Author name cluster

Yonatan Halpern

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5 papers

2 author rows

ICML Conference 2024 Conference Paper

FRAPPÉ: A Group Fairness Framework for Post-Processing Everything

Alexandru Tifrea
Preethi Lahoti
Ben Packer
Yonatan Halpern
Ahmad Beirami
Flavien Prost

Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model. In these situations, post-processing is a viable alternative. However, current methods are tailored to specific problem settings and fairness definitions and hence, are not as broadly applicable as in-processing. In this work, we propose a framework that turns any regularized in-processing method into a post-processing approach. This procedure prescribes a way to obtain post-processing techniques for a much broader range of problem settings than the prior post-processing literature. We show theoretically and through extensive experiments that our framework preserves the good fairness-error trade-offs achieved with in-processing and can improve over the effectiveness of prior post-processing methods. Finally, we demonstrate several advantages of a modular mitigation strategy that disentangles the training of the prediction model from the fairness mitigation, including better performance on tasks with partial group labels.

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ICML Conference 2020 Conference Paper

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

Rares-Darius Buhai
Yonatan Halpern
Yoon Kim
Andrej Risteski
David A. 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). In contrast, unsupervised settings have been under-explored, despite the fact that it was observed that overparameterization can be helpful as early as Dasgupta & Schulman (2007). We perform an empirical study of different aspects of overparameterization in unsupervised learning of latent variable models via synthetic and semi-synthetic experiments. We discuss benefits to different metrics of success (recovering the parameters of the ground-truth model, held-out log-likelihood), sensitivity to variations of the training algorithm, and behavior as the amount of overparameterization increases. We find that across a variety of models (noisy-OR networks, sparse coding, probabilistic context-free grammars) and training algorithms (variational inference, alternating minimization, expectation-maximization), overparameterization can significantly increase the number of ground truth latent variables recovered.

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ICML Conference 2013 Conference Paper

A Practical Algorithm for Topic Modeling with Provable Guarantees

Sanjeev Arora
Rong Ge 0001
Yonatan Halpern
David M. Mimno
Ankur Moitra
David A. Sontag
Yichen Wu
Michael Zhu

Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model learning have been based on a maximum likelihood objective. Efficient algorithms exist that attempt to approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for learning topic models that is both provable and practical. The algorithm produces results comparable to the best MCMC implementations while running orders of magnitude faster.

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NeurIPS Conference 2013 Conference Paper

Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests

Yacine Jernite
Yonatan Halpern
David Sontag

We give a polynomial-time algorithm for provably learning the structure and parameters of bipartite noisy-or Bayesian networks of binary variables where the top layer is completely hidden. Unsupervised learning of these models is a form of discrete factor analysis, enabling the discovery of hidden variables and their causal relationships with observed data. We obtain an efficient learning algorithm for a family of Bayesian networks that we call quartet-learnable, meaning that every latent variable has four children that do not have any other parents in common. We show that the existence of such a quartet allows us to uniquely identify each latent variable and to learn all parameters involving that latent variable. Underlying our algorithm are two new techniques for structure learning: a quartet test to determine whether a set of binary variables are singly coupled, and a conditional mutual information test that we use to learn parameters. We also show how to subtract already learned latent variables from the model to create new singly-coupled quartets, which substantially expands the class of structures that we can learn. Finally, we give a proof of the polynomial sample complexity of our learning algorithm, and experimentally compare it to variational EM.

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UAI Conference 2013 Conference Paper

Unsupervised Learning of Noisy-Or Bayesian Networks

Yonatan Halpern
David A. Sontag

This paper considers the problem of learning the parameters in Bayesian networks of discrete variables with known structure and hidden variables. Previous approaches in these settings typically use expectation maximization; when the network has high treewidth, the required expectations might be approximated using Monte Carlo or variational methods. We show how to avoid inference altogether during learning by giving a polynomial-time algorithm based on the method-of-moments, building upon recent work on learning discrete-valued mixture models. In particular, we show how to learn the parameters for a family of bipartite noisy-or Bayesian networks. In our experimental results, we demonstrate an application of our algorithm to learning QMR-DT, a large Bayesian network used for medical diagnosis. We show that it is possible to fully learn the parameters of QMR-DT even when only the findings are observed in the training data (ground truth diseases unknown).

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