Combining Prior Knowledge and Data: Beyond the Bayesian Framework

For many tasks such as text categorization and control of robotic systems, state-of-the art learning systems can produce results comparable in accuracy to those of human subjects. However, the amount of training data needed for such systems can be prohibitively large for many practical problems. A text categorization system, for example, may need to see many text postings manually tagged with their subjects before it learns to predict the subject of the next posting with high accuracy. A reinforcement learning (RL) system learning how to drive a car needs a lot of experimentation with the actual car before acquiring the optimal policy. An optimizing compiler targeting a certain platform has to construct, compile, and execute many versions of the same code with different optimization parameters to determine which optimizations work best. Such extensive sampling can be time-consuming, expensive (in terms of both expense of the human expertise needed to label data and wear and tear on the robotic equipment used for exploration in case of RL), and sometimes dangerous (e.g., an RL agent driving the car off the cliff to see if it survives the crash). The goal of this work is to reduce the amount of training data an agent needs in order to learn how to perform a task successfully. This is done by providing the system with prior knowledge about its domain. The knowledge is used to bias the agent towards useful solutions and limit the amount of training needed. We explore this task in three contexts: classification (determining the subject of a newsgroup posting), control (learning to perform tasks such as driving a car up the mountain in simulation), and optimization (optimizing performance of linear algebra operations on different hardware platforms). For the text categorization problem, we introduce a novel algorithm which efficiently integrates prior knowledge into large margin classification. We show that prior knowledge simplifies the problem by reducing the size of the hypothesis space. We also provide formal convergence guarantees for our algorithm. For reinforcement learning, we introduce a novel framework for defining planning problems in terms of qualitative statements about the world (e.g., ``the faster the car is going, the more likely it is to reach the top of the mountain''). We present an algorithm based on policy iteration for solving such qualitative problems and prove its convergence. We also present an alternative framework which allows the user to specify prior knowledge quantitatively in form of a Markov Decision Process (MDP). This prior is used to focus exploration on those regions of the world in which the optimal policy is most sensitive to perturbations in transition probabilities and rewards. Finally, in the compiler optimization problem, the prior is based on an analytic model which determines good optimization parameters for a given platform. This model defines a Bayesian prior which, combined with empirical samples (obtained by measuring the performance of optimized code segments), determines the maximum-a-posteriori estimate of the optimization parameters

BERT for Long Documents: A Case Study of Automated ICD Coding

Transformer models have achieved great success across many NLP problems. However, previous studies in automated ICD coding concluded that these models fail to outperform some of the earlier solutions such as CNN-based models. In this paper we challenge this conclusion. We present a simple and scalable method to process long text with the existing transformer models such as BERT. We show that this method significantly improves the previous results reported for transformer models in ICD coding, and is able to outperform one of the prominent CNN-based methods

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Combining Prior Knowledge and Data: Beyond the Bayesian Framework

114 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.We explore this task in three contexts: classification (determining the subject of a newsgroup posting), control (learning to perform tasks such as driving a car up a mountain in simulation), and optimization (optimizing performance of linear algebra operations on different hardware platforms). For the text categorization problem, we introduce a novel algorithm which efficiently integrates prior knowledge into large margin classification. For reinforcement learning, we introduce a novel framework for defining and solving planning problems in terms of qualitative statements about the world. In compiler optimization, Bayesian prior based on an analytic model of hardware is combined with empirical measurements of performance of optimized code to determine the maximum-a-posteriori estimates of the optimization parameters.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Rotational prior knowledge for SVMs

Abstract. Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear support vector machines in form of constraints on the rotation of the normal to the separating hyperplane. Such knowledge frequently arises naturally, e.g., as inhibitory and excitatory influences of input variables. We demonstrate that the generalization ability of rotationally-constrained classifiers is improved by analyzing their VC and fat-shattering dimensions. Interestingly, the analysis shows that large-margin classification framework justifies the use of stronger prior knowledge than the traditional VC framework. Empirical experiments with text categorization and political party affiliation prediction confirm the usefulness of rotational prior knowledge.

MODULAR SOFT COMPUTING APPROACH FOR AIRCRAFT CARRIER LANDING TRAJECTORY PREDICTION

A modular learning design for classifying aircraft flight data in time-series prediction is proposed in this paper. This is part of the decision support system to assist landing signal officers in guiding aircraft to land on aircraft carriers. NeuroFuzzy systems are used to emulate the flight patterns for future real-time flight prediction. To improve the learning efficiency, a two stage modular learning design is proposed. The data to be learned is first decomposed into categories in accordance to their physical structure. Each module of data is presented to a different NeuroFuzzy system for learning purpose. Individually trained modules are modeled as genetic chromosomes. Genetic algorithm is used to produce a chromosome module that represents a generalization of all the trained modules. As compared with the non-modular approach, the modular approach offers comparable prediction performance with significantly lower overall computation time. We show that the reduction in computation time with the modular approach is exponential as the problem size increases. Navy aircraft data were used to validate the effectiveness of the modular design and the result is consistent and promising.Aircraft trajectory, genetic algorithm, modular design, neurofuzzy system

Generative Prior Knowledge for Discriminative Classification

We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs) to utilize prior knowledge specified in the generative setting. The dual objective of fitting the data and respecting prior knowledge is formulated as a bilevel program, which is solved (approximately) via iterative application of second-order cone programming. To test our approach, we consider the problem of using WordNet (a semantic database of English language) to improve low-sample classification accuracy of newsgroup categorization. WordNet is viewed as an approximate, but readily available source of background knowledge, and our framework is capable of utilizing it in a flexible way. 1

Qualitative Reinforcement Learning

When the transition probabilities and rewards of a Markov Decision Process are specified exactly, the problem can be solved without any interaction with the environment. When no such specification is available, the agent’s only recourse is a long and potentially dangerous exploration. We present a framework which allows the expert to specify imprecise knowledge of transition probabilities in terms of stochastic dominance constraints. Our algorithm can be used to find optimal policies for qualitatively specified problems, or, when no such solution is available, to decrease the required amount of exploration. The algorithm’s behavior is demonstrated on simulations of two classic problems: mountain car ascent and cart pole balancing. 1