Search Results for author: Yoni Halpern

Found 10 papers, 3 papers with code

Causally motivated Shortcut Removal Using Auxiliary Labels

1 code implementation13 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.

Causal Inference Disentanglement +1

Measuring Recommender System Effects with Simulated Users

no code implementations12 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.

Collaborative Filtering Recommendation Systems

Fair treatment allocations in social networks

no code implementations1 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.

Fairness

Benefits of Overparameterization in Single-Layer Latent Variable Generative Models

no code implementations25 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).

Variational Inference

Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models

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).

Variational Inference

BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity

no code implementations17 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 Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World

no code implementations22 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.

General Classification

Clinical Tagging with Joint Probabilistic Models

no code implementations2 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.

Anchored Discrete Factor Analysis

no code implementations10 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.

Medical Diagnosis TAG

A Practical Algorithm for Topic Modeling with Provable Guarantees

2 code implementations19 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.

Dimensionality Reduction Topic Models

Cannot find the paper you are looking for? You can Submit a new open access paper.