Emma Brunskill

Bio: Emma Brunskill is an academic researcher from Stanford University. The author has contributed to research in topics: Reinforcement learning & Markov decision process. The author has an hindex of 38, co-authored 199 publications receiving 5693 citations. Previous affiliations of Emma Brunskill include Carnegie Mellon University & Massachusetts Institute of Technology.

Global and regional hearing impairment prevalence: an analysis of 42 studies in 29 countries

Abstract: Background: Hearing impairment is a leading cause of disease burden, yet population-based studies that measure hearing impairment are rare. We estimate regional and global hearing impairment prevalence from sparse data and calculate corresponding uncertainty intervals. Methods: We accessed papers from a published literature review and obtained additional detailed data tabulations from investigators. We estimated the prevalence of hearing impairment by region, sex, age and hearing level using a Bayesian hierarchical model, a method that is effective for sparse data. As the primary objective of modelling was to produce regional and global prevalence estimates, including for those regions with scarce to no data, models were evaluated using cross-validation. Results: We used data from 42 studies, carried out between 1973 and 2010 in 29 countries. Hearing impairment was positively related to age, male sex and middle- and low-income regions. We estimated that the global prevalence of hearing impairment (defined as an average hearing level of 35 decibels or more in the better ear) in 2008 was 1.4% (95% uncertainty interval 1.0–2.2%) for children aged 5–14 years, 9.8% (7.7–13.2%) for females >15 years of age and 12.2% (9.7–16.2%) for males >15 years of age. The model exhibited good external validity in the cross-validation analysis, with 87% of survey estimates falling within our final model's 95% uncertainty intervals. Conclusion: Our results suggest that the prevalence of child and adult hearing impairment is substantially higher in middle- and low-income countries than in high-income countries, demonstrating the global need for attention to hearing impairment.

Data-efficient off-policy policy evaluation for reinforcement learning

Abstract: In this paper we present a new way of predicting the performance of a reinforcement learning policy given historical data that may have been generated by a different policy. The ability to evaluate a policy from historical data is important for applications where the deployment of a bad policy can be dangerous or costly. We show empirically that our algorithm produces estimates that often have orders of magnitude lower mean squared error than existing methods--it makes more efficient use of the available data. Our new estimator is based on two advances: an extension of the doubly robust estimator (Jiang & Li, 2015), and a new way to mix between model based and importance sampling based estimates.

Building peer-to-peer systems with chord, a distributed lookup service

Abstract: We argue that the core problem facing peer-to-peer Systems is locating documents in a decentralized network and propose Chord, a distributed lookup primitive Chord provides an efficient method of locating documents while placing few constraints on the applications that use it As proof that Chord's functionality is useful in the development of peer-to-peer applications, we outline the implementation of a peer-to-peer file sharing system based on Chord

Designing mobile interfaces for novice and low-literacy users

Abstract: While mobile phones have found broad application in bringing health, financial, and other services to the developing world, usability remains a major hurdle for novice and low-literacy populations. In this article, we take two steps to evaluate and improve the usability of mobile interfaces for such users. First, we offer an ethnographic study of the usability barriers facing 90 low-literacy subjects in India, Kenya, the Philippines, and South Africa. Then, via two studies involving over 70 subjects in India, we quantitatively compare the usability of different points in the mobile design space. In addition to text interfaces such as electronic forms, SMS, and USSD, we consider three text-free interfaces: a spoken dialog system, a graphical interface, and a live operator.Our results confirm that textual interfaces are unusable by first-time low-literacy users, and error prone for literate but novice users. In the context of healthcare, we find that a live operator is up to ten times more accurate than text-based interfaces, and can also be cost effective in countries such as India. In the context of mobile banking, we find that task completion is highest with a graphical interface, but those who understand the spoken dialog system can use it more quickly due to their comfort and familiarity with speech. We synthesize our findings into a set of design recommendations.

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Abstract: Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.

Chord: A scalable peer-to-peer lookup service for internet applications

Abstract: A fundamental problem that confronts peer-to-peer applications is to efficiently locate the node that stores a particular data item. This paper presents Chord, a distributed lookup protocol that addresses this problem. Chord provides support for just one operation: given a key, it maps the key onto a node. Data location can be easily implemented on top of Chord by associating a key with each data item, and storing the key/data item pair at the node to which the key maps. Chord adapts efficiently as nodes join and leave the system, and can answer queries even if the system is continuously changing. Results from theoretical analysis, simulations, and experiments show that Chord is scalable, with communication cost and the state maintained by each node scaling logarithmically with the number of Chord nodes.

Bayesian Reinforcement Learning in Factored POMDPs

Abstract: Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the exploration-exploitation trade-off, but such methods typically assume a fully observable environments. The few Bayesian RL methods that are applicable in partially observable domains, such as the Bayes-Adaptive POMDP (BA-POMDP), scale poorly. To address this issue, we introduce the Factored BA-POMDP model (FBA-POMDP), a framework that is able to learn a compact model of the dynamics by exploiting the underlying structure of a POMDP. The FBA-POMDP framework casts the problem as a planning task, for which we adapt the Monte-Carlo Tree Search planning algorithm and develop a belief tracking method to approximate the joint posterior over the state and model variables. Our empirical results show that this method outperforms a number of BRL baselines and is able to learn efficiently when the factorization is known, as well as learn both the factorization and the model parameters simultaneously.

Hearing loss and cognitive decline in older adults

Abstract: Background:Whetherhearinglossisindependentlyassociatedwithacceleratedcognitivedeclineinolderadults is unknown. Methods:Westudied1984olderadults(meanage,77.4 years) enrolled in the Health ABC Study, a prospective observational study begun in 1997-1998. Our baseline cohort consisted of participants without prevalent cognitive impairment (Modified Mini-Mental State Examination [3MS] score, 80) who underwent audiometric testinginyear5.Participantswerefollowedupfor6years. Hearing was defined at baseline using a pure-tone average of thresholds at 0.5 to 4 kHz in the better-hearing ear. Cognitive testing was performed in years 5, 8, 10, and11andconsistedofthe3MS(measuringglobalfunction) and the Digit Symbol Substitution test (measuring executive function). Incident cognitive impairment was definedasa3MSscoreoflessthan80oradeclinein3MS score of more than 5 points from baseline. Mixedeffects regression and Cox proportional hazards regression models were adjusted for demographic and cardiovascular risk factors. Results: In total, 1162 individuals with baseline hearing loss (pure-tone average 25 dB) had annual rates of declinein3MSandDigitSymbolSubstitutiontestscoresthat were41%and32%greater,respectively,thanthoseamong individuals with normal hearing. On the 3MS, the annual score changes were 0.65 (95% CI, 0.73 to 0.56) vs 0.46(95%CI,0.55to0.36)pointsperyear(P=.004). On the Digit Symbol Substitution test, the annual score changes were 0.83 (95% CI, 0.94 to 0.73) vs 0.63 (95% CI, 0.75 to 0.51) points per year (P=.02). Comparedtothosewithnormalhearing,individualswithhearing loss at baseline had a 24% (hazard ratio, 1.24; 95% CI, 1.05-1.48) increased risk for incident cognitive impairment. Rates of cognitive decline and the risk for incident cognitiveimpairmentwerelinearlyassociatedwiththeseverity of an individual’s baseline hearing loss.