- Convex Q|Learning🔍
- Convex Optimization for Machine Learning🔍
- Convex Q|Learning in Continuous Time with Application to Dispatch ...🔍
- Reinforcement Learning for Linear|Convex Models with Jumps via ...🔍
- Convex learning problems🔍
- Convex Learning with Invariances🔍
- Reinforcement learning through convex programming🔍
- Reinforcement Learning with Convex Constraints🔍
Convex Q|Learning
Convex Q-Learning: Theory and Applications - ProQuest
A new class of Q-learning algorithms called convex Q-learning has been proposed in this dissertation, along with a sequence of theoretical results.
Convex Optimization for Machine Learning - Now Publishers
This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer.
Convex Q-Learning in Continuous Time with Application to Dispatch ...
(iii) Convex Q-learning with linear function approximation is a convex program. It is shown that the constraint region is bounded, subject to an ...
Reinforcement Learning for Linear-Convex Models with Jumps via ...
Hence we establish the sub-Weibull properties of the stochastic integrals by applying the Burkholder's inequality to estimate the growth of their \(L^q\) -norms ...
▻ In general, a convex learning problem is a problem. 1. whose hypothesis class is a convex set and. 2. whose loss function is a convex function ...
Convex Learning with Invariances - NIPS papers
Authors. Choon Teo, Amir Globerson, Sam Roweis, Alex Smola. Abstract. Incorporating invariances into a learning algorithm is a common problem in ma- chine ...
Reinforcement learning through convex programming - OptiML PSE
We propose convex functions instead of deep neural networks as state-action value function approximators to reduce computational complexity. A convex Q-function ...
Reinforcement learning through convex programming - OUCI
Convex Q-learning: Reinforcement learning through convex programming. https://doi.org/10.1016/b978-0-323-85159-6.50056-7. Journal: Computer Aided Chemical ...
Reinforcement Learning with Convex Constraints - Sobhan Miryoosefi
current policy (possibly specified implicitly as a Q-function).2 Furthermore, the average of the mea- surement vectors z collected over the last n ...
Convex Optimization for Machine Learning - Master 2 Computer ...
Under some assumptions (to come), this inequality can be strenghtened, making gradient descent more relevant. 5. Page 9. Convergence of GD for Convex-Lipschitz ...
Difference-of-Convex Learning: Directional Stationarity, Optimality ...
Difference-of-Convex Learning: Directional Stationarity, Optimality, and Sparsity ... Q. Yao and J.T. Kwok, Efficient learning with a family of nonconvex ...
Online Learning and Online Convex Optimization - Now Publishers
The goal of online learning is to make a sequence of accurate predictions given knowledge of the correct answer to previous prediction tasks and ...
Learning Convex Optimization Models
Learning Convex Optimization Models. A. Agrawal, S. Barratt, and S. Boyd. (Authors listed in alphabetical order.) IEEE/CAA Journal of Automatica Sinica, 8(8): ...
Convex Functions for Reinforcement Learning - Siddartha Devic
exploring convexity in deep reinforcement learning, we believe that we can outperform them in the Q-learning setting using our simple convex function ...
An Optimal Algorithm for Online Non-Convex Learning
In many online learning paradigms, convexity plays a central role in the derivation and analysis of online learning algorithms.
Coefficient estimates of new classes of q-starlike and q-convex ...
Abstract. We introduce new classes of q-starlike and q-convex functions of complex order in- volving the q-derivative operator defined in the open ...
CPSC 540: Machine Learning - Convex Optimization
The function f is a convex function. This lecture is boring, but convexity ideas will show up throughout the course. Page 5 ...
CAREER: Advancing Constrained and Non-Convex Learning
... Convex Optimization with Expectation Constraints" Journal of machine learning research , 2020 Citation Details. Liu, M. and Rafique, H. and Lin, Q. and Yang ...
Machine learning theory - Convex learning problems
Let (H,Z,`) be a convex-Lipschitz-bounded learning problem with parameters ρ,B. For any training set size m, let λ = q. 2ρ2. B2m . Then,the ...
Lecture 2 (Convex Functions Cont, Analysis of Gradient Descent)
Optimization in Machine Learning: Lecture 2 - Continuation of Convex Functions: Properties, Examples, Global Miniman and Local Minima ...