Ethan Katz-Bassett

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Ethan Katz-Bassett trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Christoph Helmberg
  2. Ramesh Govindan
  3. Paramvir Victor Bahl
  4. Neil T. Spring
  5. Frank Dabek
  6. V. Padmanabhan
  7. Hongsuda Tangmunarunkit
  8. Anthony LaMarca
  9. Ratul Mahajan

Papers from Ethan Katz-Bassett's perspective

Ordered by how strongly the researchers Ethan Katz-Bassett trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Developing a predictive model of quality of experience for internet video (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
  2. RADAR: an in-building RF-based user location and tracking system (Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064))
  3. Understanding the impact of video quality on user engagement (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
  4. Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs (ACM/SIGCOMM Internet Measurement Conference)
  5. Measuring ISP topologies with Rocketfuel (IEEE/ACM Transactions on Networking)
  6. Heuristics for Internet map discovery (Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064))
  7. Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE (Conference on Emerging Network Experiment and Technology)
  8. A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (Computer communication review)
  9. Towards agile and smooth video adaptation in dynamic HTTP streaming (Conference on Emerging Network Experiment and Technology)
  10. Reinforcement Learning: An Introduction

Their papers