10 citations · 19 across the 14 of their papers we have counts for
14 papers
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…
Experimental Designs for Heteroskedastic Variance
Justin Weltz, Tanner Fiez, Alexander Volfovsky +4
Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set…
Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering
Tavish McDonald, Brian Tsan, Amar Saini +5
Researchers produce thousands of scholarly documents containing valuable technical knowledge. The community faces the laborious task of reading these documents to identify, extract…
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…
A Blessing of Dimensionality in Membership Inference through Regularization
Jasper Tan, Daniel LeJeune, Blake Mason +2
Is overparameterization a privacy liability? In this work, we study the effect that the number of parameters has on a classifier's vulnerability to membership inference attacks. We…
An Experimental Design Approach for Regret Minimization in Logistic Bandits
Blake Mason, Kwang-Sung Jun, Lalit Jain
In this work we consider the problem of regret minimization for logistic bandits. The main challenge of logistic bandits is reducing the dependence on a potentially large problem d…
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
Jasper Tan, Blake Mason, Hamid Javadi +1
A surprising phenomenon in modern machine learning is the ability of a highly overparameterized model to generalize well (small error on the test data) even when it is trained to m…
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
Julian Katz-Samuels, Blake Mason, Kevin Jamieson +1
We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begi…
Nearly Optimal Algorithms for Level Set Estimation
Blake Mason, Romain Camilleri, Subhojyoti Mukherjee +3
The level set estimation problem seeks to find all points in a domain where the value of an unknown function exceeds a threshold .…
NFT-K: Non-Fungible Tangent Kernels
Sina Alemohammad, Hossein Babaei, CJ Barberan +4
Deep neural networks have become essential for numerous applications due to their strong empirical performance such as vision, RL, and classification. Unfortunately, these networks…
Nearest Neighbor Search Under Uncertainty
Blake Mason, Ardhendu Tripathy, Robert Nowak
Nearest Neighbor Search (NNS) is a central task in knowledge representation, learning, and reasoning. There is vast literature on efficient algorithms for constructing data structu…
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits
Kwang-Sung Jun, Lalit Jain, Blake Mason +1
We propose improved fixed-design confidence bounds for the linear logistic model. Our bounds significantly improve upon the state-of-the-art bound by Li et al. (2017) via recent de…
Learning Nearest Neighbor Graphs from Noisy Distance Samples
Blake Mason, Ardhendu Tripathy, Robert Nowak
We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance…
Learning Low-Dimensional Metrics
Lalit Jain, Blake Mason, Robert Nowak
This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general l…