14 citations · 25 across the 27 of their papers we have counts for
31 papers
Schrödinger-Bass Bridge Barycenter Models for Measure-Valued Data
Ngoc-Hai Nguyen, Thuan Nguyen, Prakash Ishwar +1
Barycenter models, based on stochastic and deterministic optimal transport bridges between probability measures, have proven to be useful for supervised and unsupervised learning f…
Sparse Dictionary Learning for Measure-Valued Data via Linearized Optimal Transport: Efficient Algorithms and Sample Complexity
Brendan Mallery, Shuchin Aeron, James Murphy
We study dictionary learning for probability measure-valued data by embedding measures into Hilbert spaces using features derived from optimal transport geometry. We learn data-ada…
Primal-Dual Flow Matching for Sample-Wise Constrained Generation
Zhengyan Huan, Peter Y. Lu, Shuchin Aeron
We consider the problem of learning to generate data while satisfying a prescribed \emph{sample-wise} constraint with a user-specified probability of constraint satisfaction. Start…
Multi-Marginal Inverse Optimal Transport for Contrastive Learning Via Explicit Anchor-Positive-Negative Coupling
Ngoc-Hai Nguyen, Thuan Nguyen, Prakash Ishwar +1
Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pair of data marginals in the representati…
Evidence-Aligned Local Composition of Discrete Experts for Sequence Restoration
Mohammad Panahazari, Usman A. Khan, Shuchin Aeron
A document modeled as a discrete sequence of tokens can be thought of as being generated from a composition of texts from different domains; a README file, for example, moves betwe…
Measuring Cognitive Engagement in Collaborative Discourse with an Extended ICAP Framework: Comparing Human Annotation, In-Context Learning, and Reflective LLM Agents
Lan Anh Do, Hanling Jiang, Shuchin Aeron +1
Collaboration supports learning and problem-solving, but its effectiveness depends on cognitive engagement during discourse. This study applies an extended 7-point ICAP framework b…
Optimal Representations for Generalized Contrastive Learning with Imbalanced Datasets
Thuan Nguyen, Shuchin Aeron, D. Richard Brown +1
In this paper, we provide a computable characterization of the geometry of optimal representations in Contrastive Learning (CL) when the classes are imbalanced. When classes are ba…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Inverse Problems Conditioned on Observation Ensembles: Applications and Methods
Zhengyan Huan, Camila Pazos, Martin Klassen +3
We introduce a new multivariate statistical problem that we refer to as the Ensemble-conditioned Inverse Problem (EIP). The aim of EIP is to invert for an ensemble that is distribu…
Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
Mudit Gaur, Prashant Trivedi, Shuchin Aeron +3
Flow matching has recently emerged as a promising alternative to diffusion-based generative models, offering faster sampling and simpler training by learning continuous flows gover…
From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding
Farjana Sultana Mim, Shuchin Aeron, Eric Miller +1
Identifying discourse features in student conversations is quite important for educational researchers to recognize the curricular and pedagogical variables that cause students to…
Constraint-Aware Flow Matching via Randomized Exploration
Zhengyan Huan, Jacob Boerma, Li-Ping Liu +1
We consider the problem of designing constraint-aware flow matching (FM) models that address the issue of constraint violations commonly observed in vanilla generative models. We c…
Synthesis and Analysis of Data as Probability Measures with Entropy-Regularized Optimal Transport
Brendan Mallery, James M. Murphy, Shuchin Aeron
We consider synthesis and analysis of probability measures using the entropy-regularized Wasserstein-2 cost and its unbiased version, the Sinkhorn divergence. The synthesis problem…
Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications
Matthew Werenski, Brendan Mallery, Shuchin Aeron +1
We propose the linear barycentric coding model (LBCM) which utilizes the linear optimal transport (LOT) metric for analysis and synthesis of probability measures. We provide a clos…
Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models
Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin +4
Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional u…
Estimation of entropy-regularized optimal transport maps between non-compactly supported measures
Matthew Werenski, James M. Murphy, Shuchin Aeron
This paper addresses the problem of estimating entropy-regularized optimal transport (EOT) maps with squared-Euclidean cost between source and target measures that are subGaussian.…
Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-Collapse
Ruijie Jiang, Thuan Nguyen, Shuchin Aeron +1
For a widely-studied data model and general loss and sample-hardening functions we prove that the losses of Supervised Contrastive Learning (SCL), Hard-SCL (HSCL), and Unsupervised…
Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images
Zeviel Imani, Shuchin Aeron, Taritree Wongjirad
For the first time, we show high-fidelity generation of LArTPC-like data using a generative neural network. This demonstrates that methods developed for natural images do transfer…
Systematic comparison of semi-supervised and self-supervised learning for medical image classification
Zhe Huang, Ruijie Jiang, Shuchin Aeron +1
In typical medical image classification problems, labeled data is scarce while unlabeled data is more available. Semi-supervised learning and self-supervised learning are two diffe…
A principled approach to model validation in domain generalization
Boyang Lyu, Thuan Nguyen, Matthias Scheutz +2
Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen dom…
Trade-off between reconstruction loss and feature alignment for domain generalization
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
Domain generalization (DG) is a branch of transfer learning that aims to train the learning models on several seen domains and subsequently apply these pre-trained models to other…
Geometric Sparse Coding in Wasserstein Space
Marshall Mueller, Shuchin Aeron, James M. Murphy +1
Wasserstein dictionary learning is an unsupervised approach to learning a collection of probability distributions that generate observed distributions as Wasserstein barycentric co…
Supervised Contrastive Learning with Hard Negative Samples
Ruijie Jiang, Thuan Nguyen, Prakash Ishwar +1
Through minimization of an appropriate loss function such as the InfoNCE loss, contrastive learning (CL) learns a useful representation function by pulling positive samples close t…
Joint covariate-alignment and concept-alignment: a framework for domain generalization
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly…