22 citations · 34 across the 11 of their papers we have counts for
11 papers
Workload-Aware Caching for Multi-Agent Systems
Anas Mohamed, Kaizan Haque, Azal Ahmad Khan +3
Multi-agent systems decompose complex tasks into directed acyclic graphs (DAGs) of specialized agent executions, creating natural opportunities for caching intermediate results acr…
Faster Synchronous On-Policy RL via Straggler-Aware Group Sizing
Azal Ahmad Khan, Ammar Ahmed, Zeshan Fayyaz +3
Synchronous reinforcement learning methods such as Group Relative Policy Optimization (GRPO) provide stable and reproducible on-policy training, but they are highly vulnerable to s…
Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts
Ammar Ahmed, Azal Ahmad Khan, Ayaan Ahmad +3
Large reasoning models improve accuracy by producing long reasoning traces, but this inflates latency and cost, motivating inference-time efficiency. We propose Retrieval-of-Though…
Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling
Seyyed Saeid Cheshmi, Azal Ahmad Khan, Xinran Wang +2
Large Language Models (LLMs) are increasingly relied upon for solving complex reasoning tasks in domains such as mathematics, logic, and multi-step question answering. A growing li…
Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering
Anas Mohamed, Azal Ahmad Khan, Xinran Wang +5
Generative AI can now synthesize strikingly realistic images from text, yet output quality remains highly sensitive to how prompts are phrased. Direct Preference Optimization (DPO)…
Safety Aware Task Planning via Large Language Models in Robotics
Azal Ahmad Khan, Michael Andrev, Muhammad Ali Murtaza +5
The integration of large language models (LLMs) into robotic task planning has unlocked better reasoning capabilities for complex, long-horizon workflows. However, ensuring safety…
LADs: Leveraging LLMs for AI-Driven DevOps
Ahmad Faraz Khan, Azal Ahmad Khan, Anas Mohamed +7
Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions la…
Personalized Federated Learning Techniques: Empirical Analysis
Azal Ahmad Khan, Ahmad Faraz Khan, Haider Ali +1
Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal perf…
IP-FL: Incentivized and Personalized Federated Learning
Ahmad Faraz Khan, Xinran Wang, Qi Le +7
Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personaliza…
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra
Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. Ensemble learning combines multiple models to…
Balanced Split: A new train-test data splitting strategy for imbalanced datasets
Azal Ahmad Khan
Classification data sets with skewed class proportions are called imbalanced. Class imbalance is a problem since most machine learning classification algorithms are built with an a…