Paper introduces score embedding for Twitter sentiment analysis of health care issues.
problem Analyzing public opinion on health care plans using social media.
method Score embedding, a neural network model for word representations.
result Score embedding effectively captures sentiment and outperforms existing methods.
Model shows IRS procedure for health insurance tax credits can diverge, proposing a new bisection method.
problem IRS procedure for calculating health insurance tax credits diverges for some self-employed taxpayers.
method Proposed a bisection procedure to calculate appropriate premium tax credits for tax returns.
result The bisection procedure can calculate appropriate premium tax credits for a model of simple tax returns.
Model predicts wound and episode-level readmission risk and time to re-admit.
problem Identify patients at high risk of re-admission to prevent wound recurrences and reduce healthcare costs.
method Data-driven analysis of wound care and episode-level patient data.
result Model achieves high recall and precision for predicting re-admission risk and time.
HAL learns hierarchical affordances to prune impossible subtasks, improving reinforcement learning efficiency.
problem Reinforcement learning struggles with complex hierarchical dependency structures.
method HAL learns a model of hierarchical affordances to prune impossible subtasks.
result HAL agents are better at learning complex tasks, navigating stochastic environments, and acquiring diverse skills.
Proposes a fix for IRS calculation of Obamacare tax credits.
problem IRS iteration leads to divergent sequences for some self-employed taxpayers.
method Introduces a bisection procedure to calculate premium tax credits.
result Bisection procedure works for simple tax returns and those receiving credits in advance.
The paper introduces affordances for reinforcement learning, improving planning and learning efficiency.
problem Reinforcement learning assumes all actions are available, but real-world agents face limited action spaces.
method Developed a theory of affordances for Markov Decision Processes, proposing methods to learn and use affordances.
result Affordances improve planning speed and learning efficiency, leading to simpler and better generalizing transition models.
Model learns tool affordances from vision, enabling tool selection.
problem Learning tool affordances from visual input.
method Vision-based generative model with task predictor.
result Agents can select appropriate tools based on task success criteria.
One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactio…
EAML combines human and machine learning for better predictions.
problem Limited data and trust in machine learning models.
method Automated method guiding expert knowledge integration into machine models.
result Improved predictions and generalizability with less data.
We consider the Neumann Laplacian acting on square-integrable functions on a triangle in the hyperbolic plane that has one cusp. We show that the generic such triangle has no eigenvalues embedded in its continuous spectrum. To prove this result we study the behavior of the real-analytic eigenvalue branches of a degener…
Develops models for temporally abstract reasoning and attention.
problem Temporal abstraction and attention in reinforcement learning.
method Defines affordances for options and develops partial option models.
result Identifies trade-offs between estimation and approximation error.
Machine learning predicts circulatory failure in ICU patients.
problem Limited ability of clinicians to recognize early signs of patient deterioration.
method Developed an early warning system using machine learning on ICU data.
result Predicts 90.0% of circulatory failure events with 81.8% identified more than two hours in advance.
Novel RL method handles urban driving tasks including traffic lights.
problem Difficulties in reinforcement learning for complex urban driving tasks.
method Implicit affordances for model-free RL.
result Successfully handles urban driving tasks including traffic lights.
Collage-CNN reduces cloud inference latency by 1.47X with 9X reduced latency variation.
problem Reducing latency variance in cloud machine learning inference.
method Proposes a novel Collage-CNN model that combines multiple images for classification, providing redundancy and cost efficiency.
result Significant reduction in 99th percentile tail latency and variation in inference latency.
New PCGML approach generates novel game content across multiple platformer domains.
problem Generating novel game content in new domains.
method Using a new affordance and path vocabulary, variational autoencoders trained on data from six platformer games produce new content with varying proportions of different domains.
result Captures latent level space spanning multiple domains and generates new content with varying proportions of different domains.
