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06/13/2023
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
In several practical applications of federated learning (FL), the client...
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12/03/2022
Intermediate Entity-based Sparse Interpretable Representation Learning
Interpretable entity representations (IERs) are sparse embeddings that a...
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10/12/2022
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations
Counterfactual explanations have substantially increased in popularity i...
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10/11/2022
Robustify Transformers with Robust Kernel Density Estimation
Recent advances in Transformer architecture have empowered its empirical...
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10/10/2022
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
Three key properties that are desired of trustworthy machine learning mo...
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06/27/2022
Split Localized Conformal Prediction
Conformal prediction is a simple and powerful tool that can quantify unc...
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05/27/2022
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
We propose a novel approach to the problem of clustering hierarchically ...
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05/25/2022
Federated Self-supervised Learning for Heterogeneous Clients
Federated Learning has become an important learning paradigm due to its ...
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02/15/2022
Architecture Agnostic Federated Learning for Neural Networks
With growing concerns regarding data privacy and rapid increase in data ...
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12/22/2021
MECATS: Mixture-of-Experts for Quantile Forecasts of Aggregated Time Series
We introduce a mixture of heterogeneous experts framework called , which...
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12/13/2021
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection
Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task req...
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06/17/2021
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that off...
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02/25/2021
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
Many real-life applications involve simultaneously forecasting multiple ...
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12/20/2020
Biased Models Have Biased Explanations
We study fairness in Machine Learning (FairML) through the lens of attri...
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10/13/2020
FaiR-N: Fair and Robust Neural Networks for Structured Data
Fairness in machine learning is crucial when individuals are subject to ...
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09/13/2019
Explainable Machine Learning in Deployment
Explainable machine learning seeks to provide various stakeholders with ...
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07/25/2019
Vehicular Multi-object Tracking with Persistent Detector Failures
Autonomous vehicles often perceive the environment by feeding sensor dat...
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07/22/2019
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
Machine learning based decision making systems are increasingly affectin...
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07/19/2019
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval
The limited availability of ground truth relevance labels has been a maj...
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06/11/2019
On Single Source Robustness in Deep Fusion Models
Algorithms that fuse multiple input sources benefit from both complement...
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05/20/2019
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
As artificial intelligence plays an increasingly important role in our s...
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04/18/2019
Explaining Deep Classification of Time-Series Data with Learned Prototypes
The emergence of deep learning networks raises a need for algorithms to ...
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10/23/2018
Interpreting Black Box Predictions using Fisher Kernels
Research in both machine learning and psychology suggests that salient e...
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08/08/2018
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
It has been recently shown that sparse, nonnegative tensor factorization...
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06/22/2018
xGEMs: Generating Examplars to Explain Black-Box Models
This work proposes xGEMs or manifold guided exemplars, a framework to un...
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05/21/2018
Measurement-wise Occlusion in Multi-object Tracking
Handling object interaction is a fundamental challenge in practical mult...
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02/21/2018
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems
A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is...
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11/20/2017
Relaxed Oracles for Semi-Supervised Clustering
Pairwise "same-cluster" queries are one of the most widely used forms of...
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09/11/2017
Semi-Supervised Active Clustering with Weak Oracles
Semi-supervised active clustering (SSAC) utilizes the knowledge of a dom...
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08/16/2017
Optimal Alarms for Vehicular Collision Detection
An important application of intelligent vehicles is advance detection of...
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08/05/2017
Boosting Variational Inference: an Optimization Perspective
Variational Inference is a popular technique to approximate a possibly i...
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03/08/2017
Scalable Greedy Feature Selection via Weak Submodularity
Greedy algorithms are widely used for problems in machine learning such ...
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12/15/2016
Graphical RNN Models
Many time series are generated by a set of entities that interact with o...
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11/14/2016
Preference Completion from Partial Rankings
We propose a novel and efficient algorithm for the collaborative prefere...
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08/02/2016
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization
This work proposes a new algorithm for automated and simultaneous phenot...
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07/12/2016
Information Projection and Approximate Inference for Structured Sparse Variables
Approximate inference via information projection has been recently intro...
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06/16/2016
ACDC: α-Carving Decision Chain for Risk Stratification
In many healthcare settings, intuitive decision rules for risk stratific...
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05/14/2016
Generalized Linear Models for Aggregated Data
Databases in domains such as healthcare are routinely released to the pu...
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05/14/2016
Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Learning the true ordering between objects by aggregating a set of exper...
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03/29/2016
Unified View of Matrix Completion under General Structural Constraints
In this paper, we present a unified analysis of matrix completion under ...
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12/30/2015
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
A gamma process dynamic Poisson factor analysis model is proposed to fac...
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09/15/2015
Exponential Family Matrix Completion under Structural Constraints
We consider the matrix completion problem of recovering a structured mat...
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04/27/2014
A Constrained Matrix-Variate Gaussian Process for Transposable Data
Transposable data represents interactions among two sets of entities, an...
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12/18/2013
Perturbed Gibbs Samplers for Synthetic Data Release
We propose a categorical data synthesizer with a quantifiable disclosure...
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09/26/2013
Constrained Bayesian Inference for Low Rank Multitask Learning
We present a novel approach for constrained Bayesian inference. Unlike c...
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02/11/2013
The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking
We propose a novel hierarchical model for multitask bipartite ranking. T...
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11/10/2012
Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning
Unsupervised models can provide supplementary soft constraints to help c...
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11/10/2012
Dating Texts without Explicit Temporal Cues
This paper tackles temporal resolution of documents, such as determining...
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10/16/2012
Learning to Rank With Bregman Divergences and Monotone Retargeting
This paper introduces a novel approach for learning to rank (LETOR) base...
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04/20/2012