68 citations · 100 across the 20 of their papers we have counts for
20 papers
Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
Shelly Bensal, Axel Magnuson, Aparna Balagopalan +1
Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time. We show they also make models less correct by amplifying sycophancy, wherein models p…
The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications
Zhenyu Zhao, Aparna Balagopalan, Adi Agrawal +3
Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode that LLMs frequently displa…
Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments
Jennah Gosciak, Aparna Balagopalan, Derek Ouyang +3
Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because d…
What's in a Query: Polarity-Aware Distribution-Based Fair Ranking
Aparna Balagopalan, Kai Wang, Olawale Salaudeen +2
Machine learning-driven rankings, where individuals (or items) are ranked in response to a query, mediate search exposure or attention in a variety of safety-critical settings. Thu…
LEMoN: Label Error Detection using Multimodal Neighbors
Haoran Zhang, Aparna Balagopalan, Nassim Oufattole +4
Large repositories of image-caption pairs are essential for the development of vision-language models. However, these datasets are often extracted from noisy data scraped from the…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Event-Based Contrastive Learning for Medical Time Series
Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott +4
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse o…
The Role of Relevance in Fair Ranking
Aparna Balagopalan, Abigail Z. Jacobs, Asia Biega
Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms,…
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh +3
Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc exp…
Quantifying the Task-Specific Information in Text-Based Classifications
Zining Zhu, Aparna Balagopalan, Marzyeh Ghassemi +1
Recently, neural natural language models have attained state-of-the-art performance on a wide variety of tasks, but the high performance can result from superficial, surface-level…
Comparing Acoustic-based Approaches for Alzheimer's Disease Detection
Aparna Balagopalan, Jekaterina Novikova
Robust strategies for Alzheimer's disease (AD) detection are important, given the high prevalence of AD. In this paper, we study the performance and generalizability of three appro…
Augmenting BERT Carefully with Underrepresented Linguistic Features
Aparna Balagopalan, Jekaterina Novikova
Fine-tuned Bidirectional Encoder Representations from Transformers (BERT)-based sequence classification models have proven to be effective for detecting Alzheimer's Disease (AD) fr…
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach
Benjamin Eyre, Aparna Balagopalan, Jekaterina Novikova
Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains…
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
Aparna Balagopalan, Benjamin Eyre, Frank Rudzicz +1
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly…
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott +3
Multi-language speech datasets are scarce and often have small sample sizes in the medical domain. Robust transfer of linguistic features across languages could improve rates of ea…
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
Jekaterina Novikova, Aparna Balagopalan, Ksenia Shkaruta +1
Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vuln…
Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others
Aparna Balagopalan, Ksenia Shkaruta, Jekaterina Novikova
Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, litt…
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz +1
Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, mod…
ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs
Aparna Balagopalan, Satya Gorti, Mathieu Ravaut +1
Generative Adversarial Networks (GANs) have seen steep ascension to the peak of ML research zeitgeist in recent years. Mostly catalyzed by its success in the domain of image genera…