ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1802.07543
  4. Cited By
The Many Faces of Exponential Weights in Online Learning
v1v2 (latest)

The Many Faces of Exponential Weights in Online Learning

21 February 2018
Dirk van der Hoeven
T. Erven
W. Kotłowski
    OffRL
ArXiv (abs)PDFHTML

Papers citing "The Many Faces of Exponential Weights in Online Learning"

11 / 11 papers shown
Title
Introduction to Online Convex Optimization
Introduction to Online Convex Optimization
Elad Hazan
OffRL
188
1,940
0
07 Sep 2019
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
Laurent Orseau
Tor Lattimore
Shane Legg
25
22
0
08 Jan 2019
Combining Adversarial Guarantees and Stochastic Fast Rates in Online
  Learning
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
Wouter M. Koolen
Peter Grünwald
T. Erven
65
38
0
20 May 2016
MetaGrad: Multiple Learning Rates in Online Learning
MetaGrad: Multiple Learning Rates in Online Learning
T. Erven
Wouter M. Koolen
ODL
86
98
0
29 Apr 2016
Coin Betting and Parameter-Free Online Learning
Coin Betting and Parameter-Free Online Learning
Francesco Orabona
D. Pál
164
166
0
12 Feb 2016
Second-order Quantile Methods for Experts and Combinatorial Games
Second-order Quantile Methods for Experts and Combinatorial Games
Wouter M. Koolen
T. Erven
96
105
0
27 Feb 2015
The entropic barrier: a simple and optimal universal self-concordant
  barrier
The entropic barrier: a simple and optimal universal self-concordant barrier
Sébastien Bubeck
Ronen Eldan
74
65
0
04 Dec 2014
Efficient Sampling from Time-Varying Log-Concave Distributions
Efficient Sampling from Time-Varying Log-Concave Distributions
Hariharan Narayanan
Alexander Rakhlin
54
47
0
23 Sep 2013
A Generalized Online Mirror Descent with Applications to Classification
  and Regression
A Generalized Online Mirror Descent with Applications to Classification and Regression
Francesco Orabona
K. Crammer
Nicolò Cesa-Bianchi
188
79
0
10 Apr 2013
Follow the Leader If You Can, Hedge If You Must
Follow the Leader If You Can, Hedge If You Must
S. D. Rooij
T. Erven
Peter Grünwald
Wouter M. Koolen
202
181
0
03 Jan 2013
Towards minimax policies for online linear optimization with bandit
  feedback
Towards minimax policies for online linear optimization with bandit feedback
Sébastien Bubeck
Nicolò Cesa-Bianchi
Sham Kakade
OffRL
288
151
0
14 Feb 2012
1