45 citations · 105 across the 14 of their papers we have counts for
17 papers
GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks
Shivanshu Gupta, Clemens Rosenbaum, Ethan R. Elenberg
In-context Learning (ICL) is the ability of Large Language Models (LLMs) to perform new tasks when conditioned on prompts comprising a few task examples. However, ICL performance c…
Multi-Step Dialogue Workflow Action Prediction
Ramya Ramakrishnan, Ethan R. Elenberg, Hashan Narangodage +1
In task-oriented dialogue, a system often needs to follow a sequence of actions, called a workflow, that complies with a set of guidelines in order to complete a task. In this pape…
On the Effectiveness of Offline RL for Dialogue Response Generation
Paloma Sodhi, Felix Wu, Ethan R. Elenberg +2
A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different w…
Submodular Minimax Optimization: Finding Effective Sets
Loay Mualem, Ethan R. Elenberg, Moran Feldman +1
Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings. In thi…
Domain Private Transformers for Multi-Domain Dialog Systems
Anmol Kabra, Ethan R. Elenberg
Large, general purpose language models have demonstrated impressive performance across many different conversational domains. While multi-domain language models achieve low overall…
CEREAL: Few-Sample Clustering Evaluation
Nihal V. Nayak, Ethan R. Elenberg, Clemens Rosenbaum
Evaluating clustering quality with reliable evaluation metrics like normalized mutual information (NMI) requires labeled data that can be expensive to annotate. We focus on the und…
Achieving Multi-Port Memory Performance on Single-Port Memory with Coding Techniques
Hardik Jain, Matthew Edwards, Ethan Elenberg +2
Many performance critical systems today must rely on performance enhancements, such as multi-port memories, to keep up with the increasing demand of memory-access capacity. However…
Identifying Mislabeled Data using the Area Under the Margin Ranking
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg +1
Not all data in a typical training set help with generalization; some samples can be overly ambiguous or outrightly mislabeled. This paper introduces a new method to identify such…
Metric Learning for Dynamic Text Classification
Jeremy Wohlwend, Ethan R. Elenberg, Samuel Altschul +2
Traditional text classifiers are limited to predicting over a fixed set of labels. However, in many real-world applications the label set is frequently changing. For example, in in…
Importance Weighted Generative Networks
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen +3
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution. However, often we do not have direct…
Scalable Greedy Feature Selection via Weak Submodularity
Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +2
Greedy algorithms are widely used for problems in machine learning such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of e…
On Approximation Guarantees for Greedy Low Rank Optimization
Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +1
We provide new approximation guarantees for greedy low rank matrix estimation under standard assumptions of restricted strong convexity and smoothness. Our novel analysis also unco…
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Ethan R. Elenberg, Alexandros G. Dimakis, Moran Feldman +1
In many machine learning applications, it is important to explain the predictions of a black-box classifier. For example, why does a deep neural network assign an image to a partic…
Restricted Strong Convexity Implies Weak Submodularity
Ethan R. Elenberg, Rajiv Khanna, Alexandros G. Dimakis +1
We connect high-dimensional subset selection and submodular maximization. Our results extend the work of Das and Kempe (2011) from the setting of linear regression to arbitrary obj…
Mining and modeling character networks
Anthony Bonato, David Ryan D'Angelo, Ethan R. Elenberg +2
We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as s…
Distributed Estimation of Graph 4-Profiles
Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich +1
We present a novel distributed algorithm for counting all four-node induced subgraphs in a big graph. These counts, called the -profile, describe a graph's connectivity properti…
Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs
Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich +1
We study the problem of approximating the -profile of a large graph. -profiles are generalizations of triangle counts that specify the number of times a small graph appears a…