.4.2 Limitation of the KRRPM -- 7.5 Nearest Neighbours Prediction Machine -- 7.6 Universal Conformal Predictive Systems -- 7.6.1 Definitions -- 7.6.2 Universal Conformal Predictive Systems -- 7.6.3 Universal Deterministic Predictive Systems -- 7.7 Applications to Decision Making -- 7.7.1 A Standard Problem of Decision Making -- 7.7.2 Examples -- 7.7.3 Asymptotically Efficient Decision Making -- 7.7.4 Dangers of Overfitting -- 7.8 Computationally Efficient Versions -- 7.8.1 Inductive Conformal Predictive Systems -- 7.8.2 Cross-Conformal Predictive Distributions -- 7.8.3 Practical Aspects -- 7.8.4 Beyond Randomness -- 7.9 Proofs and Calculations -- 7.9.1 Proofs for Sect.7.2 -- Proof of Lemma 7.1 -- Proof of Proposition 7.2 -- 7.9.2 Proofs for Sect.7.3 -- Proof of Proposition 7.4 -- Proof of Proposition 7.5 -- Proof of Proposition 7.6 -- Proof of Proposition 7.7 -- Proof of Proposition 7.8 -- Computations for the Studentized LSPM -- The Ordinary LSPM -- Proof of (7.22) -- 7.9.3 Proof of Theorem 7.16 -- 7.9.4 Proofs for Sect.7.8 -- Proof of Proposition 7.17. Proof of Proposition 7.18. |
| Record Nr. | UNISA-996503551103316 |
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Vovk Vladimir <1960->
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| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer
| Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer |
| Autore | Vovk Vladimir <1960-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (490 pages) |
| Disciplina | 518.1 |
| Soggetto topico |
Algorithms
Algorithms - Study and teaching Teoria de la predicció Algorismes Processos estocàstics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-06649-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Notation and Abbreviations -- Sets, Bags, and Sequences -- Stochastics -- Machine Learning -- Programming -- Confidence Prediction -- Other Notations -- Abbreviations -- 1 Introduction -- 1.1 Machine Learning -- 1.1.1 Learning Under Randomness -- 1.1.2 Learning Under Unconstrained Randomness -- 1.2 A Shortcoming of Statistical Learning Theory -- 1.2.1 The Hold-Out Estimate of Confidence -- 1.2.2 The Contribution of This Book -- 1.3 The Online Framework -- 1.3.1 Online Learning -- 1.3.2 Online/Offline Compromises -- 1.3.3 One-Off and Offline Learning -- 1.3.4 Induction, Transduction, and the Online Framework -- 1.4 Conformal Prediction -- 1.4.1 Nested Prediction Sets -- 1.4.2 Validity -- 1.4.3 Efficiency -- 1.4.4 Conditionality -- 1.4.5 Flexibility of Conformal Predictors -- 1.5 Probabilistic Prediction Under Unconstrained Randomness -- 1.5.1 Universally Consistent Probabilistic Predictor -- 1.5.2 Probabilistic Prediction Using a Finite Dataset -- 1.5.3 Venn Prediction -- 1.5.4 Conformal Predictive Distributions -- 1.6 Beyond Randomness -- 1.6.1 Testing Randomness -- 1.6.2 Online Compression Models -- 1.7 Context -- Part I Set Prediction -- 2 Conformal Prediction: General Case and Regression -- 2.1 Confidence Predictors -- 2.1.1 Assumptions -- 2.1.2 Simple Predictors and Confidence Predictors -- 2.1.3 Validity -- 2.1.4 Randomized Confidence Predictors -- 2.1.5 Confidence Predictors Over a Finite Horizon -- 2.1.6 One-Off and Offline Confidence Predictors -- 2.2 Conformal Predictors -- 2.2.1 Bags -- 2.2.2 Nonconformity and Conformity -- 2.2.3 p-Values -- 2.2.4 Definition of Conformal Predictors -- 2.2.5 Validity -- 2.2.6 Smoothed Conformal Predictors -- 2.2.7 Finite-Horizon Conformal Prediction -- 2.2.8 One-Off and Offline Conformal Predictors.
