12 citations · 24 across the 18 of their papers we have counts for
21 papers
Compositional Self-Improvement: Promises and Pitfalls
Changho Shin, Daiwei Chen, Brenden Lake +2
Although self-improvement is becoming increasingly important as tasks become more complex, models struggle to generate reliable supervision for harder, out-of-distribution instance…
GRASP: Graph Agentic Search over Propositions for Multi-hop Question Answering
Stockton Jenkins, Ramya Korlakai Vinayak, Junjie Hu
Agentic retrieval improves multi-hop question answering by giving language models autonomy to iteratively gather evidence. Recent work augments these systems with knowledge graphs…
Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation
Daiwei Chen, Zhoutong Fu, Chengming Jiang +12
Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in generative recommendation. The standar…
Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights
Yi Chen, Daiwei Chen, Sukrut Madhav Chikodikar +2
Large language models (LLMs) frequently hallucinate, limiting their reliability in knowledge-intensive applications. Retrieval-augmented generation (RAG) and conformal factuality h…
Almost Asymptotically Optimal Active Clustering Through Pairwise Observations
Rachel S. Y. Teo, P. N. Karthik, Ramya Korlakai Vinayak +1
We propose a new analysis framework for clustering items into an unknown number of distinct groups using noisy and actively collected responses. At each time step, an agent…
Agentic Very Long Video Understanding
Aniket Rege, Arka Sadhu, Yuliang Li +5
The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual understanding, one that goes beyond sho…
Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measure
Zhi Wang, Chicheng Zhang, Ramya Korlakai Vinayak
In lifelong learning, a learner faces a sequence of tasks with shared structure and aims to identify and leverage it to accelerate learning. We study the setting where such structu…
Metric Learning in an RKHS
Gokcan Tatli, Yi Chen, Blake Mason +2
Metric learning from a set of triplet comparisons in the form of "Do you think item h is more similar to item i or item j?", indicating similarity and differences between items, pl…
CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems
Aniket Rege, Zinnia Nie, Mahesh Ramesh +5
Popular text-to-image (T2I) systems are trained on web-scraped data, which is heavily Amero and Euro-centric, underrepresenting the cultures of the Global South. To analyze these b…
ASAT: Adaptive Scoring and Thresholding with Human Feedback for Robust Out-of-Distribution Detection
Daisuke Yamada, Harit Vishwakarma, Ramya Korlakai Vinayak
Machine Learning (ML) models are trained on in-distribution (ID) data but often encounter out-of-distribution (OOD) inputs during deployment---posing serious risks in safety-critic…
PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
Daiwei Chen, Yi Chen, Aniket Rege +1
Large foundation models pretrained on raw web-scale data are not readily deployable without additional step of extensive alignment to human preferences. Such alignment is typically…
Taming False Positives in Out-of-Distribution Detection with Human Feedback
Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
Robustness to out-of-distribution (OOD) samples is crucial for safely deploying machine learning models in the open world. Recent works have focused on designing scoring functions…
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling
Harit Vishwakarma, Reid, Chen +4
Auto-labeling is an important family of techniques that produce labeled training sets with minimum manual labeling. A prominent variant, threshold-based auto-labeling (TBAL), works…
Metric Learning from Limited Pairwise Preference Comparisons
Zhi Wang, Geelon So, Ramya Korlakai Vinayak
We study metric learning from preference comparisons under the ideal point model, in which a user prefers an item over another if it is closer to their latent ideal item. These ite…
Limitations of Face Image Generation
Harrison Rosenberg, Shimaa Ahmed, Guruprasad V Ramesh +2
Text-to-image diffusion models have achieved widespread popularity due to their unprecedented image generation capability. In particular, their ability to synthesize and modify hum…
Promises and Pitfalls of Threshold-based Auto-labeling
Harit Vishwakarma, Heguang Lin, Frederic Sala +1
Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data obtai…
One for All: Simultaneous Metric and Preference Learning over Multiple Users
Gregory Canal, Blake Mason, Ramya Korlakai Vinayak +1
This paper investigates simultaneous preference and metric learning from a crowd of respondents. A set of items represented by -dimensional feature vectors and paired comparison…
Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
Zhen Miao, Weihao Kong, Ramya Korlakai Vinayak +2
This paper investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions. Build…
Estimating the number and effect sizes of non-null hypotheses
Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson
We study the problem of estimating the distribution of effect sizes (the mean of the test statistic under the alternate hypothesis) in a multiple testing setting. Knowing this dist…
Optimal Estimation of Change in a Population of Parameters
Ramya Korlakai Vinayak, Weihao Kong, Sham M. Kakade
Paired estimation of change in parameters of interest over a population plays a central role in several application domains including those in the social sciences, epidemiology, me…
Maximum Likelihood Estimation for Learning Populations of Parameters
Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant +1
Consider a setting with independent individuals, each with an unknown parameter, drawn from some unknown distribution . After observing the outcomes o…