17 citations · 33 across the 7 of their papers we have counts for
6 papers · 1 filter
Hill Sampling for Test-Time Scaling: A Simple and Better Alternative to Repeated Sampling, Evolution, and Training
Jacob Beck, Philip V. Ogren, Ari Kobren
Large language models (LLMs) can improve solutions to verifiable scientific and algorithmic problems by spending additional computation at test time. Recent systems achieve strong…
Leveraging Extracted Model Adversaries for Improved Black Box Attacks
Naveen Jafer Nizar, Ari Kobren
We present a method for adversarial input generation against black box models for reading comprehension based question answering. Our approach is composed of two steps. First, we a…
Scalable Hierarchical Clustering with Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy +2
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. Th…
Optimal Transport-based Alignment of Learned Character Representations for String Similarity
Derek Tam, Nicholas Monath, Ari Kobren +3
String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE --a learned model for computing the similarity of two strings.…
Supervised Hierarchical Clustering with Exponential Linkage
Nishant Yadav, Ari Kobren, Nicholas Monath +1
In supervised clustering, standard techniques for learning a pairwise dissimilarity function often suffer from a discrepancy between the training and clustering objectives, leading…
An Online Hierarchical Algorithm for Extreme Clustering
Ari Kobren, Nicholas Monath, Akshay Krishnamurthy +1
Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for…