Sharing knowledge is part of our core culture at LinkedIn, whether it’s through hackdays or contributions to open-source projects. We actively participate in academic conferences, such as KDD, SIGIR, RecSys, and CIKM, as well as industry conferences like QCON and Strata.
Beyond sharing our own knowledge, we provide a platform for researchers and practitioners to share their insights with the technical community. We host an Tech Talk series at our Mountain View headquarters that we open up to the general public. Some of our recent speakers include Coursera founders Daphne Koller and Andrew Ng, UC-Berkeley professor Joe Hellerstein, and Hadapt Chief Scientist Daniel Abadi. It’s an excellent opportunity for people with shared professional interests can reconnect with people they know, as well as make new connections. For those who cannot attend, we offer a live stream.
Our next talk will be by Panos Ipeirotis, a professor at NYU and one of the world’s top experts on crowdsourcing. Here is a full description:
Crowdsourcing: Achieving Data Quality with Imperfect Humans
Friday, September 7, 2012 at 3:00 PM
LinkedIn (map)
Crowdsourcing is a great tool to collect data and support machine learning — it is the ultimate form of outsourcing. But crowdsourcing introduces budget and quality challenges that must be addressed to realize its benefits.
In this talk, I will discuss the use of crowdsourcing for building robust machine learning models quickly and under budget constraints. I’ll operate under the realistic assumption that we are processing imperfect labels that reflect random and systematic error on the part of human workers. I will also describe our “beat the machine” system engages humans to improve a machine learning system by discovering cases where the machine fails and fails while confident on being correct. I’ll use classification problems that arise in online advertising.
Finally, I’ll discuss our latest results showing that mice and Mechanical Turk workers are not that different after all.
Panos Ipeirotis is an Associate Professor and George A. Kellner Faculty Fellow at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University. His recent research interests focus on crowdsourcing and on mining user-generated content on the Internet. He received his Ph.D. degree in Computer Science from Columbia University in 2004, with distinction. He has received three “Best Paper” awards (IEEE ICDE 2005, ACM SIGMOD 2006, WWW 2011), two “Best Paper Runner Up” awards (JCDL 2002, ACM KDD 2008), and is also a recipient of a CAREER award from the National Science Foundation.
If you’re in the Bay Area, I encourage you to attend in person — Panos is a great speaker, and it’s also a great opportunity to network with other attendees. If not, then you can follow on the live stream.
The event is free, but please sign up on the event page. See you next week!