University of Colorado Boulder

research

∙ 09/13/2021

Online Unsupervised Learning of Visual Representations and Categories

Real world learning scenarios involve a nonstationary distribution of cl...

Mengye Ren, et al. ∙

research

∙ 08/31/2018

Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction

Today's densely instrumented world offers tremendous opportunities for c...

Homa Hosseinmardi, et al. ∙

research

∙ 01/17/2019

Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails

Robots hold promise in many scenarios involving outdoor use, such as sea...

Michael L. Iuzzolino, et al. ∙

research

∙ 10/23/2018

Stochastic Substitute Training: A Gray-box Approach to Craft Adversarial Examples Against Gradient Obfuscation Defenses

It has been shown that adversaries can craft example inputs to neural ne...

Mohammad Hashemi, et al. ∙

research

∙ 07/07/2021

A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems tha...

Masoud Mansoury, et al. ∙

research

∙ 11/11/2021

Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport

This paper presents a novel discriminator-constrained optimal transport ...

Hsin-Yi Lin, et al. ∙

research

∙ 05/03/2021

MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture

This work presents MemX: a biologically-inspired attention-aware eyewear...

Yuhu Chang, et al. ∙

research

∙ 01/14/2020

"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans

To support human decision making with machine learning models, we often ...

Vivian Lai, et al. ∙

research

∙ 12/23/2021

A Random Point Initialization Approach to Image Segmentation with Variational Level-sets

Image segmentation is an essential component in many image processing an...

J. N. Mueller, et al. ∙

research

∙ 12/21/2021

Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning

Generalized few-shot semantic segmentation was introduced to move beyond...

Josh Myers-Dean, et al. ∙

research

∙ 11/19/2018

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Humans are the final decision makers in critical tasks that involve ethi...

Vivian Lai, et al. ∙

research

∙ 08/01/2019

Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness

Various domain users are increasingly leveraging real-time social media ...

Luke S. Snyder, et al. ∙

research

∙ 02/06/2019

Equal Opportunity in Online Classification with Partial Feedback

We study an online classification problem with partial feedback in which...

Yahav Bechavod, et al. ∙

research

∙ 07/16/2019

Decentralized & Collaborative AI on Blockchain

Machine learning has recently enabled large advances in artificial intel...

Justin D. Harris, et al. ∙

research

∙ 07/09/2020

Wandering Within a World: Online Contextualized Few-Shot Learning

We aim to bridge the gap between typical human and machine-learning envi...

Mengye Ren, et al. ∙

research

∙ 10/22/2021

HDRVideo-GAN: Deep Generative HDR Video Reconstruction

High dynamic range (HDR) videos provide a more visually realistic experi...

Mrinal Anand, et al. ∙

research

∙ 06/16/2019

A tunable multiresolution smoother for scattered data with application to particle filtering

A smoothing algorithm is presented that can reduce the small-scale conte...

Gregor A. Robinson, et al. ∙

research

∙ 12/08/2017

Online Red Packets: A Large-scale Empirical Study of Gift Giving on WeChat

Gift giving is a ubiquitous social phenomenon, and red packets have been...

Yuan Yuan, et al. ∙

research

∙ 10/11/2017

Discrete Event, Continuous Time RNNs

We investigate recurrent neural network architectures for event-sequence...

Michael C. Mozer, et al. ∙

research

∙ 11/08/2017

"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships

As technology becomes more advanced, those who design, use and are other...

Brett W Israelsen, et al. ∙

research

∙ 10/31/2017

Characterizing the structural diversity of complex networks across domains

The structure of complex networks has been of interest in many scientifi...

Kansuke Ikehara, et al. ∙

research

∙ 12/24/2016

Improving Human-Machine Cooperative Visual Search With Soft Highlighting

Advances in machine learning have produced systems that attain human-lev...

Ronald T. Kneusel, et al. ∙

research

∙ 08/01/2017

"I can assure you [...] that it's going to be all right" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships

As technology become more advanced, those who design, use and are otherw...

Brett W Israelsen, et al. ∙

research

∙ 08/21/2017

Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge

Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is demo...

David Zhuzhunashvili, et al. ∙

research

∙ 08/08/2017

Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects

The Nyström method is a popular technique for computing fixed-rank appro...

Farhad Pourkamali-Anaraki, et al. ∙

research

∙ 03/14/2016

How deep is knowledge tracing?

In theoretical cognitive science, there is a tension between highly stru...

