☆ 3.8 Proceedings Paper

A General Theory of Sample Complexity for Multi-Item Profit Maximization

ACM EC'18: PROCEEDINGS OF THE 2018 ACM CONFERENCE ON ECONOMICS AND COMPUTATION (2018)

Get Full Text
Add to Collection

Journal

ACM EC'18: PROCEEDINGS OF THE 2018 ACM CONFERENCE ON ECONOMICS AND COMPUTATION
Volume -, Issue -, Pages 173-174

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3219166.3219217

Keywords

Learning theory; machine learning; profit maximization; revenue maximization; combinatorial auctions

Categories

Computer Science, Theory & Methods

Funding

  1. National Science Foundation [CCF-1422910, CCF-1535967, IIS-1618714, IIS-1718457, IIS-1617590, CCF-1733556]
  2. Microsoft Research Faculty Fellowship
  3. Amazon Research Award
  4. NSF Graduate Research Fellowship
  5. Microsoft Research Women's Fellowship
  6. ARO [W911NF-17-1-0082]

Ask authors/readers for more resources

Protocol

Community support

Reagent

Community support

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

3.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available
Webinars Posters Questions Hubs Funding Journals Papers Connect Collections Reviewers About Us FAQs Mobile App Privacy Policy Terms of Use
© Peeref 2019-2024. All rights reserved.