31 citations · 71 across the 12 of their papers we have counts for
12 papers
DIAL-SUMMER: A Structured Evaluation Framework of Hierarchical Errors in Dialogue Summaries
Sahana Ramnath, Nima Chitsazan, Mingyang Zhou +8
Dialogues are a predominant mode of communication for humans, and it is immensely helpful to have automatically generated summaries of them (e.g., to revise key points discussed in…
LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
Akshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das +4
Large language models (LLMs) have revolutionized various domains, yet their utility comes with significant challenges related to outdated or problematic knowledge embedded during p…
Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT
Akshaj Kumar Veldanda, Fabian Grob, Shailja Thakur +4
Large Language Models (LLMs) such as GPT-3.5, Bard, and Claude exhibit applicability across numerous tasks. One domain of interest is their use in algorithmic hiring, specifically…
Application of BadNets in Spam Filters
Swagnik Roychoudhury, Akshaj Kumar Veldanda
Spam filters are a crucial component of modern email systems, as they help to protect users from unwanted and potentially harmful emails. However, the effectiveness of these filter…
Path Planning Under Uncertainty to Localize mmWave Sources
Kai Pfeiffer, Yuze Jia, Mingsheng Yin +8
In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimat…
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta +2
Fair machine learning methods seek to train models that balance model performance across demographic subgroups defined over sensitive attributes like race and gender. Although sens…
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen +3
The success of DNNs is driven by the counter-intuitive ability of over-parameterized networks to generalize, even when they perfectly fit the training data. In practice, test error…
Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification
Mingsheng Yin, Akshaj Veldanda, Amee Trivedi +7
The millimeter wave (mmWave) bands have attracted considerable attention for high precision localization applications due to the ability to capture high angular and temporal resolu…
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
Hao Fu, Akshaj Kumar Veldanda, Prashanth Krishnamurthy +2
This paper proposes a new defense against neural network backdooring attacks that are maliciously trained to mispredict in the presence of attacker-chosen triggers. Our defense is…
On Evaluating Neural Network Backdoor Defenses
Akshaj Veldanda, Siddharth Garg
Deep neural networks (DNNs) demonstrate superior performance in various fields, including scrutiny and security. However, recent studies have shown that DNNs are vulnerable to back…
CryptoNAS: Private Inference on a ReLU Budget
Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen +1
Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to p…
NNoculation: Catching BadNets in the Wild
Akshaj Kumar Veldanda, Kang Liu, Benjamin Tan +5
This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, repairs a BadNet both pre-deployment and online in response to backdo…