Mohammad Khajah, et al. ∙

research

∙ 05/25/2017

A Neural Framework for Generalized Topic Models

Topic models for text corpora comprise a popular family of methods that ...

Dallas Card, et al. ∙

research

∙ 07/29/2017

Classifying Graphs as Images with Convolutional Neural Networks

The task of graph classification is currently dominated by graph kernels...

Antoine Jean-Pierre Tixier, et al. ∙

research

∙ 12/13/2016

Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions

A key drawback of the current generation of artificial decision-makers i...

Brett Israelsen, et al. ∙

research

∙ 12/20/2016

Randomized Clustered Nystrom for Large-Scale Kernel Machines

The Nystrom method has been popular for generating the low-rank approxim...

Farhad Pourkamali-Anaraki, et al. ∙

research

∙ 03/13/2016

A Signaling Game Approach to Databases Querying and Interaction

As most database users cannot precisely express their information needs,...

Ben McCamish, et al. ∙

research

∙ 08/20/2016

The ground truth about metadata and community detection in networks

Across many scientific domains, there is a common need to automatically ...

Leto Peel, et al. ∙

research

∙ 06/15/2015

Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game

Interpersonal relations are fickle, with close friendships often dissolv...

Vlad Niculae, et al. ∙

research

∙ 03/01/2016

Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition

We focus on the robust principal component analysis (RPCA) problem, and ...

Aleksandr Y. Aravkin, et al. ∙

research

∙ 12/30/2015

Estimation of the sample covariance matrix from compressive measurements

This paper focuses on the estimation of the sample covariance matrix fro...

Farhad Pourkamali-Anaraki, et al. ∙

research

∙ 10/31/2015

Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means

We analyze a compression scheme for large data sets that randomly keeps ...

Farhad Pourkamali-Anaraki, et al. ∙

research

∙ 06/19/2015

Detectability thresholds and optimal algorithms for community structure in dynamic networks

We study the fundamental limits on learning latent community structure i...

Amir Ghasemian, et al. ∙

research

∙ 04/05/2015

Efficient Dictionary Learning via Very Sparse Random Projections

Performing signal processing tasks on compressive measurements of data h...

Farhad Pourkamali-Anaraki, et al. ∙

research

∙ 11/14/2014

A unified view of generative models for networks: models, methods, opportunities, and challenges

Research on probabilistic models of networks now spans a wide variety of...

Abigail Z. Jacobs, et al. ∙

research

∙ 04/03/2014

An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length

Bayesian networks are convenient graphical expressions for high dimensio...

Jem Corcoran, et al. ∙

research

∙ 04/02/2014

Learning Latent Block Structure in Weighted Networks

Community detection is an important task in network analysis, in which w...

Christopher Aicher, et al. ∙

research

∙ 03/05/2014

Detecting change points in the large-scale structure of evolving networks

Interactions among people or objects are often dynamic in nature and can...

Leto Peel, et al. ∙

research

∙ 10/31/2011

Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective

We represent planning as a set of loosely coupled network flow problems,...

Menkes Hector Louis van den Briel, et al. ∙

research

∙ 12/27/2013

Active Discovery of Network Roles for Predicting the Classes of Network Nodes

Nodes in real world networks often have class labels, or underlying attr...

Leto Peel, et al. ∙

research

∙ 05/24/2013

Adapting the Stochastic Block Model to Edge-Weighted Networks

We generalize the stochastic block model to the important case in which ...

Christopher Aicher, et al. ∙

research

∙ 05/01/2013

Inverting Nonlinear Dimensionality Reduction with Scale-Free Radial Basis Function Interpolation

Nonlinear dimensionality reduction embeddings computed from datasets do ...

Nathan D. Monnig, et al. ∙

research

∙ 07/08/2012

Keeping greed good: sparse regression under design uncertainty with application to biomass characterization

In this paper, we consider the classic measurement error regression scen...

David J. Biagioni, et al. ∙

research

∙ 11/05/2014

Online SLAM with Any-time Self-calibration and Automatic Change Detection

A framework for online simultaneous localization, mapping and self-calib...

Nima Keivan, et al. ∙

research

∙ 11/04/2014

Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery

We present an unsupervised framework for simultaneous appearance-based o...

Lu Ma, et al. ∙

research

∙ 11/20/2011

Non-Asymptotic Analysis of Tangent Space Perturbation

Constructing an efficient parameterization of a large, noisy data set of...

Daniel N. Kaslovsky, et al. ∙

University of Colorado Boulder

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