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  1. Book ; Online: Participatory Personalization in Classification

    Joren, Hailey / Nagpal, Chirag / Heller, Katherine / Ustun, Berk

    2023  

    Abstract: Machine learning models are often personalized with information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people but do not facilitate nor inform their consent. Individuals cannot opt out of ... ...

    Abstract Machine learning models are often personalized with information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people but do not facilitate nor inform their consent. Individuals cannot opt out of reporting personal information to a model, nor tell if they benefit from personalization in the first place. We introduce a family of classification models, called participatory systems, that let individuals opt into personalization at prediction time. We present a model-agnostic algorithm to learn participatory systems for personalization with categorical group attributes. We conduct a comprehensive empirical study of participatory systems in clinical prediction tasks, benchmarking them with common approaches for personalization and imputation. Our results demonstrate that participatory systems can facilitate and inform consent while improving performance and data use across all groups who report personal data.

    Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
    Keywords Computer Science - Machine Learning ; Computer Science - Computers and Society
    Subject code 006
    Publishing date 2023-02-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: STUDY

    Ahmed, Eltayeb / Mincu, Diana / Harrell, Lauren / Heller, Katherine / Roy, Subhrajit

    Socially Aware Temporally Causal Decoder Recommender Systems

    2023  

    Abstract: Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to use social network information effectively in recommender systems. This is ...

    Abstract Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to use social network information effectively in recommender systems. This is especially true for demographic groups with interests that differ from the majority. This paper introduces STUDY, a Socially-aware Temporally caUsal Decoder recommender sYstem. STUDY introduces a new socially-aware recommender system architecture that is significantly more efficient to learn and train than existing methods. STUDY performs joint inference over socially connected groups in a single forward pass of a modified transformer decoder network. We demonstrate the benefits of STUDY in the recommendation of books for students who are dyslexic, or struggling readers. Dyslexic students often have difficulty engaging with reading material, making it critical to recommend books that are tailored to their interests. We worked with our non-profit partner Learning Ally to evaluate STUDY on a dataset of struggling readers. STUDY was able to generate recommendations that more accurately predicted student engagement, when compared with existing methods.

    Comment: 15 pages, 5 figures
    Keywords Computer Science - Social and Information Networks ; Computer Science - Artificial Intelligence ; Computer Science - Information Retrieval
    Subject code 006
    Publishing date 2023-06-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Analysis of SIR epidemic models with sociological phenomenon

    Allen, Robert F. / Heller, Katherine / Pons, Matthew A.

    2022  

    Abstract: We propose two SIR models which incorporate sociological behavior of groups of individuals. It is these differences in behaviors which impose different infection rates on the individual susceptible populations, rather than biological differences. We ... ...

    Abstract We propose two SIR models which incorporate sociological behavior of groups of individuals. It is these differences in behaviors which impose different infection rates on the individual susceptible populations, rather than biological differences. We compute the basic reproduction number for each model, as well as analyze the sensitivity of $R_0$ to changes in sociological parameter values.
    Keywords Mathematics - Dynamical Systems ; Physics - Physics and Society ; Quantitative Biology - Populations and Evolution ; primary: 92D30 ; secondary: 92B05
    Publishing date 2022-07-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: REFRESH: A new approach to modeling dimensional biases in perceptual similarity and categorization.

    Sanborn, Adam N / Heller, Katherine / Austerweil, Joseph L / Chater, Nick

    Psychological review

    2021  Volume 128, Issue 6, Page(s) 1145–1186

    Abstract: Much categorization behavior can be explained by family resemblance: New items are classified by comparison with previously learned exemplars. However, categorization behavior also shows a variety of dimensional biases, where the underlying space has so- ... ...

    Abstract Much categorization behavior can be explained by family resemblance: New items are classified by comparison with previously learned exemplars. However, categorization behavior also shows a variety of dimensional biases, where the underlying space has so-called "separable" dimensions: Ease of learning categories depends on how the stimuli align with the separable dimensions of the space. For example, if a set of objects of various sizes and colors can be accurately categorized using a single separable dimension (e.g., size), then category learning will be fast, while if the category is determined by both dimensions, learning will be slow. To capture these dimensional biases, almost all models of categorization supplement family resemblance with either rule-based systems or selective attention to separable dimensions. But these models do not explain how separable dimensions initially arise; they are presumed to be unexplained psychological primitives. We develop, instead, a pure family resemblance version of the Rational Model of Categorization (RMC), which we term the Rational Exclusively Family RESemblance Hierarchy (REFRESH), which does not presuppose any separable dimensions in the space of stimuli. REFRESH infers how the stimuli are clustered and uses a hierarchical prior to learn expectations about the variability of clusters across categories. We first demonstrate the dimensional alignment of natural-category features and then show how through a lifetime of categorization experience REFRESH will learn prior expectations that clusters of stimuli will align with separable dimensions. REFRESH captures the key dimensional biases and also explains their stimulus-dependence and how they are learned and develop. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
    MeSH term(s) Bias ; Concept Formation ; Humans ; Learning
    Language English
    Publishing date 2021-09-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 209907-x
    ISSN 1939-1471 ; 0033-295X
    ISSN (online) 1939-1471
    ISSN 0033-295X
    DOI 10.1037/rev0000310
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Nonoperating room anesthesia: strategies to improve performance.

