13 papers · 1 filter
Understanding Machine Unlearning Through the Lens of Mode Connectivity
Jiali Cheng, Hadi Amiri
Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch. Despite recent progress, the loss landscape and optimization geome…
Fair Cognitive Impairment Detection Through Unlearning
William Nguyen, Jiali Cheng, Hadi Amiri
Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities. MCI detection from spontaneous speech is pr…
A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning
Jiali Cheng, Ziheng Chen, Chirag Agarwal +1
Machine unlearning is becoming essential for building trustworthy and compliant language models. Yet unlearning success varies considerably across individual samples: some are reli…
Do Students Debias Like Teachers? On the Distillability of Bias Mitigation Methods
Jiali Cheng, Chirag Agarwal, Hadi Amiri
Knowledge distillation (KD) is an effective method for model compression and transferring knowledge between models. However, its effect on model's robustness against spurious corre…
WebATLAS: An LLM Agent with Experience-Driven Memory and Action Simulation
Jiali Cheng, Anjishnu Kumar, Roshan Lal +7
Large Language Model (LLM) web agents often struggle with long-horizon web navigation and web task completion in new websites, producing inefficient action sequences unless fine-tu…
FUTURE: Flexible Unlearning for Tree Ensemble
Ziheng Chen, Jin Huang, Jiali Cheng +5
Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finan…
Speech Unlearning
Jiali Cheng, Hadi Amiri
We introduce machine unlearning for speech tasks, a novel and underexplored research problem that aims to efficiently and effectively remove the influence of specific data from tra…
Understanding Machine Unlearning Through the Lens of Mode Connectivity
Jiali Cheng, Hadi Amiri
Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch. Despite recent progress, the loss landscape and optimization geome…
FairFlow: Mitigating Dataset Biases through Undecided Learning
Jiali Cheng, Hadi Amiri
Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing fr…
FROG: Fair Removal on Graphs
Ziheng Chen, Jiali Cheng, Hadi Amiri +5
With growing emphasis on privacy regulations, machine unlearning has become increasingly critical in real-world applications such as social networks and recommender systems, many o…
Tool Unlearning for Tool-Augmented LLMs
Jiali Cheng, Hadi Amiri
Tool-augmented large language models (LLMs) are often trained on datasets of query-response pairs, which embed the ability to use tools or APIs directly into the parametric knowled…
CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech
Jiali Cheng, Mohamed Elgaar, Nidhi Vakil +1
Mild Cognitive Impairment (MCI) is a medical condition characterized by noticeable declines in memory and cognitive abilities, potentially affecting individual's daily activities.…
MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning
Jiali Cheng, Hadi Amiri
Recent advancements in Machine Unlearning (MU) have introduced solutions to selectively remove certain training samples, such as those with outdated or sensitive information, from…