2.2.9 General Schemes for Defining Nonconformity -- Conformity to a Bag -- Conformity to a Property -- 2.2.10 Deleted Conformity Measures -- 2.3 Conformalized Ridge Regression -- 2.3.1 Least Squares and Ridge Regression -- 2.3.2 Basic CRR -- 2.3.3 Two Modifications -- 2.3.4 Dual Form Ridge Regression -- 2.4 Conformalized Nearest Neighbours Regression -- 2.5 Efficiency of Conformalized Ridge Regression -- 2.5.1 Hard and Soft Models -- 2.5.2 Bayesian Ridge Regression -- 2.5.3 Efficiency of CRR -- 2.6 Are There Other Ways to Achieve Validity? -- 2.7 Conformal Transducers -- 2.7.1 Definitions and Properties of Validity -- 2.7.2 Normalized Confidence Predictors and Confidence Transducers -- 2.8 Proofs -- 2.8.1 Proof of Theorem 2.2 -- 2.8.2 Proof of Theorem 2.7 -- Regularizing the Rays in Upper CRR -- Proof Proper -- 2.8.3 Proof of Theorem 2.10 -- 2.9 Context -- 2.9.1 Exchangeability vs Randomness -- 2.9.2 Conformal Prediction -- 2.9.3 Two Equivalent Definitions of Nonconformity Measures -- 2.9.4 The Two Meanings of Conformity in Conformal Prediction -- 2.9.5 Examples of Nonconformity Measures -- 2.9.6 Kernel Methods -- 2.9.7 Burnaev-Wasserman Programme -- 2.9.8 Completeness Results -- 3 Conformal Prediction: Classification and General Case -- 3.1 Criteria of Efficiency for Conformal Prediction -- 3.1.1 Basic Criteria -- 3.1.2 Other Prior Criteria -- 3.1.3 Observed Criteria -- 3.1.4 Idealised Setting -- 3.1.5 Conditionally Proper Criteria of Efficiency -- 3.1.6 Criteria of Efficiency that Are not Conditionally Proper -- 3.1.7 Discussion -- 3.2 More Ways of Computing Nonconformity Scores -- 3.2.1 Nonconformity Scores from Nearest Neighbours -- 3.2.2 Nonconformity Scores from Support Vector Machines -- 3.2.3 Reducing Classification Problems to the Binary Case -- 3.3 Weak Teachers -- 3.3.1 Imperfectly Taught Predictors -- 3.3.2 Weak Validity. 3.3.3 Strong Validity -- 3.3.4 Iterated Logarithm Validity -- 3.3.5 Efficiency -- 3.4 Proofs -- 3.4.1 Proofs for Sect.3.1 -- Proof of Theorem 3.1 -- Proof of Theorem 3.2 -- Proof of Theorem 3.3 -- Proof of Theorem 3.4 -- 3.4.2 Proofs for Sect.3.3 -- Proof of Theorem 3.7, Part I -- Proof of Theorem 3.7, Part II -- Proof of Theorem 3.9 -- Proof of Theorem 3.13 -- 3.5 Context -- 3.5.1 Criteria of Efficiency -- 3.5.2 Examples of Nonconformity Measures -- 3.5.3 Universal Predictors -- 3.5.4 Weak Teachers -- 4 Modifications of Conformal Predictors -- 4.1 The Topics of This Chapter -- 4.2 Inductive Conformal Predictors -- 4.2.1 Inductive Conformal Predictors in the Online Mode -- 4.2.2 Inductive Conformal Predictors in the Offline and Semi-Online Modes -- 4.2.3 The General Scheme for Defining Nonconformity -- 4.2.4 Normalization and Hyper-Parameter Selection -- 4.3 Further Ways of Computing Nonconformity Scores -- 4.3.1 Nonconformity Measures Considered Earlier -- 4.3.2 De-Bayesing -- 4.3.3 Neural Networks and Other Multiclass Scoring Classifiers -- 4.3.4 Decision Trees and Random Forests -- 4.3.5 Binary Scoring Classifiers -- 4.3.6 Logistic Regression -- 4.3.7 Regression and Bootstrap -- 4.3.8 Training Inductive Conformal Predictors -- 4.4 Cross-Conformal Prediction -- 4.4.1 Definition of Cross-Conformal Predictors -- 4.4.2 Computational Efficiency -- 4.4.3 Validity and Lack Thereof for Cross-Conformal Predictors -- 4.5 Transductive Conformal Predictors -- 4.5.1 Definition -- 4.5.2 Validity -- 4.6 Conditional Conformal Predictors -- 4.6.1 One-Off Conditional Conformal Predictors -- 4.6.2 Mondrian Conformal Predictors and Transducers -- 4.6.3 Using Mondrian Conformal Transducers for Prediction -- 4.6.4 Generality of Mondrian Taxonomies -- 4.6.5 Conformal Prediction -- 4.6.6 Inductive Conformal Prediction -- 4.6.7 Label-Conditional Conformal Prediction. 