This research synthesizes 12-lead ECG from a single-lead ECG device.
problem Limited cardiac diagnostics from single-lead ECG devices.
method Random forest machine learning model using historical 12-lead recordings.
result Synthesized 12-lead ECG with accuracies exceeding 90%.
Propagates adversarial robustness in federated learning.
problem Heterogeneous federated users with non-iid data and limited resources.
method Adversarial robustness propagation through batch-normalization.
result Federated models achieve remarkable robustness even with limited adversarial training.
New AI model optimizes personalized care for elderly residents.
problem Limited care workforce impacts health outcomes and quality of life.
method Bandit algorithms for personalized care planning.
result Improves care quality and health outcomes through personalized care planning.
Gradient Descent with small random initialization solves rank-1 matrix completion efficiently.
problem Matrix completion for rank-1 symmetric matrices.
method Gradient Descent with small random initialization.
result Gradient Descent converges to the ground truth for rank-1 symmetric matrix completion.
Let M be a closed, connected, orientable topological four-manifold with H1(M) nontrivial and free abelian, b2(M)=0,2, and χ(M)=0. We show that if G is a finite group of 2-rank ≤1 which admits a homologically trivial, locally linear, effective action on M, then G must be cyclic. With addition…
Deep RL controls anesthesia more accurately than traditional methods.
problem Controlling the level of unconsciousness during anesthesia.
method Deep Reinforcement Learning (DRL) to map patient state to propofol dosage.
result Deep RL model outperformed traditional controllers (1.7% vs 3.4% median absolute performance error).
The study uses machine learning to analyze office floor plans and predict function based on geometry.
problem Lack of formalisms to describe spatial affordance in automated floor-plan generation tools.
method Supervised and unsupervised data mining techniques, including J48 algorithm, were used to analyze office floor plans.
result J48 algorithm can predict class performance on unseen examples up to 79.5% for office dataset.
The paper presents a method to score patient engagement in care programs and predicts their response.
problem Improving health outcomes of high-need patients through better patient engagement.
method Data-driven behavioral engagement scoring pipeline for two aspects of patient engagement.
result The scoring method successfully predicts patient engagement and provides interpretable insights.
The study shows that certain manifolds with positive curvature cannot contain specific geometric structures.
problem The existence of certain geometric structures in manifolds with positive curvature.
method Careful study of the stability of minimal two spheres in manifolds of positive curvature.
result No element of the fundamental group reverses the orientation of a class in the second homotopy group.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
problem Impact of health care capacity on COVID-19 growth in Senegal.
method Compartmental model with logistic growth health care capacity, machine learning projection.
result Condition to avoid overwhelming health care system provided.
We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The mo…
Care2Vec uses deep learning to classify self-care problems in disabled children.
problem Accurate classification of self-care problems in physically disabled children.
method Care2Vec is a deep learning approach combining unsupervised and supervised learning using Autoencoders and Deep neural networks.
result Care2Vec outperforms traditional methods like Decision trees and Artificial neural networks in self-care classification.
Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a …
CARE improves LLM aggregation by accounting for shared confounders.
problem LLM judges' correlated errors due to shared latent confounders.
method CARE explicitly models judges' scores as true quality and confounders.
result CARE reduces aggregation error by up to 26.8% across various benchmarks.
We define a finite-dimensional cubic quotient of the group algebra of the braid group, endowed with a (essentially unique) Markov trace which affords the Links-Grould invariant of knots and links. We investigate several of its properties, and state several conjectures about its structure.
Public benchmark for machine learning models in critical care.
problem Lack of public benchmarks for machine learning in critical care.
method Defined four tasks (mortality prediction, length of stay, phenotyping, decompensation risk) and compared clinical and deep learning models on eICU dataset.
result First public benchmark on multi-centre critical care dataset, comparing clinical models with predictive models.
Paper develops new algorithms for unsupervised multi-class domain adaptation.
problem Improving performance of machine learning models across different domains without labeled data.
method Introduces MCSD divergence and a new domain adaptation bound, develops adversarial learning objectives, and proposes new algorithms like McDalNets and SymmNets.
result New algorithms improve performance across different domain adaptation settings.
Study on privacy-preserving health care models that sacrifice accuracy for data protection.
problem Privacy-preserving models in health care neglect data from the tails, reducing accuracy for small groups.
method Used state-of-the-art differentially private learning methods for clinical prediction tasks.
result Privacy-preserving models in health care exhibit steep tradeoffs between privacy and utility, and disproportionately influence large demographic groups.
Mathematical models help keep vaccine prices low.
problem Pricing COVID-19 vaccines to ensure affordability and profitability.
method Optimization and game theory approaches modeling a duopoly market.
result Government can negotiate low prices while manufacturers earn profits.
Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures include zero inflation, over-dispersion, and skewness, all of which complicate st…
System predicts HIV patients at risk of dropping out of care.
problem High non-adherence and dropout rates among HIV patients.
method Predictive machine learning model based on resource constraints, stability, and fairness.
result Model performs 3x better than baseline for clinical use and 2.3x better for city-wide use.
This work trains a model to predict human driving directions from road scenes.
problem Defining implicit rules of human behavior for autonomous vehicles.
method Self-supervised learning of probabilistic network model.
result Model successfully generalizes to new road scenes.
New fair regression methods improve health care spending predictions for undercompensated groups.
problem Current risk adjustment formulas underpredict spending for specific health groups, leading to unfair compensation.
method Developed new fair regression methods by integrating fairness considerations into the objective function.
result New methods lead to significant improvements in fairness (98%) with minimal impact on overall fit (4%).
Estimates causal effect of managed care plans on NYC Medicaid spending.
problem Generalizing causal estimates to a target population not well-represented by randomized studies.
method Conditional cross-design synthesis estimators combining randomized and observational data.
result Estimates causal effect of managed care plans on health care spending.
LHIEM model predicts health, income, and employment over years.
problem Lack of path dependency in health policy simulations.
method Discrete-time microsimulation with Markov chain modules.
result Validates health care financing proposal through detailed modeling.
We present a survey of the calibrated geometries arising in the study of the local singularity structure of supersymmetric fivebranes in M-theory. We pay particular attention to the geometries of 4-planes in eight dimensions, for which we present some new results as well as many details of the computations. We also ana…
Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potenti…
We consider composite loss functions for multiclass prediction comprising a proper (i.e., Fisher-consistent) loss over probability distributions and an inverse link function. We establish conditions for their (strong) convexity and explore the implications. We also show how the separation of concerns afforded by using …
AdvImmune improves certifiable robustness of GNNs against adversarial attacks.
problem Vulnerability of graph neural networks to adversarial attacks.
method Proposes AdvImmune, an algorithm that optimizes with meta-gradient to improve certifiable robustness.
result Remarkably improves the ratio of robust nodes by 12%, 42%, 65% with an affordable immune budget of only 5% edges.
Characterizes Fredholm conditions for group-invariant pseudodifferential operators.
problem Understanding Fredholm conditions for operators on quotient spaces.
method Study of symbol C∗-algebra and topology of primitive ideal spectrum. result Explicit characterization of Fredholm operators in terms of principal symbol and group action.
LoAdaBoost boosts federated machine learning efficiency for ICU data.
problem Different data distributions in federated machine learning.
method Loss-based AdaBoost method for federated learning.
result Higher predictive accuracy with lower computational complexity.
Graph-based methods pervade the inference toolkits of numerous disciplines including sociology, biology, neuroscience, physics, chemistry, and engineering. A challenging problem encountered in this context pertains to determining the attributes of a set of vertices given those of another subset at possibly different ti…
This work aims to create a large-scale model for critical care time series data.
problem Lack of large-scale datasets and distribution shifts in critical care time series data.
method Harmonized dataset creation and transfer learning research.
result Established a foundation for large-scale multi-variate time series models in critical care.