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Convex Analysisの二,三の進展について
徹 丸山
- Vol. 70, Iss: 1, pp 97-119
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The article was published on 1977-02-01 and is currently open access. It has received 5933 citations till now.read more
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Implicit Online Learning
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A microscopic model for the burgers equation and longest increasing subsequences
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Monotonicity and Implementability
TL;DR: In this paper, the set of monotonicity domains of valuation functions for an agent with private values and quasilinear utility functions is characterized. But the set is not restricted to monotone randomized allocation.
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Additive models, boosting, and inference for generalized divergences
TL;DR: A framework for designing incremental learning algorithms derived from generalized entropy functionals based on the use of Bregman divergences together with the associated class of additive models constructed using the Legendre transform is presented.
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A Scenario Approach for Non-Convex Control Design
TL;DR: A novel scenario approach is derived for a wide class of random non-convex programs, with a sample complexity similar to that of uncertain convex programs and with probabilistic guarantees that hold not only for the optimal solution of the scenario program, but for all feasible solutions inside a set of a-priori chosen complexity.
References
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Deep Learning
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Increasing Returns and Long-Run Growth
TL;DR: In this paper, the authors present a fully specified model of long-run growth in which knowledge is assumed to be an input in production that has increasing marginal productivity, which is essentially a competitive equilibrium model with endogenous technological change.
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Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.