25 citations · 75 across the 16 of their papers we have counts for
6 papers · 1 filter
VeriX: Towards Verified Explainability of Deep Neural Networks
Min Wu, Haoze Wu, Clark Barrett
We present VeriX (Verified eXplainability), a system for producing optimal robust explanations and generating counterfactuals along decision boundaries of machine learning models.…
Proof-Stitch: Proof Combination for Divide and Conquer SAT Solvers
Abhishek Nair, Saranyu Chattopadhyay, Haoze Wu +2
With the increasing availability of parallel computing power, there is a growing focus on parallelizing algorithms for important automated reasoning problems such as Boolean satisf…
On Optimizing Back-Substitution Methods for Neural Network Verification
Tom Zelazny, Haoze Wu, Clark Barrett +1
With the increasing application of deep learning in mission-critical systems, there is a growing need to obtain formal guarantees about the behaviors of neural networks. Indeed, ma…
Toward Certified Robustness Against Real-World Distribution Shifts
Haoze Wu, Teruhiro Tagomori, Alexander Robey +6
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications…
Efficient Neural Network Analysis with Sum-of-Infeasibilities
Haoze Wu, Aleksandar Zeljić, Guy Katz +1
Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel procedure for analyzing verification queries on neural networks with piecewise-linear activati…
Scalable Verification of GNN-based Job Schedulers
Haoze Wu, Clark Barrett, Mahmood Sharif +2
Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics. Despite their impressive perf…