Search Results for author:
Found 31 papers, 13 papers with code
Essay Quality Signals as Weak Supervision for Source-based Essay Scoring
However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model.
Change is Hard: A Closer Look at Subpopulation Shift
Machine learning models often perform poorly on subgroups that are underrepresented in the training data.
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
By relating masked modeling to manifold learning, SimMTM proposes to recover masked time points by the weighted aggregation of multiple neighbors outside the manifold, which eases the reconstruction task by assembling ruined but complementary temporal variations from multiple masked series.
Efficient Estimation for Longitudinal Network via Adaptive Merging
Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time.
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts
In this work, we introduce the problem of attributing performance differences between environments to distribution shifts in the underlying data generating mechanisms.
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies
This paper develops a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect, which can greatly facilitate the downstream analysis, including community detection, anomaly detection, and network inference.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups.
Target Confusion in End-to-end Speaker Extraction: Analysis and Approaches
However, its performance is often inferior to that of a blind source separation (BSS) counterpart with a similar network architecture, due to the auxiliary speaker encoder may sometimes generate ambiguous speaker embeddings.
Improving the Fairness of Chest X-ray Classifiers
We also find that methods which achieve group fairness do so by worsening performance for all groups.
Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel
A new expert data generation method, called Model Predictive Based Expert (MPBE) which combines Model Predictive Control and Deep Deterministic Policy Gradient, is developed to provide high quality supervision data for RLfD algorithms.
Learning Optimal Predictive Checklists
Our results show that our method can fit simple predictive checklists that perform well and that can easily be customized to obey a rich class of custom constraints.
Differentiable Projection for Constrained Deep Learning
In this paper, we propose to use a differentiable projection layer in DNN instead of directly solving time-consuming KKT conditions.
OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model.
An open GPS trajectory dataset and benchmark for travel mode detection
In this study, we propose and open GPS trajectory dataset marked with travel mode and benchmark for the travel mode detection.
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing
This systematic investigation underlines the importance of accounting for the underlying data-generating mechanisms and fortifying data-preprocessing pipelines with a causal framework to develop methods robust to confounding biases.
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality.
Invariance-based Multi-Clustering of Latent Space Embeddings for Equivariant Learning
Variational Autoencoders (VAEs) have been shown to be remarkably effective in recovering model latent spaces for several computer vision tasks.
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
An Empirical Framework for Domain Generalization in Clinical Settings
In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data.
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Machine learning models have been successfully used in many scientific and engineering fields.
Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation
The inner-word encoder uses the self-attention mechanisms to capture the inner-word features of the target word.
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data.
Active Learning Approaches to Enhancing Neural Machine Translation
Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models.
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring
While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks.
Word Embedding for Response-To-Text Assessment of Evidence
Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality.
Co-Attention Based Neural Network for Source-Dependent Essay Scoring
This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring.
eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
Writing a good essay typically involves students revising an initial paper draft after receiving feedback.
Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.
Applications
The Holdout Randomization Test for Feature Selection in Black Box Models
We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.
Methodology