4.6.8 Object-Conditional Conformal Prediction -- 4.7 Training-Conditional Validity -- 4.7.1 Conditional Validity -- 4.7.2 Training-Conditional Validity of Inductive Conformal Predictors -- 4.8 Context -- 4.8.1 Computationally Efficient Hedged Prediction -- 4.8.2 Specific Learning Algorithms and Nonconformity Measures -- 4.8.3 Training Conformal Predictors -- 4.8.4 Cross-Conformal Predictors and Alternative Approaches -- 4.8.5 Transductive Conformal Predictors -- 4.8.6 Conditional Conformal Predictors -- Part II Probabilistic Prediction -- 5 Impossibility Results -- 5.1 Introduction -- 5.2 Diverse Datasets -- 5.3 Impossibility of Estimation of Probabilities -- 5.3.1 Binary Case -- 5.3.2 Multiclass Case -- 5.4 Proof of Theorem 5.2 -- 5.4.1 Probability Estimators and Statistical Tests -- 5.4.2 Complete Statistical Tests -- 5.4.3 Restatement of the Theorem in Terms of Statistical Tests -- 5.4.4 The Proof of the Theorem -- 5.5 Context -- 5.5.1 More Advanced Results -- 5.5.2 Density Estimation, Regression Estimation, and Regression with Deterministic Objects -- 5.5.3 Universal Probabilistic Predictors -- 5.5.4 Algorithmic Randomness Perspective -- 6 Probabilistic Classification: Venn Predictors -- 6.1 Introduction -- 6.2 Venn Predictors -- 6.2.1 Validity of One-Off Venn Predictors -- 6.2.2 Are There Other Ways to Achieve Perfect Calibration? -- 6.2.3 Venn Prediction with Binary Labels and No Objects -- 6.3 A Universal Venn Predictor -- 6.4 Venn-Abers Predictors -- 6.4.1 Full Venn-Abers Predictors -- 6.4.2 Inductive Venn-Abers Predictors -- 6.4.3 Probabilistic Predictors Derived from Venn Predictors -- 6.4.4 Cross Venn-Abers Predictors -- 6.4.5 Merging Multiprobability Predictions into a Probabilistic Prediction -- 6.5 Proofs -- 6.5.1 Proof of Theorem 6.4 -- 6.5.2 PAVA and the Proof of Lemma 6.6 -- 6.5.3 Proof of Proposition 6.7 -- 6.6 Context. 6.6.1 Risk and Uncertainty -- 6.6.2 John Venn, Frequentist Probability, and the Problem of the Reference Class -- 6.6.3 Online Venn Predictors Are Calibrated -- 6.6.4 Isotonic Regression -- 7 Probabilistic Regression: Conformal Predictive Systems -- 7.1 Introduction -- 7.2 Conformal Predictive Systems -- 7.2.1 Basic Definitions -- 7.2.2 Properties of Validity -- 7.2.3 Simplest Example: Monotonic Conformity Measures -- 7.2.4 Criterion of Being a CPS -- 7.3 Least Squares Prediction Machine -- 7.3.1 Three Kinds of LSPM -- 7.3.2 The Studentized LSPM in an Explicit Form -- 7.3.3 The Offline Version of the Studentized LSPM -- 7.3.4 The Ordinary LSPM -- 7.3.5 Asymptotic Efficiency of the LSPM -- 7.3.6 Illustrations -- 7.4 Kernel Ridge Regression Prediction Machine -- 7.4.1 Explicit Forms of the KRRPM -- 7.4.2 Limitation of the KRRPM -- 7.5 Nearest Neighbours Prediction Machine -- 7.6 Universal Conformal