Towards Evaluating Exploratory Model Building Process with AutoML Systems
NYU/UTD/Uncharted; Keywords: Human-guided machine learning; Evaluation Methodology; System Evaluation; Machine Learning; Automated Machine Learning; AutoML; Exploratory Visual Analysis; Exploratory Model Building

Towards Human-Guided Machine Learning
ISI/Harvard/UTD; Keywords: Human-guided machine learning; Automated machine learning (AutoML); Task analysis; Scientific workflows.

Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
NYU

Learning Across Tasks with Surrogate Model Ensembles for Algorithm and Hyperparameter Optimization
SRI (Eindhoven)

Learning to go with the flow: on the adaptability of automated machine learning to evolving data
SRI (Eindhoven)

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
SRI (Eindhoven)

MetaTPOT: Enhancing A Tree-based Pipeline Optimization Tool Using Meta-Learning
UC Berkeley; Keywords: AutoML; Meta-Learning; Genetic Programming(GP); TPOT

Metalearning by Exploiting Granular Machine Learning Pipeline Metadata
BYU; Keywords: metalearning;automl;ml pipeline;metamodel;meta-dataset;metafeatures

Modeling and Forecasting Armed Conflict: AutoML with Human-Guided Machine Learning
Harvard/UTD; Keywords: Human-guided machine learning; Automated machine learning (AutoML); conflict forecasting.

On Robustness of Neural Architecture Search under Label Noise.
TAMU; Keywords: deep learning; automated machine learning; neuralarchitecture search; label noise; robust lossfunction

P4ML: A phased performance-based pipeline planner for automated machine learning
ISI; Keywords: Automating machine learning, planning, pipelines, workflows, AutoML

Preprocessor Selection for Machine Learning Pipelines
BYU; Keywords: metalearning;preprocessor selection;ml pipeline design;automl

Provable Guarantees for Gradient-Based Meta-Learning
SRI (CMU)

RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines
UC Berkeley; Keywords: AutoML; meta-learning; machine learning pipelines

Speeding up Algorithm Selection via Meta-learning and Active Testing
SRI (Eindhoven)

The ABC of Data: A Classifying Framework for Data Readiness
SRI (Eindhoven)

AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks Roque Lopez, Raoni Lourenco, Remi Rampin, Sonia Castelo, Aécio SR Santos, Jorge Henrique Piazentin Ono, Claudio Silva, Juliana Freire AutoML Conference 2023, 2023

Using Pipeline Performance Prediction to Accelerate AutoML Systems Haoxiang Zhang, Roque López, Aécio Santos, Jorge Piazentin Ono, Aline Bessa, Juliana Freire Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning, 2023 Best paper award.

An ecosystem of applications for modeling political Aline Bessa, Sonia Castelo, Rémi Rampin, Aécio Santos, Mike Shoemate, Vito D'Orazio, Juliana Freire Proceedings of the 2021 International Conference on Management of Data

Inference for multiple heterogeneous networks with a common invariant subspace
JHU

Joint Embedding of Graphs
JHU; Keywords: graphs, embedding, feature extraction, statistical inference

Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition
RPI; Keywords: computer vision; facial expression and action unites; prior knowledge

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
UBC

Label Error Correction and Generation Through Label Relationships
RPI; Keywords: Bayesian network, structure learning, and label denoising

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
ISI

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
UBC

On a 'Two Truths' Phenomenon in Spectral Graph Clustering
JHU; Keywords: Spectral Embedding, Spectral Clustering, Graph, Network, Connectome

On consistent vertex nomination schemes
JHU; Keywords: vertex nomination, Bayes optimal

On estimation and inference in latent structure random graphs
JHU; Keywords: efficiency, Latent structure random graphs, manifold learning, spectral graph inference

On spectral embedding performance and elucidating network structure in stochastic block model graphs
JHU; Keywords: Statistical network analysis; random graphs; stochastic block model; Laplacian spectral embed- ding; adjacency spectral embedding; Chernoff information; vertex clustering.

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
UBC

Seeded Graph Matching
JHU; Keywords: Hungarian Algorithm, Quadratic Assignment Problem (QAP), Vertex Alignment

Semi-supervised Sequential Generative Models
UBC

Signal-plus-noise matrix models: eigenvector deviations and fluctuations
JHU; Keywords: Random matrix; Signal-plus-noise; Eigenvector perturbation; Principal component analysis; Asymptotic normality.

Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
JHU; Keywords: Adjacency spectral embedding, Model-based clustering, Stochastic block model

Sparse Variational Inference: Bayesian Coresets from Scratch
UBC

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models
UBC

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators
UBC

The Thermodynamic Variational Objective
UBC

The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
UBC

The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
JHU; Keywords: singular value decomposition, principal component analysis, eigenvector perturbation, spectral methods, Procrustes analysis, high-dimensional statis- tics

Type-augmented Relation Prediction in Knowledge Graphs
RPI; Keywords: prior knowledge, relation prediction, knowledge graph

Uncertainty in Neural Processes
UBC

Vertex Nomination Via Seeded Graph Matching
JHU; Keywords: vertex nomination, graph matching, seeded graph matching, graph inference, graph mining, stochastic block model

Vertex Nomination, Consistent Estimation, and Adversarial Modification
JHU; Keywords: adversarial machine learning, networks, Random graphs, statistics, Vertex nomination

Vertex nomination: The canonical sampling and the extended spectral nomination schemes
JHU; Keywords: vertex nomination, Markov chain Monte Carlo, spectral partitioning, Mclust

Interactive Data Visualization in Jupyter Notebooks
NYU; Keywords: Visualization

On Evaluation of AutoML System
UC Berkeley, BYU, TAMU, CMU, JPL

OpenML-Python: an extensible Python API for OpenML
SRI (Eindhoven)

Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
Stanford

Low-dimensional knowledge graph embeddings via hyperbolic rotations
Stanford

MAST: A Tool for Visualizing CNN Model Architecture Searches
Tufts

ML Friend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data
MIT/FeatureLabs

ML Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
SRI (Eindhoven)

MMD GAN: Towards Deeper Understanding of Moment Matching Network
CMU

Missing Value Imputation for Mixed Data Through Gaussian Copula
Cornell University; Keywords: mixed data; ordinal data; Gaussian copula; missing values; imputation

Multi-Resolution Weak Supervision for Sequential Data
Stanford

Multi-fidelity Bayesian Optimisation with Continuous Approximations
CMU

Multi-task learning with a natural metric for quantitative structure activity relationship learning.
SRI (Eindhoven)

Multi-fidelity Gaussian Process Bandit Optimisation
CMU

Multitask learning and benchmarking with clinical time series data
ISI

Mutually Regressive Point Processes
CMU

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
CMU

Neural Architecture Search with Bayesian Optimisation and Optimal Transport
CMU

Newton-MR: Newton's Method Without Smoothness or Convexity
ICSI

Noise-Tolerant Interactive Learning Using Pairwise Comparisons
CMU

Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losse
CMU

Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses
CMU

Nonparametric Density Estimation with Adversarial Losse
CMU

Nonparametric Regression with Comparisons:Escaping the Curse of Dimensionality with Ordinal Information
CMU

Northstar: An Interactive Data Science System
MIT/Brown

NuSys: Towards a Document IDE for Knowledge Work
MIT/Brown

OBOE: Collaborative Filtering for AutoML Model Selection
Cornell University; Keywords: AutoML; meta-learning; time-constrained; model selection; collaborative filtering

On the Downstream Performance of Compressed Word Embeddings
Stanford

On the Interaction Effects Between Prediction and Clustering
CMU

Online high-rank matrix completion
Cornell University

Ordalia: Deep Learning Hyperparameter Search via Generalization Error Bounds Extrapolation
MIT/Brown; Keywords: Deep Learning, Hyperparameters Optimization, Multi-armed Bandits, Automated Machine Learning

Out-of-sample extension of graph adjacency spectral embedding
ICSI

Overton: A Data System for Monitoring and Improving Machine-Learned Products
Stanford

PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction
UC Berkeley

Pairwise Feedback for Data Programming
CMU

Parallelised Bayesian Optimisation via Thompson Sampling
CMU

Personalized Explanations for Hybrid Recommender Systems
SRI (UCSC)

Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction
ICSI

Planning as Inference in Epidemiological Models
UBC

Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
Cornell University

Predicting Delayed Cerebral Ischemia Using Sequential Patterns in Plethysmography Data
CMU

Predicting Propositional Satisfiability via End-to-End Learning
UBC

Predicting hypotension episode with numerical vital sign signals in the intensive care unit
CMU

Predicting neurological recovery with Canonical Autocorrelation Embeddings
CMU

Prediction Factory: automated development and collaborative evaluation of predictive models
MIT/FeatureLabs

ProSecCo: Progressive Sequence Mining with Convergence Guarantees.
MIT/Brown

Prognostication of Neurological Recovery by Analyzing Structural Breaks in EEG Data
CMU

PyHessian: Neural Networks Through the Lens of the Hessian
ICSI

Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
CMU

Random Search and Reproducibility for Neural Architecture Search
SRI (CMU)

Randomized dynamic mode decomposition
ICSI

Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels
Stanford

Rethinking Curriculum Learning With Incremental Labels And Adaptive Compensation
Michigan

Revealing Actionable Simplicity in Data
CMU

Revisiting Reuse for Approximate Query Processing
MIT/Brown

Robust Multi-View Representation Learning (Student Abstract)
CMU

Safe Visual Data Exploration
MIT/Brown

Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
ICSI

Scalable Probabilistic Causal Structure Discovery
SRI (UCSC)

Scalable Structure Learning For Probabilistic Soft Logic
SRI (UCSC)

Scene Graph Prediction with Limited Labels
Stanford

Semi-Supervised Prediction of Comorbid Rare Conditions Using Medical Claims Data
CMU

Shallow learning for fluid flow reconstruction with limited sensors and limited data
ICSI

Sherlock: A Deep Learning Approach to Semantic Data Type Detection.
MIT/Brown

Slice Finder: Automated Data Slicing for Model Validation
MIT/Brown

Slice-Based Learning: A Programming Model for Residual Learning on Critical Slices
Stanford

Smallify: Learning Network Size while Training
MIT/Brown

Snorkel Drybell: A Case Study in Deploying Weak Supervision at Industrial Scale
Stanford

Snorkel: Rapid Training Data Creation with Weak Supervision
Stanford

Snuba: Automating Weak Supervision to Label Training Data
Stanford

Solving the "False Positives" Problem in Fraud Prediction
MIT/FeatureLabs

Sparse Principal Component Analysis via Variable Projection
ICSI

Spatio-Temporal Urban Data Analysis: A Visual Analytics Perspective
NYU

Statistical guarantees for local graph clustering
ICSI

Sub-Sampled Newton Methods I: Globally Convergent Algorithms
ICSI

Supervised Local Modeling for Interpretability
SRI (CMU)

Supervised Principal Component Analysis Via Manifold Optimization
Michigan

Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations
SRI (UCSC)

TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size
MIT/Brown

Tandem Inference: An Out-of-Core Streaming Algorithm For Very Large-Scale Relational Inference
SRI (UCSC)

Techniques for Automated Machine Learning
TAMU; Keywords: automated machine learning; neural architecture search; bayesian optimization; reinforce-ment learning; evolutionary algorithm; gradient-based methods

The Binomial Block Bootstrap Estimatorfor Evaluating Loss on Dependent Clusters
CMU

The Impact of Environmental Stressors on Human Trafficking
SRI (UCSC)

The Machine Learning Bazaar: Harnassing the ML Ecosystem for Effective System Development
MIT/FeatureLabs

The Multi-fidelity Multi-armed Bandit
CMU

The Statistical Recurrent Unit
CMU

The online performance estimation framework: Heterogeneous Ensemble Learning for Data Streams
SRI (Eindhoven)

Third-Party Data Providers Ruin Simple Mechanisms
UBC (Hebrew University of Jerusalem)

Thresholding Bandit Problem with Dueling Choices
CMU

Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series
NYU

Toward Sustainable Insights, or Why Polygamy is Bad for You
MIT/Brown

Towards Automated Neural Architecture Discovering for Click-Through Rate Prediction
TAMU; Keywords: Information systems; Recommender systems; Theory of computation; Evolutionary algorithms; Computing methodologies; Neural networks

Towards Interactive Curation & Automatic Tuning of ML Pipelines
MIT/Brown

Towards Interactive Data Exploration
MIT/Brown

Towards Interactive Data Exploration.
MIT/Brown

Towards Quantifying Uncertainty in Data Analysis & Exploration.
MIT/Brown

Tractable Marginal Inference for Hinge-Loss Markov Random Fields
SRI (UCSC)

Tractable Probabilistic Reasoning Through Effective Grounding
SRI (UCSC)

Training Complex Models with Multi-Task Weak Supervision
Stanford

Transformation Autoregressive Networks
CMU

Trust, Perceptions, and Effects of News Sources and Social Media: A Data Driven Study of the Recent Unrest in Kashmir
CMU

Tuplex: Robust, Efficient Analytics When Python Rules
MIT/Brown

TwoRavens for Event Data
Harvard/UTD

Understanding Evolution of Long-running MOOCs
SRI (UCSC)

Understanding Hybrid-MOOC Effectiveness with a Collective Socio-Behavioral Model.
SRI (UCSC)

Understanding Spatio-Temporal Urban Processes
NYU; Keywords: data quality;data profiling;urban data

Understanding and Improving Information Transfer in Multi-Task Learning
Stanford

Unified Vision-Language Pre-Training for Image Captioning and VQA
Michigan

Unknown Examples & Machine Learning Model Generalization
MIT/Brown

Using Comparisons to Reduce Cost of Data Annotation Required to Train Models for Bedside Monitoring
CMU

Using Deep Networks and Transfer Learning to Address Disinformation
Uncharted

VMI-PSL: Visual Model Inspector for Probabilistic Soft Logic
SRI (UCSC)

Variational Perspective on Local Graph Clustering
ICSI

Video Event Specification using Programming Composition
Stanford

Visual Designs for Binned Aggregation of Multi-Class Scatterplots
Tufts

Visual Exploration of Time Series Anomalies with Metro-Viz
MIT/Brown

VizML: A Machine Learning Approach to Visualization Recommendation
MIT/Brown

VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
MIT/Brown

VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository.
MIT/Brown

VizRec: A framework for secure data exploration via visual representation
MIT/Brown

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
Stanford