31 citations · 83 across the 9 of their papers we have counts for
9 papers
Olmo 3
Team Olmo, :, Allyson Ettinger +66
We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
Md Musfiqur Rahman, Matt Jordan, Murat Kocaoglu
Causal inference from observational data plays critical role in many applications in trustworthy machine learning. While sound and complete algorithms exist to compute causal effec…
Zonotope Domains for Lagrangian Neural Network Verification
Matt Jordan, Jonathan Hayase, Alexandros G. Dimakis +1
Neural network verification aims to provide provable bounds for the output of a neural network for a given input range. Notable prior works in this domain have either generated bou…
Inverse Problems Leveraging Pre-trained Contrastive Representations
Sriram Ravula, Georgios Smyrnis, Matt Jordan +1
We study a new family of inverse problems for recovering representations of corrupted data. We assume access to a pre-trained representation learning network R(x) that operates on…
Provable Lipschitz Certification for Generative Models
Matt Jordan, Alexandros G. Dimakis
We present a scalable technique for upper bounding the Lipschitz constant of generative models. We relate this quantity to the maximal norm over the set of attainable vector-Jacobi…
Quarantines as a Targeted Immunization Strategy
Jessica Hoffmann, Matt Jordan, Constantine Caramanis
In the context of the recent COVID-19 outbreak, quarantine has been used to "flatten the curve" and slow the spread of the disease. In this paper, we show that this is not the only…
Exactly Computing the Local Lipschitz Constant of ReLU Networks
Matt Jordan, Alexandros G. Dimakis
The local Lipschitz constant of a neural network is a useful metric with applications in robustness, generalization, and fairness evaluation. We provide novel analytic results rela…
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
Matt Jordan, Justin Lewis, Alexandros G. Dimakis
We propose a novel method for computing exact pointwise robustness of deep neural networks for all convex norms. Our algorithm, GeoCert, finds the largest ball ce…
Quantifying Perceptual Distortion of Adversarial Examples
Matt Jordan, Naren Manoj, Surbhi Goel +1
Recent work has shown that additive threat models, which only permit the addition of bounded noise to the pixels of an image, are insufficient for fully capturing the space of impe…