Predictive Systems -- 7.6.1 Definitions -- 7.6.2 Universal Conformal Predictive Systems -- 7.6.3 Universal Deterministic Predictive Systems -- 7.7 Applications to Decision Making -- 7.7.1 A Standard Problem of Decision Making -- 7.7.2 Examples -- 7.7.3 Asymptotically Efficient Decision Making -- 7.7.4 Dangers of Overfitting -- 7.8 Computationally Efficient Versions -- 7.8.1 Inductive Conformal Predictive Systems -- 7.8.2 Cross-Conformal Predictive Distributions -- 7.8.3 Practical Aspects -- 7.8.4 Beyond Randomness -- 7.9 Proofs and Calculations -- 7.9.1 Proofs for Sect.7.2 -- Proof of Lemma 7.1 -- Proof of Proposition 7.2 -- 7.9.2 Proofs for Sect.7.3 -- Proof of Proposition 7.4 -- Proof of Proposition 7.5 -- Proof of Proposition 7.6 -- Proof of Proposition 7.7 -- Proof of Proposition 7.8 -- Computations for the Studentized LSPM -- The Ordinary LSPM -- Proof of (7.22) -- 7.9.3 Proof of Theorem 7.16 -- 7.9.4 Proofs for Sect.7.8 -- Proof of Proposition 7.17. Proof of Proposition 7.18. |
| Record Nr. | UNINA-9910635392203321 |
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Vovk Vladimir <1960->
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| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithmic puzzles / / Anany Levitin and Maria Levitin
| Algorithmic puzzles / / Anany Levitin and Maria Levitin |
| Autore | Levitin Anany |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, c2011 |
| Descrizione fisica | 1 online resource (280 p.) |
| Disciplina | 793.74 |
| Altri autori (Persone) | LevitinMaria |
| Collana | Oxford scholarship online |
| Soggetto topico |
Mathematical recreations
Algorithms Algorismes Trencaclosques |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9786613299895
9780197563021 0197563023 9780199911776 0199911770 9781283299893 1283299895 9780199876549 0199876541 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover; Contents; Preface; Acknowledgments; List of Puzzles; Tutorial Puzzles; Main Section Puzzles; The Epigraph Puzzle: Who said what?; 1. Tutorials; General Strategies for Algorithm Design; Analysis Techniques; 2. Puzzles; Easier Puzzles (#1 to #50); Puzzles of Medium Difficulty (#51 to #110); Harder Puzzles (#111 to #150); 3. Hints; 4. Solutions; References; Design Strategy and Analysis Index; Index of Terms and Names; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; V; W |
| Record Nr. | UNINA-9910960991503321 |
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Levitin Anany
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| Oxford ; ; New York, : Oxford University Press, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithmica
| Algorithmica |
| Pubbl/distr/stampa | New York : , : Springer-Verlag, , 1986- |
| Disciplina | 004 |
| Soggetto topico |
Electronic data processing
Computer algorithms Algorithmes Informatique Technologie de l'information Algorithmus Zeitschrift Online-Ressource Datenverarbeitung Algorismes |
| Soggetto genere / forma |
Periodicals.
Zeitschrift Online-Publikation Revistes electròniques. |
| ISSN | 1432-0541 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910146354403321 |
| New York : , : Springer-Verlag, , 1986- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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