Ben London

620 citations

18 papers · 263 indexed · h-index 9

Artificial Intelligence top 5%
- Machine Learning and Algorithms 9
- Bayesian Modeling and Causal Inference 5
- Domain Adaptation and Few-Shot Learning 4
- Gaussian Processes and Bayesian Inference 3
- Stochastic Gradient Optimization Techniques 3
- Data Stream Mining Techniques 2
- Machine Learning and Data Classification 2
Statistical and Nonlinear Physics top 10%
Information Systems top 10%
Management Science and Operations Research
Computer Science Applications
Computer Vision and Pattern Recognition
- Human Pose and Action Recognition 2

Co-authors: Lise Getoor Bert Huang Galileo Namata Stephen H. Bach Steven Isley Ben Taskar Jay Pujara Sameh Khamis
Cited by: Artificial Intelligence Statistical and Nonlinear Physics Information Systems
Journals: Journal of Machine Learning Research (1 paper)AI Magazine (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)
Partner nations: United States Germany United Kingdom

In The Last Decade

Ben London

18 papers receiving 242 citations

Peers

Countries citing papers authored by Ben London

Since Specialization

Citations

This map shows the geographic impact of Ben London'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 Ben London with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben London more than expected).

Fields of papers citing papers by Ben London

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ben London. 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 Ben London. The network helps show where Ben London may publish in the future.

Co-authorship network

The 15 scholars most cited alongside Ben London, 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 Ben London Line = papers co-authored together Ben London links everyone, so they are left out of the graph.

All Works

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18 of 18 papers shown

#	Work
1	Practical Bandits: An Industry Perspective Bram van den Akker,Olivier Jeunen,Ying Li,Ben London,Zahra Nazari,Devesh Parekh	2024	4
2	Recommendations as treatments AI Magazine ·Thorsten Joachims,Ben London,Yi Su,Adith Swaminathan,Lequn Wang	2021	7
3	Sustainability at scale Sabina Tomkins,Steven Isley,Ben London,Lise Getoor	2018	28
4	A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent Neural Information Processing Systems ·Ben London	2017	8
5	Generalization Bounds for Randomized Learning with Application to Stochastic Gradient Descent Ben London	2017	2
6	Stability and generalization in structured prediction Journal of Machine Learning Research ·Ben London,Bert Huang,Lise Getoor	2016	15
7	Generative Adversarial Structured Networks Ben London,Alex Schwing	2016	1
8	Collective Graph Identification ACM Transactions on Knowledge Discovery from Data ·Galileo Namata,Ben London,Lise Getoor	2016	9
9	Budgeted online collective inference Uncertainty in Artificial Intelligence ·Jay Pujara,Ben London,Lise Getoor	2015	3
10	The Benefits of Learning with Strongly Convex Approximate Inference Ben London,Bert Huang,Lise Getoor	2015	2
11	{PAC-Bayesian Collective Stability} International Conference on Artificial Intelligence and Statistics ·Ben London,Bert Huang,Ben Taskar,Lise Getoor	2014	7
12	Collective Classification of Network Data. Ben London,Lise Getoor	2014	17
13	Hinge-loss Markov random fields: convex inference for structured prediction arXiv (Cornell University) ·Stephen H. Bach,Bert Huang,Ben London,Lise Getoor	2013	33
14	Improved Generalization Bounds for Large-scale Structured Prediction Ben London,Bert Huang,Lise Getoor	2013	1
15	Collective Activity Detection Using Hinge-loss Markov Random Fields Ben London,Sameh Khamis,Stephen H. Bach,Bert Huang,Lise Getoor,Larry S. Davis	2013	9
16	Collective Stability in Structured Prediction: Generalization from One Example Ben London,Bert Huang,Ben Taskar,Lise Getoor	2013	11
17	Query-driven active surveying for collective classification Galileo Namata,Ben London,Lise Getoor,Bert Huang	2012	100
18	Reducing Label Cost by Combining Feature Labels and Crowdsourcing Jay Pujara,Ben London,Lise Getoor	2011	6

About Ben London

Ben London is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 18 papers that have together received 263 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (9 papers), Bayesian Modeling and Causal Inference (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Gaussian Processes and Bayesian Inference (3 papers), Stochastic Gradient Optimization Techniques (3 papers), Data Stream Mining Techniques (2 papers), Human Pose and Action Recognition (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Artificial Intelligence (210 citations), Statistical and Nonlinear Physics (67 citations) and Information Systems (52 citations). Ben London has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Lise Getoor, Bert Huang, Galileo Namata, Stephen H. Bach, Steven Isley, Ben Taskar, Jay Pujara, Sameh Khamis, Larry S. Davis and Zahra Nazari. Their work appears in journals such as Journal of Machine Learning Research, AI Magazine and ACM Transactions on Knowledge Discovery from Data.

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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