4.3k citations · 8.6k across the 159 of their papers we have counts for
17 papers · 1 filter
Deep Archimedean Copulas
Chun Kai Ling, Fei Fang, J. Zico Kolter
A central problem in machine learning and statistics is to model joint densities of random variables from data. Copulas are joint cumulative distribution functions with uniform mar…
Community detection using fast low-cardinality semidefinite programming
Po-Wei Wang, J. Zico Kolter
Modularity maximization has been a fundamental tool for understanding the community structure of a network, but the underlying optimization problem is nonconvex and NP-hard to solv…
Efficient semidefinite-programming-based inference for binary and multi-class MRFs
Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter
Probabilistic inference in pairwise Markov Random Fields (MRFs), i.e. computing the partition function or computing a MAP estimate of the variables, is a foundational problem in pr…
Enforcing robust control guarantees within neural network policies
Priya L. Donti, Melrose Roderick, Mahyar Fazlyab +1
When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rig…
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks
Anit Kumar Sahu, Satya Narayan Shukla, J. Zico Kolter
We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations. Alt…
Poisoned classifiers are not only backdoored, they are fundamentally broken
Mingjie Sun, Siddhant Agarwal, J. Zico Kolter
Under a commonly-studied backdoor poisoning attack against classification models, an attacker adds a small trigger to a subset of the training data, such that the presence of this…
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Filipe de Avila Belbute-Peres, Thomas D. Economon, J. Zico Kolter
Solving large complex partial differential equations (PDEs), such as those that arise in computational fluid dynamics (CFD), is a computationally expensive process. This has motiva…
A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Learning perturbation sets for robust machine learning
Eric Wong, J. Zico Kolter
Although much progress has been made towards robust deep learning, a significant gap in robustness remains between real-world perturbations and more narrowly defined sets typically…
Provably Safe PAC-MDP Exploration Using Analogies
Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter
A key challenge in applying reinforcement learning to safety-critical domains is understanding how to balance exploration (needed to attain good performance on the task) with safet…
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications
Eric Wong, Tim Schneider, Joerg Schmitt +2
Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have…
Multiscale Deep Equilibrium Models
Shaojie Bai, Vladlen Koltun, J. Zico Kolter
We propose a new class of implicit networks, the multiscale deep equilibrium model (MDEQ), suited to large-scale and highly hierarchical pattern recognition domains. An MDEQ direct…
Monotone operator equilibrium networks
Ezra Winston, J. Zico Kolter
Implicit-depth models such as Deep Equilibrium Networks have recently been shown to match or exceed the performance of traditional deep networks while being much more memory effici…
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman, Mingjie Sun, Greg Yang +2
We present a method for provably defending any pretrained image classifier against adversarial attacks. This method, for instance, allows public vision API providers and u…
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…
Learning Stable Deep Dynamics Models
Gaurav Manek, J. Zico Kolter
Deep networks are commonly used to model dynamical systems, predicting how the state of a system will evolve over time (either autonomously or in response to control inputs). Despi…
Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, J. Zico Kolter
Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversari…