no code implementations • 23 Aug 2023 • Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi
In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback.
1 code implementation • 13 May 2021 • Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D'Amour
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.
no code implementations • 12 Jan 2021 • Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel
Using this simulation framework, we can (a) isolate the effect of the recommender system from the user preferences, and (b) examine how the system performs not just on average for an "average user" but also the extreme experiences under atypical user behavior.
no code implementations • 1 Nov 2019 • James Atwood, Hansa Srinivasan, Yoni Halpern, D. Sculley
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them.
no code implementations • 25 Sep 2019 • Rares-Darius Buhai, Andrej Risteski, Yoni Halpern, David Sontag
One of the most surprising and exciting discoveries in supervising learning was the benefit of overparameterization (i. e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i. e. generalization).
1 code implementation • ICML 2020 • Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag
One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i. e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i. e. generalization).
no code implementations • 17 Dec 2018 • Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers.
no code implementations • 22 Nov 2017 • Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, D. Sculley
Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales.
no code implementations • 2 Aug 2016 • Yoni Halpern, Steven Horng, David Sontag
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record.
no code implementations • 10 Nov 2015 • Yoni Halpern, Steven Horng, David Sontag
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables.
2 code implementations • 19 Dec 2012 • Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu
Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora.