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Found 52 papers, 13 papers with code
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution.
Constrained Bayesian Inference for Low Rank Multitask Learning
We present a novel approach for constrained Bayesian inference.
Perturbed Gibbs Samplers for Synthetic Data Release
We propose a categorical data synthesizer with a quantifiable disclosure risk.
A Constrained Matrix-Variate Gaussian Process for Transposable Data
Transposable data represents interactions among two sets of entities, and are typically represented as a matrix containing the known interaction values.
On Prior Distributions and Approximate Inference for Structured Variables
In cases where this projection is intractable, we propose a family of parameterized approximations indexed by subsets of the domain.
Exponential Family Matrix Completion under Structural Constraints
We consider the matrix completion problem of recovering a structured matrix from noisy and partial measurements.
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors.
Unified View of Matrix Completion under General Structural Constraints
In this paper, we present a unified analysis of matrix completion under general low-dimensional structural constraints induced by {\em any} norm regularization.
Generalized Linear Models for Aggregated Data
We consider a limiting case of generalized linear modeling when the target variables are only known up to permutation, and explore how this relates to permutation testing; a standard technique for assessing statistical dependency.
Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation.
ACDC: $α$-Carving Decision Chain for Risk Stratification
The resulting chain of decision rules yields a pure subset of the minority class examples.
Information Projection and Approximate Inference for Structured Sparse Variables
Approximate inference via information projection has been recently introduced as a general-purpose approach for efficient probabilistic inference given sparse variables.
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization
This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co-occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs).
Preference Completion from Partial Rankings
We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over a shared set of items, based on a limited number of observed affinity values.
Graphical RNN Models
Many time series are generated by a set of entities that interact with one another over time.
Scalable Greedy Feature Selection via Weak Submodularity
Furthermore, we show that a bounded submodularity ratio can be used to provide data dependent bounds that can sometimes be tighter also for submodular functions.
Boosting Variational Inference: an Optimization Perspective
Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one.
Optimal Alarms for Vehicular Collision Detection
An important application of intelligent vehicles is advance detection of dangerous events such as collisions.
Semi-Supervised Active Clustering with Weak Oracles
For each weak oracle model, we show that a small query complexity is adequate for the effective $k$ means clustering with high probability.
Relaxed Oracles for Semi-Supervised Clustering
Pairwise "same-cluster" queries are one of the most widely used forms of supervision in semi-supervised clustering.
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems
A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is proposed to model sequentially observed multivariate data.
Measurement-wise Occlusion in Multi-object Tracking
Handling object interaction is a fundamental challenge in practical multi-object tracking, even for simple interactive effects such as one object temporarily occluding another.
xGEMs: Generating Examplars to Explain Black-Box Models
This work proposes xGEMs or manifold guided exemplars, a framework to understand black-box classifier behavior by exploring the landscape of the underlying data manifold as data points cross decision boundaries.
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping.
Interpreting Black Box Predictions using Fisher Kernels
Research in both machine learning and psychology suggests that salient examples can help humans to interpret learning models.
Explaining Deep Classification of Time-Series Data with Learned Prototypes
We improve upon existing models by optimizing for increased prototype diversity and robustness, visualize how these prototypes in the latent space are used by the model to distinguish classes, and show that prototypes are capable of learning features on two dimensional time-series data to produce explainable insights during classification tasks.
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
Given a model and an input instance, CERTIFAI uses a custom genetic algorithm to generate counterfactuals: instances close to the input that change the prediction of the model.
On Single Source Robustness in Deep Fusion Models
We investigate learning fusion algorithms that are robust against noise added to a single source.
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval
Recent works have shown that it is possible to take advantage of the performance of these unsupervised methods to generate training data for learning-to-rank models.
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome.
Vehicular Multi-object Tracking with Persistent Detector Failures
Autonomous vehicles often perceive the environment by feeding sensor data to a learned detector algorithm, then feeding detections to a multi-object tracker that models object motions over time.
Robotics
Explainable Machine Learning in Deployment
Yet there is little understanding of how organizations use these methods in practice.
FaiR-N: Fair and Robust Neural Networks for Structured Data
Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains.
Model-Agnostic Explanations using Minimal Forcing Subsets
How can we find a subset of training samples that are most responsible for a specific prediction made by a complex black-box machine learning model?
Biased Models Have Biased Explanations
We study fairness in Machine Learning (FairML) through the lens of attribute-based explanations generated for machine learning models.
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
In such applications, it is important that the forecasts, in addition to being reasonably accurate, are also consistent w. r. t one another.
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.
MECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series
We introduce a mixture of heterogeneous experts framework called MECATS, which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy.
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection
In this work, we empirically study how and whether such methods, applied in a bi-modal setting, can improve an existing VQA system's performance on the KBVQA task.
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series
We conduct a comprehensive evaluation of both point and quantile forecasts for hierarchical time series (HTS), including public data and user records from a large financial software company.
Architecture Agnostic Federated Learning for Neural Networks
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(FL) has become an important learning paradigm.
Federated Self-supervised Learning for Heterogeneous Clients
As the field advances, two key challenges that still remain to be addressed are: (1) system heterogeneity - variability in the compute and/or data resources present on each client, and (2) lack of labeled data in certain federated settings.
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
To attain this goal, each time series is first assigned the forecast for its cluster representative, which can be considered as a "shrinkage prior" for the set of time series it represents.
Split Localized Conformal Prediction
The modified score inherits the spirit of split conformal methods, which is simple and efficient and can scale to high dimensional settings.
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
In this paper, we propose FEAMOE, a novel "mixture-of-experts" inspired framework aimed at learning fairer, more explainable/interpretable models that can also rapidly adjust to drifts in both the accuracy and the fairness of a classifier.
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations
The ability to quickly examine combinations of the most promising gradient directions as well as to incorporate additional user-defined constraints allows us to generate multiple counterfactual explanations that are sparse, realistic, and robust to input manipulations.
Intermediate Entity-based Sparse Interpretable Representation Learning
However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training.
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
Moreover, the need for uncertainty quantification and data privacy constraints are often particularly amplified for clients that have limited local data.
Federated Learning for Estimating Heterogeneous Treatment Effects
To overcome this obstacle, in this work, we propose a novel framework for collaborative learning of HTE estimators across institutions via Federated Learning.
Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes
Data accuracy is essential for scientific research and policy development.
Novel Node Category Detection Under Subpopulation Shift
We introduce a new approach, Recall-Constrained Optimization with Selective Link Prediction (RECO-SLIP), to detect nodes belonging to novel categories in attributed graphs under subpopulation shifts.
Exploring Explainability in Video Action Recognition
To address these, we introduce Video-TCAV, by building on TCAV for Image Classification tasks, which aims to quantify the importance of specific concepts in the decision-making process of Video Action Recognition models.
