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Mark A. Davenport

11 accepted papers

2026

LORE: Jointly Learning The Intrinsic Dimensionality and Relative Similarity Structure from Ordinal Data

Vivek Anand, Alec Helbling, Mark A. Davenport, Gordon J Berman, Sankaraleengam Alagapan, Christopher John Rozell
ICLR 2026poster

Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging problem. We introduce LORE (Low Rank Ordinal Embedding), a scalable framework that jointly learns both the intrinsic dimensionality and an ordinal embedding fr…

Cited by 0Source ↗code ↗Scholar ↗
2025

SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures

Peimeng Guan, Mark A. Davenport
ICML 2025poster

Inverse problems aim to reconstruct unseen data from corrupted or perturbed measurements. While most work focuses on improving reconstruction quality, generalization accuracy and robustness are equally important, especially for safety-critical applications. Model-based architectures (MBAs), such as…

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2023

Active metric learning and classification using similarity queries

Namrata Nadagouda, Austin Xu, Mark A. Davenport
UAI 2023poster

Active learning is commonly used to train label-efficient models by adaptively selecting the most informative queries. However, most active learning strategies are designed to either learn a representation of the data (e.g., embedding or metric learning) or perform well on a task (e.g., classificati…

Cited by 13Source ↗PDF ↗Scholar ↗
2023

Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning

Austin Xu, Andrew McRae, Jingyan Wang, Mark A. Davenport, Ashwin Pananjady
NeurIPS 2023poster

We introduce a new type of query mechanism for collecting human feedback, called the perceptual adjustment query (PAQ). Being both informative and cognitively lightweight, the PAQ adopts an inverted measurement scheme, and combines advantages from both cardinal and ordinal queries. We showcase the P…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2022

Delta Distancing: A Lifting Approach to Localizing Items from User Comparisons

Andrew D. McRae, Austin Xu, Jihui Jin, Namrata Nadagouda, Nauman Ahad, Peimeng Guan +2
ICASSP 2022accepted

A common problem in recommendation systems is to learn a model of user preferences based only on comparisons of the relative attractiveness of different items. We consider this problem in the context of an ideal point model of user preference, where each user can be represented as a point in a low-d…

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2021

Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time

Feng Zhu, Andrew R Sedler, Harrison A Grier, Nauman Ahad, Mark A. Davenport, Matthew Kaufman +2
NeurIPS 2021poster

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obt…

Cited by 14Source ↗PDF ↗code ↗Scholar ↗
2021

Semi-supervised Sequence Classification through Change Point Detection

Nauman Ahad, Mark A. Davenport
AAAI 2021technical

Sequential sensor data is generated in a wide variety of real-world applications. A fundamental machine learning challenge involves learning effective classifiers for such sequential data. While deep learning has led to impressive performance gains in recent years within domains such as speech, this…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2020

The Picasso Algorithm for Bayesian Localization Via Paired Comparisons in a Union of Subspaces Model

Gregory Canal, Marissa Connor, Jihui Jin, Namrata Nadagouda, Matthew R. O'Shaughnessy, Christopher J. Rozell +1
ICASSP 2020accepted

We develop a framework for localizing an unknown point w using paired comparisons of the form "w is closer to point x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> than to x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http…

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2017

Fast orthogonal approximations of sampled sinusoids and bandlimited signals

Zhihui Zhu, Santhosh Karnik, Michael B. Wakin, Mark A. Davenport, Justin K. Romberg
ICASSP 2017accepted

In this paper, we provide a dictionary for representing the discrete vector one obtains when collecting a finite set of uniform samples from a baseband analog signal. Like the discrete prolate spheroidal sequences (DPSS's), the proposed orthogonal basis compactly captures most of the energy in overs…

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