
Explainable Machine Learning in Deployment
Explainable machine learning seeks to provide various stakeholders with ...
read it

Interpreting Black Box Predictions using Fisher Kernels
Research in both machine learning and psychology suggests that salient e...
read it

Towards Realistic Individual Recourse and Actionable Explanations in BlackBox Decision Making Systems
Machine learning based decision making systems are increasingly affectin...
read it

Relaxed Oracles for SemiSupervised Clustering
Pairwise "samecluster" queries are one of the most widely used forms of...
read it

Graphical RNN Models
Many time series are generated by a set of entities that interact with o...
read it

SemiSupervised Active Clustering with Weak Oracles
Semisupervised active clustering (SSAC) utilizes the knowledge of a dom...
read it

Optimal Alarms for Vehicular Collision Detection
An important application of intelligent vehicles is advance detection of...
read it

Boosting Variational Inference: an Optimization Perspective
Variational Inference is a popular technique to approximate a possibly i...
read it

Scalable Greedy Feature Selection via Weak Submodularity
Greedy algorithms are widely used for problems in machine learning such ...
read it

Preference Completion from Partial Rankings
We propose a novel and efficient algorithm for the collaborative prefere...
read it

Linear and Order Statistics Combiners for Pattern Classification
Several researchers have experimentally shown that substantial improveme...
read it

Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical PreCancer
The mortality related to cervical cancer can be substantially reduced th...
read it

Identifiable Phenotyping using Constrained NonNegative Matrix Factorization
This work proposes a new algorithm for automated and simultaneous phenot...
read it

Information Projection and Approximate Inference for Structured Sparse Variables
Approximate inference via information projection has been recently intro...
read it

ACDC: αCarving Decision Chain for Risk Stratification
In many healthcare settings, intuitive decision rules for risk stratific...
read it

Generalized Linear Models for Aggregated Data
Databases in domains such as healthcare are routinely released to the pu...
read it

Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Learning the true ordering between objects by aggregating a set of exper...
read it

Unified View of Matrix Completion under General Structural Constraints
In this paper, we present a unified analysis of matrix completion under ...
read it

Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
A gamma process dynamic Poisson factor analysis model is proposed to fac...
read it

Exponential Family Matrix Completion under Structural Constraints
We consider the matrix completion problem of recovering a structured mat...
read it

Dating Texts without Explicit Temporal Cues
This paper tackles temporal resolution of documents, such as determining...
read it

A Constrained MatrixVariate Gaussian Process for Transposable Data
Transposable data represents interactions among two sets of entities, an...
read it

Perturbed Gibbs Samplers for Synthetic Data Release
We propose a categorical data synthesizer with a quantifiable disclosure...
read it

Constrained Bayesian Inference for Low Rank Multitask Learning
We present a novel approach for constrained Bayesian inference. Unlike c...
read it

The trace norm constrained matrixvariate Gaussian process for multitask bipartite ranking
We propose a novel hierarchical model for multitask bipartite ranking. T...
read it

Probabilistic Combination of Classifier and Cluster Ensembles for Nontransductive Learning
Unsupervised models can provide supplementary soft constraints to help c...
read it

Learning to Rank With Bregman Divergences and Monotone Retargeting
This paper introduces a novel approach for learning to rank (LETOR) base...
read it

A PrivacyAware Bayesian Approach for Combining Classifier and Cluster Ensembles
This paper introduces a privacyaware Bayesian approach that combines en...
read it

Robust Combining of Disparate Classifiers through Order Statistics
Integrating the outputs of multiple classifiers via combiners or metale...
read it

Nonparametric Bayesian Sparse Graph Linear Dynamical Systems
A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is...
read it

xGEMs: Generating Examplars to Explain BlackBox Models
This work proposes xGEMs or manifold guided exemplars, a framework to un...
read it

Measurementwise Occlusion in Multiobject Tracking
Handling object interaction is a fundamental challenge in practical mult...
read it

PIVETedGranite: Computational Phenotypes through Constrained Tensor Factorization
It has been recently shown that sparse, nonnegative tensor factorization...
read it

Explaining Deep Classification of TimeSeries Data with Learned Prototypes
The emergence of deep learning networks raises a need for algorithms to ...
read it

CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
As artificial intelligence plays an increasingly important role in our s...
read it

On Single Source Robustness in Deep Fusion Models
Algorithms that fuse multiple input sources benefit from both complement...
read it

Vehicular Multiobject Tracking with Persistent Detector Failures
Autonomous vehicles often perceive the environment by feeding sensor dat...
read it

Learning More From Less: Towards Strengthening Weak Supervision for AdHoc Retrieval
The limited availability of ground truth relevance labels has been a maj...
read it
Joydeep Ghosh
verfied profile
Professor, Schlumberger Centennial Chair in Electrical Engineering at The University of Texas at Austin and Chief Scientist at Cognitivescale