1 citations · 1 across the 8 of their papers we have counts for
9 papers
You Can't Have It Both Ways: Concept Entanglement Limits Diffusion Model Unlearning
Yian Wang, Ali Ebrahimpour-Boroojeny, Hari Sundaram +1
Concept unlearning in text-to-image diffusion models aims to suppress a target concept (e.g., \texttt{horse}) while preserving related but distinct content (e.g., \texttt{donkey}),…
State Contamination in Memory-Augmented LLM Agents
Yian Wang, Agam Goyal, Yuen Chen +1
LLM agents increasingly rely on persistent state, including transcripts, summaries, retrieved context, and memory buffers, to support long-horizon interaction. This makes safety de…
DIVEBATCH: Accelerating Model Training Through Gradient-Diversity Aware Batch Size Adaptation
Yuen Chen, Yian Wang, Hari Sundaram
The goal of this paper is to accelerate the training of machine learning models, a critical challenge since the training of large-scale deep neural models can be computationally ex…
The Chilling: Identifying Strategic Antisocial Behavior Online and Examining the Impact on Journalists
Yian Wang, Mukhilshankar Umashankar, Eshwar Chandrasekharan +1
On social platforms like Twitter, strategic targeted attacks are becoming increasingly common, especially against vulnerable groups such as female journalists. Two key challenges i…
On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
Ali Ebrahimpour-Boroojeny, Yian Wang, Hari Sundaram
In this paper, we reveal a significant shortcoming in class unlearning evaluations: overlooking the underlying class geometry can cause information leakage about the forgotten clas…
Breaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoders
Agam Goyal, Vedant Rathi, William Yeh +3
Large language models (LLMs) are now ubiquitous in user-facing applications, yet they still generate undesirable toxic outputs, including profanity, vulgarity, and derogatory remar…
AMUN: Adversarial Machine UNlearning
Ali Ebrahimpour-Boroojeny, Hari Sundaram, Varun Chandrasekaran
Machine unlearning, where users can request the deletion of a forget dataset, is becoming increasingly important because of numerous privacy regulations. Initial works on ``exact''…
LOTOS: Layer-wise Orthogonalization for Training Robust Ensembles
Ali Ebrahimpour-Boroojeny, Hari Sundaram, Varun Chandrasekaran
Transferability of adversarial examples is a well-known property that endangers all classification models, even those that are only accessible through black-box queries. Prior work…
Spectrum Extraction and Clipping for Implicitly Linear Layers
Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram
We show the effectiveness of automatic differentiation in efficiently and correctly computing and controlling the spectrum of implicitly linear operators, a rich family of layer ty…