Victor Bittorf

1.2k citations
5 papers · 254 indexed · h-index 4
Journals
Journal of Machine Learning Research (1 paper)Neural Information Processing Systems (2 papers)Conference on Innovative Data Systems Research (1 paper)
Partner nations
United States

In The Last Decade

Victor Bittorf

5 papers receiving 235 citations

Peers

Victor Bittorf
Comparison fields: 5 of 53
  • Computational Mathematics 8
  • Signal Processing 51
  • Artificial Intelligence 145
  • Computational Mechanics 70
  • Computer Vision and Pattern Recognition 63
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Boulos Harb United States
Anastasios Zouzias Canada
Morteza Monemizadeh United States
Radha Chitta United States
Ankan Saha United States
Jinliang Fan United States
Ashwin Pananjady United States
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Citations per field
00.5×1.6×
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Citations per year

Countries citing papers authored by Victor Bittorf

Since Specialization
Citations

This map shows the geographic impact of Victor Bittorf's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Victor Bittorf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Victor Bittorf more than expected).

Fields of papers citing papers by Victor Bittorf

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Victor Bittorf. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Victor Bittorf. The network helps show where Victor Bittorf may publish in the future.

Co-authorship network

The 16 scholars most cited alongside Victor Bittorf, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Victor Bittorf Line = papers co-authored together Victor Bittorf links everyone, so they are left out of the graph.

All Works

5 of 5 papers shown
#Work
1
Tradeoffs in Main-Memory Statistical Analytics from Impala to DimmWitted.
20141
2
Brainwash: A data system for feature engineering
201368
3
An Approximate, Efficient LP Solver for LP Rounding
20138
4 201394
5
Factoring nonnegative matrices with linear programs
201283

About Victor Bittorf

Victor Bittorf is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Information Systems and Management, having authored 5 papers that have together received 254 indexed citations. Recurring topics across this work include Complexity and Algorithms in Graphs (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Neural Networks and Applications (2 papers), Machine Learning and Algorithms (2 papers), Sparse and Compressive Sensing Techniques (1 paper), Error Correcting Code Techniques (1 paper), Data Stream Mining Techniques (1 paper) and Matrix Theory and Algorithms (1 paper). The work is most often cited by research in Computational Mathematics (8 citations), Signal Processing (51 citations) and Artificial Intelligence (145 citations). Victor Bittorf has collaborated with scholars based in United States. Frequent co-authors include Christopher Ré, Ben Recht, Joel A. Tropp, Steve Wright, Ji Liu, Matthew Burgess, Feng Niu, Ce Zhang, Yongjoo Park and Michael Cafarella. Their work appears in journals such as Journal of Machine Learning Research, Neural Information Processing Systems and Conference on Innovative Data Systems Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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