Search Results for author: Elan Rosenfeld

Found 10 papers, 1 papers with code

Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation

no code implementations ICLR 2022 Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski

Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models.

Deep Attentive Variational Inference

no code implementations ICLR 2022 Ifigeneia Apostolopoulou, Ian Char, Elan Rosenfeld, Artur Dubrawski

Moreover, the architecture for this class of models favors local interactions among the latent variables between neighboring layers when designing the conditioning factors of the involved distributions.

Variational Inference

Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments

no code implementations18 Jun 2021 Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski

Domain generalization aims at performing well on unseen test environments with data from a limited number of training environments.

Domain Generalization

An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization

no code implementations25 Feb 2021 Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski

A popular assumption for out-of-distribution generalization is that the training data comprises sub-datasets, each drawn from a distinct distribution; the goal is then to "interpolate" these distributions and "extrapolate" beyond them -- this objective is broadly known as domain generalization.

Domain Generalization online learning +1

The Risks of Invariant Risk Minimization

no code implementations ICLR 2021 Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski

We furthermore present the very first results in the non-linear regime: we demonstrate that IRM can fail catastrophically unless the test data are sufficiently similar to the training distribution--this is precisely the issue that it was intended to solve.

Out-of-Distribution Generalization

Self-Reflective Variational Autoencoder

no code implementations10 Jul 2020 Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski

The Variational Autoencoder (VAE) is a powerful framework for learning probabilistic latent variable generative models.

Variational Inference

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing

no code implementations ICML 2020 Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter

Machine learning algorithms are known to be susceptible to data poisoning attacks, where an adversary manipulates the training data to degrade performance of the resulting classifier.

Data Poisoning General Classification +1

Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing

no code implementations25 Sep 2019 Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter

This paper considers label-flipping attacks, a type of data poisoning attack where an adversary relabels a small number of examples in a training set in order to degrade the performance of the resulting classifier.

Data Poisoning

Certified Adversarial Robustness via Randomized Smoothing

7 code implementations8 Feb 2019 Jeremy M Cohen, Elan Rosenfeld, J. Zico Kolter

We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\ell_2$ norm.

Adversarial Defense Adversarial Robustness +1

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