    Anwar, Anjum / Heller, Katherine O / Esper, Stephen A / Ferreira, Renata G

    International anesthesiology clinics

    2021  Volume 59, Issue 4, Page(s) 27–36

    MeSH term(s) Anesthesia ; Anesthesiology ; Humans
    Language English
    Publishing date 2021-08-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 210757-0
    ISSN 1537-1913 ; 0020-5907
    ISSN (online) 1537-1913
    ISSN 0020-5907
    DOI 10.1097/AIA.0000000000000339
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 243: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  6. Book ; Online: Evaluation Gaps in Machine Learning Practice

    Hutchinson, Ben / Rostamzadeh, Negar / Greer, Christina / Heller, Katherine / Prabhakaran, Vinodkumar

    2022  

    Abstract: Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad range of factors including harms, benefits, and responsibilities. In ... ...

    Abstract Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad range of factors including harms, benefits, and responsibilities. In practice, however, evaluations of ML models frequently focus on only a narrow range of decontextualized predictive behaviours. We examine the evaluation gaps between the idealized breadth of evaluation concerns and the observed narrow focus of actual evaluations. Through an empirical study of papers from recent high-profile conferences in the Computer Vision and Natural Language Processing communities, we demonstrate a general focus on a handful of evaluation methods. By considering the metrics and test data distributions used in these methods, we draw attention to which properties of models are centered in the field, revealing the properties that are frequently neglected or sidelined during evaluation. By studying these properties, we demonstrate the machine learning discipline's implicit assumption of a range of commitments which have normative impacts; these include commitments to consequentialism, abstractability from context, the quantifiability of impacts, the limited role of model inputs in evaluation, and the equivalence of different failure modes. Shedding light on these assumptions enables us to question their appropriateness for ML system contexts, pointing the way towards more contextualized evaluation methodologies for robustly examining the trustworthiness of ML models
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-05-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Deep Cox Mixtures for Survival Regression

    Nagpal, Chirag / Yadlowsky, Steve / Rostamzadeh, Negar / Heller, Katherine

    2021  

    Abstract: Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical ... ...

    Abstract Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics.

    Comment: Machine Learning for Healthcare Conference, 2021
    Keywords Computer Science - Machine Learning ; Statistics - Methodology ; Statistics - Machine Learning
    Subject code 310 ; 006
    Publishing date 2021-01-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression

    Norcliffe, Alexander / Proleev, Lev / Mincu, Diana / Hartsell, Fletcher Lee / Heller, Katherine / Roy, Subhrajit

    2023  

    Abstract: Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for multiple sclerosis has been centered around using Magnetic Resonance ... ...

    Abstract Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for multiple sclerosis has been centered around using Magnetic Resonance Imaging scans or laboratory tests; these modalities are both expensive to acquire and can be unreliable. In a recent paper it was shown that disease progression can be predicted effectively using performance outcome measures and demographic data. In our work we build on this to investigate the modeling side, using continuous time models to predict progression. We benchmark four continuous time models using a publicly available multiple sclerosis dataset. We find that the best continuous model is often able to outperform the best benchmarked discrete time model. We also carry out an extensive ablation to discover the sources of performance gains, we find that standardizing existing features leads to a larger performance increase than interpolating missing features.

    Comment: 32 pages, 2 figures, 17 tables, published in TMLR 2023
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-02-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Globalizing Fairness Attributes in Machine Learning

    Asiedu, Mercy Nyamewaa / Dieng, Awa / Oppong, Abigail / Nagawa, Maria / Koyejo, Sanmi / Heller, Katherine

    A Case Study on Health in Africa

    2023  

    Abstract: With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has ... ...

    Abstract With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study. We propose fairness attributes for consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. This work serves as a basis and call for action for furthering research into fairness in global health.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
    Publishing date 2023-04-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Batch Calibration

    Zhou, Han / Wan, Xingchen / Proleev, Lev / Mincu, Diana / Chen, Jilin / Heller, Katherine / Roy, Subhrajit

    Rethinking Calibration for In-Context Learning and Prompt Engineering

    2023  

    Abstract: Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias factors in the prompt, including but not limited to the formatting, the ... ...

    Abstract Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias factors in the prompt, including but not limited to the formatting, the choice verbalizers, and the ICL examples. To address this problem that results in unexpected performance degradation, calibration methods have been developed to mitigate the effects of these biases while recovering LLM performance. In this work, we first conduct a systematic analysis of the existing calibration methods, where we both provide a unified view and reveal the failure cases. Inspired by these analyses, we propose Batch Calibration (BC), a simple yet intuitive method that controls the contextual bias from the batched input, unifies various prior approaches, and effectively addresses the aforementioned issues. BC is zero-shot, inference-only, and incurs negligible additional costs. In the few-shot setup, we further extend BC to allow it to learn the contextual bias from labeled data. We validate the effectiveness of BC with PaLM 2-(S, M, L) and CLIP models and demonstrate state-of-the-art performance over previous calibration baselines across more than 10 natural language understanding and image classification tasks.

    Comment: 21 pages, 10 figures, 10 tables
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 004
    Publishing date 2023-09-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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