12 citations · 35 across the 23 of their papers we have counts for
26 papers
Personalized Face Privacy Protection From a Single Image
Zachary Yahn, Fatih Ilhan, Tiansheng Huang +5
Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via unauthorized use of facial r…
Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why
Mohammadreza Armandpour, Fatih Ilhan, David Harrison +6
On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this signal is beneficial and under whi…
Attention-aware Inference Optimizations for Large Vision-Language Models with Memory-efficient Decoding
Fatih Ilhan, Gaowen Liu, Ramana Rao Kompella +5
Large Vision-Language Models (VLMs) have achieved remarkable success in multi-modal reasoning, but their inference time efficiency remains a significant challenge due to the memory…
A Multi-Agent Perception-Action Alliance for Efficient Long Video Reasoning
Yichang Xu, Gaowen Liu, Ramana Rao Kompella +6
This paper presents a multi-agent perception-action exploration alliance, dubbed A4VL, for efficient long-video reasoning. A4VL operates in a multi-round perception-action explorat…
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
Fatih Ilhan, Selim Furkan Tekin, Tiansheng Huang +6
Fine-tuning pre-trained large language models (LLMs) has become a common practice for personalized natural language understanding (NLU) applications on downstream tasks and domain-…
Adversarial Attention Perturbations for Large Object Detection Transformers
Zachary Yahn, Selim Furkan Tekin, Fatih Ilhan +5
Adversarial perturbations are useful tools for exposing vulnerabilities in neural networks. Existing adversarial perturbation methods for object detection are either limited to att…
A Neurosymbolic Agent System for Compositional Visual Reasoning
Yichang Xu, Gaowen Liu, Ramana Rao Kompella +5
The advancement in large language models (LLMs) and large vision models has fueled the rapid progress in multi-modal vision-language reasoning capabilities. However, existing visio…
Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
Tiansheng Huang, Sihao Hu, Fatih Ilhan +4
Safety alignment is an important procedure before the official deployment of a Large Language Model (LLM). While safety alignment has been extensively studied for LLM, there is sti…
Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents
Selim Furkan Tekin, Fatih Ilhan, Gaowen Liu +2
The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive frameworks for dynamically changing env…
Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation
Tiansheng Huang, Sihao Hu, Fatih Ilhan +2
Recent research shows that Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks -- models lose their safety alignment ability after fine-tuning on a few harmf…
H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs
Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang +4
The alignment of pre-trained LLMs continues to draw significant attention from both industry and academia, aiming to ensure responses that are helpful, harmless, and honest. Howeve…
LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity
Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang +2
Combining large language models during training or at inference time has shown substantial performance gain over component LLMs. This paper presents LLM-TOPLA, a diversity-optimize…
Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
Tiansheng Huang, Sihao Hu, Fatih Ilhan +2
Recent research demonstrates that the nascent fine-tuning-as-a-service business model exposes serious safety concerns: fine-tuning with a few harmful data uploaded from the users c…
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
Tiansheng Huang, Sihao Hu, Fatih Ilhan +2
Harmful fine-tuning attack poses serious safety concerns for large language models' fine-tuning-as-a-service. While existing defenses have been proposed to mitigate the issue, thei…
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
Tiansheng Huang, Sihao Hu, Fatih Ilhan +2
Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we…
Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning
Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang +4
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot m…
A Survey on Large Language Model-Based Game Agents
Sihao Hu, Tiansheng Huang, Gaowen Liu +6
Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity. As such, game agents offer a valuable testbed for exploring capabilities…
Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives
Sihao Hu, Tiansheng Huang, Fatih İlhan +2
This paper provides a systematic analysis of the opportunities, challenges, and potential solutions of harnessing Large Language Models (LLMs) such as GPT-4 to dig out vulnerabilit…
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
Ka-Ho Chow, Ling Liu, Wenqi Wei +2
Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clie…
Adaptive Deep Neural Network Inference Optimization with EENet
Fatih Ilhan, Ka-Ho Chow, Sihao Hu +9
Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test exampl…
Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
Fatih Ilhan, Oguzhan Karaahmetoglu, Ismail Balaban +1
We investigate nonlinear regression for nonstationary sequential data. In most real-life applications such as business domains including finance, retail, energy and economy, timese…
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks
Oguzhan Karaahmetoglu, Fatih Ilhan, Ismail Balaban +1
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values. Our algorithm is fully unsuper…
Achieving Online Regression Performance of LSTMs with Simple RNNs
N. Mert Vural, Fatih Ilhan, Selim F. Yilmaz +2
Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, Long-Short-Term-Memory N…
Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels
Fatih Ilhan, Suleyman Serdar Kozat
We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime pred…