Nir Rosenfeld

School of Engineering and Applied Sciences
Harvard University
email: nir.rosenfeld thatsquigglything mail dot huji dot ac dot il

I am a postdoc at Harvard University, where I am affiliated with the
Center for Research on Computation and Society (CRCS), and advised
by Yaron Singer and David Parkes. Prior to that I was a Ph.D. student at
the Hebrew University in Jeruslaem where I was advised by Amir Globerson.
I was also a long-term intern at Microsoft Research in Israel.

My main research interest is to develop machine learning methods for tasks involving
dynamic social and behavioural data, and to apply them in order to gain insight into
social and behavioral processes. I also draw on social dynamics as inspiration for
designing learning and inference algorithms. Lately I've become interested in various
aspects of incoporating humans in the learning process.

List of Publications

Discriminative Learning of Prediction Intervals
Nir Rosenfeld, Yishai Mansour, and Elad Yom-Tov
AISTATS 2018 [pdf] [supp]
Semi-Supervised Learning with Competitive Infection Models
Nir Rosenfeld and Amir Globerson
AISTATS 2018 [pdf] [supp]
Predicting Counterfactuals from Large Historical Data and Small Randomized Trials
Nir Rosenfeld, Yishai Mansour, and Elad Yom-Tov
WWW 2017 [pdf]
We Look Like Our Names: The Manifestation of Name Stereotypes in Facial Appearance
Yonat Zwebner, Anne-Laure Sellier, Nir Rosenfeld, Jacob Goldenberg, and Ruth Mayo
Journal of Personality and Social Psychology (2017) [pdf] [media]
Optimal Tagging with Markov Chain Optimization
Nir Rosenfeld and Amir Globerson
NIPS 2016 [pdf] [supp]
Discriminative Learning of Infection Models
Nir Rosenfeld, Mor Nitzan, and Amir Globerson
WSDM 2016 [pdf] [supp] [code]
Learning Structured Models with the AUC Loss and Its Generalizations
Nir Rosenfeld, Ofer Meshi, Danny Tarlow, and Amir Globerson
AISTATS 2014 [pdf] [supp]
Structured Learning for Link Prediction
M.Sc. thesis, under the supervision of Amir Globerson and Jacob Goldenberg (2013) [pdf]


NIPS yearly collaboration networks