activity
20192022
most citedCertified Adversarial Robustness via Randomized Smoothing

617 citations · 617 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2022

APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations

Elan Rosenfeld, Preetum Nakkiran, Hadi Pouransari +2

Recent advances in learning aligned multimodal representations have been primarily driven by training large neural networks on massive, noisy paired-modality datasets. In this work…

cs.LG2021

Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation

Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar +1

Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models. It has been empirically observed that the choice of the nois…

cs.LG2020

The Risks of Invariant Risk Minimization

Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski

Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the tra…

cs.LG2020

Self-Reflective Variational Autoencoder

Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski

The Variational Autoencoder (VAE) is a powerful framework for learning probabilistic latent variable generative models. However, typical assumptions on the approximate posterior di…

cs.LG2020

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing

Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar +1

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 classifi…

cs.CR2019

Human-Usable Password Schemas: Beyond Information-Theoretic Security

Elan Rosenfeld, Santosh Vempala, Manuel Blum

Password users frequently employ passwords that are too simple, or they just reuse passwords for multiple websites. A common complaint is that utilizing secure passwords is too dif…