Adaptive Algorithms and Stochastic Approximations

By Albert Benveniste, M. Métivier, Pierre Priouret. 1990

Subfields: Optimization & Training, Mathematical Optimization

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Optimization & Training

1. Michael I. Jordan
2. Francis R. Bach
3. Sham M. Kakade
4. Peter Richtárik
5. Yoshua Bengio
6. Praneeth Netrapalli
7. Csaba Szepesvari
8. Martin J. Wainwright

In Mathematical Optimization

1. Mengdi Wang
2. Csaba Szepesvari
3. John N. Tsitsiklis
4. Mohammad Ghavamzadeh
5. Benjamin Van Roy
6. Richard S. Sutton
7. Sham M. Kakade
8. Yichen Chen

Citations in the corpus

• Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning (2016, background): “For textbook references on stochastic approximation, please see (Kushner and Yin, 2003; Benveniste et al., 2012; Borkar, 2008; Bertsekas and Tsitsiklis, 1989).”
• An online primal-dual method for discounted Markov decision processes (2016, methodology): “[3], by Borkar [7], by Bertsekas and Tsitsiklis [6]).”
• Naive Reinforcement Learning With Endogenous Aspirations (2000, methodology): “The differential equations we study are frequently used to study the long-term behavior (e.g., Benveniste et al., 1990; Binmore et al., 1995) of the associated stochastic dynamic model.”