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A PAC Approach to Application-Specific Algorithm Selection

A PAC Approach to Application-Specific Algorithm Selection

23 November 2015
Rishi Gupta
Tim Roughgarden
ArXivPDFHTML

Papers citing "A PAC Approach to Application-Specific Algorithm Selection"

14 / 64 papers shown
Title
Learning Complexity of Simulated Annealing
Learning Complexity of Simulated Annealing
Avrim Blum
Chen Dan
Saeed Seddighin
90
18
0
06 Mar 2020
How much data is sufficient to learn high-performing algorithms?
  Generalization guarantees for data-driven algorithm design
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
Maria-Florina Balcan
Dan F. DeBlasio
Travis Dick
Carl Kingsford
T. Sandholm
Ellen Vitercik
21
34
0
08 Aug 2019
Learning to Link
Learning to Link
Maria-Florina Balcan
Travis Dick
Manuel Lang
18
24
0
01 Jul 2019
Data-driven Algorithm Selection and Parameter Tuning: Two Case studies
  in Optimization and Signal Processing
Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing
J. D. Loera
Jamie Haddock
A. Ma
Deanna Needell
8
0
0
31 May 2019
Learning to Optimize Computational Resources: Frugal Training with
  Generalization Guarantees
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
Maria-Florina Balcan
T. Sandholm
Ellen Vitercik
11
16
0
26 May 2019
Learning to Prune: Speeding up Repeated Computations
Learning to Prune: Speeding up Repeated Computations
Daniel Alabi
Adam Tauman Kalai
Katrina Ligett
Cameron Musco
Christos Tzamos
Ellen Vitercik
12
19
0
26 Apr 2019
Semi-bandit Optimization in the Dispersed Setting
Semi-bandit Optimization in the Dispersed Setting
Maria-Florina Balcan
Travis Dick
W. Pegden
8
20
0
18 Apr 2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Giulia Denevi
C. Ciliberto
Riccardo Grazzi
Massimiliano Pontil
20
108
0
25 Mar 2019
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive
  Algorithm Configuration
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
Robert D. Kleinberg
Kevin Leyton-Brown
Brendan Lucier
Devon R. Graham
9
20
0
14 Feb 2019
Uniform Convergence Bounds for Codec Selection
Uniform Convergence Bounds for Codec Selection
Clayton Sanford
Cyrus Cousins
E. Upfal
18
0
0
18 Dec 2018
Learning to Branch
Learning to Branch
Maria-Florina Balcan
Travis Dick
T. Sandholm
Ellen Vitercik
11
169
0
27 Mar 2018
Dispersion for Data-Driven Algorithm Design, Online Learning, and
  Private Optimization
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
Maria-Florina Balcan
Travis Dick
Ellen Vitercik
19
73
0
08 Nov 2017
Learning-Theoretic Foundations of Algorithm Configuration for
  Combinatorial Partitioning Problems
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
Maria-Florina Balcan
Vaishnavh Nagarajan
Ellen Vitercik
Colin White
19
61
0
14 Nov 2016
Online Optimization of Smoothed Piecewise Constant Functions
Online Optimization of Smoothed Piecewise Constant Functions
Vincent Cohen-Addad
Varun Kanade
12
25
0
07 Apr 2016
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