2 citations · 5 across the 13 of their papers we have counts for
14 papers
Scaling of Capability and Efficiency at Inference Time in Large Reasoning Models
Moritz Laber, Zohair Shafi, Germans Savcisens +6
Capability and efficiency are two key dimensions of reasoning in large language models (LLMs). Capability refers to the ability to solve a given problem correctly, whereas efficien…
Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors
Omair Shafi Ahmed, Zohair Shafi
Progress in streaming, edge-level graph anomaly detection (GAD) has been marked by increasingly elaborate architectures, from count-min-sketch chi square tests to memory-augmented…
DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights
Saumya Gupta, Scott Biggs, Moritz Laber +3
Building efficient and effective generative models for neural network weights has been a research focus of significant interest that faces challenges posed by the high-dimensional…
FORGE: Foundational Optimization Representations from Graph Embeddings
Zohair Shafi, Serdar Kadioglu
Combinatorial optimization problems are ubiquitous in science and engineering. Still, learning-based approaches to accelerate combinatorial optimization often require solving a lar…
REGE: A Method for Incorporating Uncertainty in Graph Embeddings
Zohair Shafi, Germans Savcisens, Tina Eliassi-Rad
Machine learning models for graphs in real-world applications are prone to two primary types of uncertainty: (1) those that arise from incomplete and noisy data and (2) those that…
Generating Human Understandable Explanations for Node Embeddings
Zohair Shafi, Ayan Chatterjee, Tina Eliassi-Rad
Node embedding algorithms produce low-dimensional latent representations of nodes in a graph. These embeddings are often used for downstream tasks, such as node classification and…
GRASP: Accelerating Shortest Path Attacks via Graph Attention
Zohair Shafi, Benjamin A. Miller, Ayan Chatterjee +2
Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical combinatorial optimization algorithms. ML-based speed ups that aim to learn in an e…
Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks
Zohair Shafi, Benjamin A. Miller, Tina Eliassi-Rad +1
Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems. We investigate the Set Cover Problem (SCP) and propose Graph-SC…
Defense Against Shortest Path Attacks
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Identifying shortest paths between nodes in a network is an important task in many applications. Recent work has shown that a malicious actor can manipulate a graph to make traffic…
Attacking Shortest Paths by Cutting Edges
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Identifying shortest paths between nodes in a network is a common graph analysis problem that is important for many applications involving routing of resources. An adversary that c…
AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands
Ayan Chatterjee, Robin Walters, Zohair Shafi +7
Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identificat…
Optimal Edge Weight Perturbations to Attack Shortest Paths
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Finding shortest paths in a given network (e.g., a computer network or a road network) is a well-studied task with many applications. We consider this task under the presence of an…
PATHATTACK: Attacking Shortest Paths in Complex Networks
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inf…
RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity
David Liu, Zohair Shafi, William Fleisher +2
We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspir…