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Learning from Sequential User Data: Models and Sample-efficient Algorithms
Recent advances in deep learning have made learning representation from ever-growing datasets possible in the domain of vision, natural language processing (NLP), and robotics, among others. However, deep networks are notoriously data-hungry; for example, training language models with attention mechanisms sometimes requires trillions of parameters and tokens. In contrast, we can often access a limited number of samples in many tasks. It is crucial to learn models from these `limited\u27 datasets. Learning with limited datasets can take several forms. In this thesis, we study how to select data samples sequentially such that downstream task performance is maximized. Moreover, we study how to introduce prior knowledge in the deep networks to maximize prediction performance. We focus on four sequential tasks: computerized adaptive testing in psychometrics, sketching in recommender systems, knowledge tracing in computer-assisted education, and career path modeling in the labor market.
In the first two tasks, we devise novel sample-efficient algorithms to query a minimal number of sequential samples to improve future predictions. We propose a Bilevel Optimization-Based framework for computerized adaptive testing to learn a data-driven question selection algorithm that improves existing data selection policies. We also tackle the sketching problem in the recommender system, with the task of recommending the next item using a stored subset of prior data samples. In this setting, we develop a data-driven sequential selection algorithm that tackles evolving downstream task distribution. In the last two tasks, we devise novel neural models to introduce prior knowledge exploiting limited data samples. For knowledge tracing, we propose a novel neural architecture, inspired by cognitive and psychometric models, to improve the prediction of students\u27 future performance and utilize the labeled data samples efficiently. For career path modeling, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals.
The data-driven differentiable data selection algorithms for the first two tasks open up future directions to query (a non-differentiable operation) a minimal number of samples optimally to maximize prediction performance. The structures, introduced in the neural architecture for the models in the last two tasks using prior knowledge, open up future directions to learn deep models augmented with prior knowledge using limited data samples
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Industrial recommender systems face the challenge of operating in
non-stationary environments, where data distribution shifts arise from evolving
user behaviors over time. To tackle this challenge, a common approach is to
periodically re-train or incrementally update deployed deep models with newly
observed data, resulting in a continual training process. However, the
conventional learning paradigm of neural networks relies on iterative
gradient-based updates with a small learning rate, making it slow for large
recommendation models to adapt. In this paper, we introduce ReLoop2, a
self-correcting learning loop that facilitates fast model adaptation in online
recommender systems through responsive error compensation. Inspired by the
slow-fast complementary learning system observed in human brains, we propose an
error memory module that directly stores error samples from incoming data
streams. These stored samples are subsequently leveraged to compensate for
model prediction errors during testing, particularly under distribution shifts.
The error memory module is designed with fast access capabilities and undergoes
continual refreshing with newly observed data samples during the model serving
phase to support fast model adaptation. We evaluate the effectiveness of
ReLoop2 on three open benchmark datasets as well as a real-world production
dataset. The results demonstrate the potential of ReLoop2 in enhancing the
responsiveness and adaptiveness of recommender systems operating in
non-stationary environments.Comment: Accepted by KDD 2023. See the project page at
https://xpai.github.io/ReLoo
Analyzing library collections with starfield visualizations
This paper presents a qualitative and formative study of the uses of a starfield-based visualization interface for analysis of library collections. The evaluation process has produced feedback that suggests ways to significantly improve starfield interfaces and the interaction process to improve their learnability and usability. The study also gave us clear indication of additional potential uses of starfield visualizations that can be exploited by further functionality and interface development.
We report on resulting implications for the design and use of starfield visualizations that will impact their graphical interface features, their use for managing data quality and their potential for various forms of visual data mining. Although the current implementation and analysis focuses on the collection of a physical library, the most important contributions of our work will be in digital libraries, in which volume, complexity and dynamism of collections are increasing dramatically and tools are needed for visualization and analysis
Experiences with starfield visualizations for analysis of library collections
This paper presents a qualitative and formative study of the uses of a starfield-based visualization interface for analysis of library collections. The evaluation process has produced feedback that suggests ways to significantly improve starfield interfaces and the interaction process to improve their learnability and usability. The study also gave us clear indication of additional potential uses of starfield visualizations that can be exploited by further functionality and interface development. We report on resulting implications for the design and use of starfield visualizations that will impact their graphical interface features, their use for managing data quality and their potential for various forms of visual data mining. Although the current implementation and analysis focuses on the collection of a physical library, the most important contributions of our work will be in digital libraries, in which volume, complexity and dynamism of collections are increasing dramatically and tools are needed for visualization and analysis
Identifying Correlated Heavy-Hitters in a Two-Dimensional Data Stream
We consider online mining of correlated heavy-hitters from a data stream.
Given a stream of two-dimensional data, a correlated aggregate query first
extracts a substream by applying a predicate along a primary dimension, and
then computes an aggregate along a secondary dimension. Prior work on
identifying heavy-hitters in streams has almost exclusively focused on
identifying heavy-hitters on a single dimensional stream, and these yield
little insight into the properties of heavy-hitters along other dimensions. In
typical applications however, an analyst is interested not only in identifying
heavy-hitters, but also in understanding further properties such as: what other
items appear frequently along with a heavy-hitter, or what is the frequency
distribution of items that appear along with the heavy-hitters. We consider
queries of the following form: In a stream S of (x, y) tuples, on the substream
H of all x values that are heavy-hitters, maintain those y values that occur
frequently with the x values in H. We call this problem as Correlated
Heavy-Hitters (CHH). We formulate an approximate formulation of CHH
identification, and present an algorithm for tracking CHHs on a data stream.
The algorithm is easy to implement and uses workspace which is orders of
magnitude smaller than the stream itself. We present provable guarantees on the
maximum error, as well as detailed experimental results that demonstrate the
space-accuracy trade-off
์๊ฐํ ์ด์ฌ์์๊ฒ ์๊ฐ์ ๋น๊ต๋ฅผ ๋๋ ์ ๋ณด ์๊ฐํ ๊ธฐ์ ์ ๋์์ธ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ์์ง์ฑ.The visual comparison is one of the fundamental tasks in information visualization (InfoVis) that enables people to organize, evaluate, and combine information fragmented in visualizations. For example, people perform visual comparison tasks to compare data over time, from different sources, or with different analytic models. While the InfoVis community has focused on understanding the effectiveness of different visualization designs for supporting visual comparison tasks, it is still unclear how to design effective comparative visualizations due to several limitations: (1) Empirical findings and practical implications from those studies are fragmented, and (2) we lack user studies that directly investigated the effectiveness of different visualization designs for visual comparison.
In this dissertation, we present the results of three studies to build our knowledge on how to support effective visual comparison to InfoVis novicesโ โgeneral people who are not familiar with visual representations and visual data exploration process. Identifying the major stages in the visualization construction process where novices confront challenges with visual comparison tasks, we explored two high-level comparison tasks with actual users: comparing visual mapping (encoding barrier) and comparing information (interpretation barrier) in visualizations. First, we conducted a systematical literature review on research papers (N = 104) that focused on supporting visual comparison tasks to gather and organize the practical insights that researchers gained in the wild. From this study, we offered implications for designing comparative visualizations, such as actionable guidelines, as well as the lucid categorization of comparative designs which can help researchers explore the design space. In the second study, we performed a qualitative user study (N = 24) to investigate how novices compare and understand visual mapping suggested in a visual-encoding recommendation interface. Based on the study, we present novices' main challenges in using visual encoding recommendations and design implications as remedies. In the third study, we conducted a design study in the area on bioinformatics to design and implement a visual analytics tool, XCluSim, that helps users to compare multiple clustering results. Case studies with a bioinformatician showed that our system enables analysts to easily evaluate the quality of a large number of clustering results. Based on the results of three studies in this dissertation, we suggest a future research agenda, such as designing recommendations for visual comparison and distinguishing InfoVis novices from experts.์๊ฐ์ ๋น๊ต๋ ์ ๋ณด ์๊ฐํ๋ฅผ ์ด์ฉํ ํต์ฌ์ ์ธ ๋ฐ์ดํฐ ๋ถ์ ๊ณผ์ ์ค ํ๋๋ก์จ, ๋ถ์ฐ๋์ด ์๋ ์ ๋ณด๋ค์ ์ฌ๋๋ค์ด ์๋ก ์ ๋ฆฌ, ํ๊ฐ, ๋ณํฉํ ์ ์๋๋ก ๋๋๋ค. ์๋ฅผ ๋ค์ด, ์ฌ๋๋ค์ ์๊ฐ์ ํ๋ฆ์ ๋ฐ๋ฅธ ๋ฐ์ดํฐ์ ๋ณํ๋ฅผ ๋ณด๊ฑฐ๋, ์๋ก ๋ค๋ฅธ ์ถ์ฒ์ ๋ฐ์ดํฐ๋ฅผ ๋น๊ตํ๊ฑฐ๋, ๊ฐ์ ๋ฐ์ดํฐ๋ฅผ ์ฌ๋ฌ ๋ถ์ ๋ชจ๋ธ๋ค์ ์ด์ฉํด ํ๊ฐํ๊ธฐ ์ํด ์๊ฐ์ ๋น๊ต ๊ณผ์
์ ํํ ์ํํ๊ฒ ๋๋ค. ํจ๊ณผ์ ์ธ ์๊ฐํ ๋์์ธ์ ์ํ ์ฌ๋ฌ ์ฐ๊ตฌ๊ฐ ์ ๋ณด ์๊ฐํ ๋ถ์ผ์์ ์ด๋ฃจ์ด์ง๊ณ ์๋ ๋ฐ๋ฉด, ์ด๋ค ๋์์ธ์ ํตํด ํจ๊ณผ์ ์ผ๋ก ์๊ฐ์ ๋น๊ต๋ฅผ ์ง์ํ ์ ์๋์ง์ ๋ํ ์ดํด๋ ๋ค์์ ์ ์ฝ๋ค๋ก ์ธํด ์์ง๊น์ง ๋ถ๋ถ๋ช
ํ๋ค. (1) ๊ฒฝํ์ ํต์ฐฐ๋ค๊ณผ ์ค์ฉ์ ์ค๊ณ ์ง์นจ๋ค์ด ํํธํ๋์ด ์์ผ๋ฉฐ (2) ๋น๊ต ์๊ฐํ๋ฅผ ์ง์ํ๋ ๋ฐฉ๋ฒ์ ์ดํดํ๊ธฐ ์ํ ์ฌ์ฉ์ ์คํ์ ์๊ฐ ์ฌ์ ํ ์ ํ์ ์ด๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์๊ฐํ ์ด์ฌ์๋ค์๊ฒ ํจ๊ณผ์ ์ผ๋ก ์๊ฐ์ ๋น๊ต๋ฅผ ์ง์ํ๊ธฐ ์ํ ์ ๋ณด ์๊ฐํ ๋์์ธ ๋ฐฉ๋ฒ์ ๋ ๊น์ด ์ดํดํ๊ธฐ ์ํด์ ์ผ๋ จ์ ์ธ ์ฐ๊ตฌ๋ฅผ ์งํํ๊ณ ์ด์ ๋ํ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ๋ค. ํน๋ณํ, ์๊ฐํ ์ด์ฌ์๋ค์ด ์๊ฐ์ ๋น๊ต๋ฅผ ํ ๋ ์ด๋ ค์์ ๊ฒฝํํ ์ ์๋ ๋ ์ฃผ์ ์๊ฐํ ๋จ๊ณ๋ฅผ ํ์ธํจ์ผ๋ก์จ, ๋ณธ ์ฐ๊ตฌ์์๋ ์๊ฐ์ ์ธ์ฝ๋ฉ ๋น๊ต (์ธ์ฝ๋ฉ ์ฅ๋ฒฝ) ๋ฐ ์ ๋ณด ๋น๊ต (ํด์ ์ฅ๋ฒฝ) ๊ณผ์
๋ค์ ์ด์ ์ ๋ง์ถ๋ค. ์ฒซ์งธ, ๋น๊ต ์๊ฐํ ๋์์ธ์ ์ ์ํ ๋ฌธํ๋ค(N = 104)์ ์ฒด๊ณ์ ์ผ๋ก ์กฐ์ฌ ๋ฐ ๋ถ์ํจ์ผ๋ก์จ ์๊ฐํ ์ฐ๊ตฌ์๋ค์ด ์ฌ์ฉ์ ์คํ๊ณผ ์๊ฐํ ์ค๊ณ ๊ณผ์ ์ ํตํด ์ป์ ์ค์ฉ์ ํต์ฐฐ๋ค์ ์ ๋ฆฌํ์๋ค. ์ด ๋ฌธํ์กฐ์ฌ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ๋น๊ต ์๊ฐํ ์ค๊ณ์ ๋ํ ์ง์นจ๋ค์ ์ ๋ฆฝํ๊ณ , ๋น๊ต ์๊ฐํ๋ฅผ ์ํ ๋์์ธ ๊ณต๊ฐ์ ๋ ๊น์ด ์ดํดํ๊ณ ํ์ํ๋ ๋ฐ ๋์์ ์ค ์ ์๋ ์๊ฐํ ๋ถ๋ฅ ๋ฐ ์์๋ค์ ์ ๊ณตํ๋ค. ๋์งธ, ์ด์ฌ์๋ค์ด ์๊ฐํ ์ถ์ฒ ์ธํฐํ์ด์ค์์ ์ด๋ป๊ฒ ์๋ก์ด ์๊ฐ์ ์ธ์ฝ๋ฉ๋ค์ ์๋ก ๋น๊ตํ๊ณ ์ฌ์ฉํ๋์ง์ ๋ํ ์ดํด๋ฅผ ๋๊ธฐ ์ํด ์ฌ์ฉ์ ์คํ(N = 24)์ ์ํํ์๋ค. ์ด ์คํ์ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก, ์ด์ฌ์๋ค์ ์ฃผ์ ์ด๋ ค์๋ค๊ณผ ์ด๋ค์ ํด๊ฒฐํ๊ธฐ ์ํ ๋์์ธ ์ง์นจ๋ค์ ์ ์ํ๋ค. ์
์งธ, ์๋ช
์ ๋ณดํ์๊ฐ ์๊ฐ์ ์ผ๋ก ๋ค์ ๊ฐ์ ํด๋ฌ์คํฐ๋ง ๊ฒฐ๊ณผ๋ค์ ๋น๊ต ๋ฐ ๋ถ์ํ ์ ์๋๋ก ๋์์ฃผ๋ ์๊ฐํ ์์คํ
, XCluSim์ ๋์์ธํ๊ณ ๊ตฌํํ๋ ๋์์ธ ์คํฐ๋๋ฅผ ์ํํ์๋ค. ์ฌ๋ก ์ฐ๊ตฌ๋ฅผ ํตํด ์ค์ ๋ก ์๋ช
์ ๋ณดํ์๊ฐ XCluSim์ ์ด์ฉํ์ฌ ๋ง์ ํด๋ฌ์คํฐ๋ง ๊ฒฐ๊ณผ๋ค์ ์ฝ๊ฒ ๋น๊ต ๋ฐ ํ๊ฐํ ์ ์๋ค๋ ๊ฒ์ ๋ณด์๋ค. ๋ง์ง๋ง์ผ๋ก, ์ด ์ธ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ค์ ๊ธฐ๋ฐ์ผ๋ก ๋น๊ต ์๊ฐํ ๋ถ์ผ์์ ์ ๋งํ ํฅํ ์ฐ๊ตฌ๋ค์ ์ ์ํ๋ค.CHAPTER 1. Introduction 1
1.1 Background and Motivation 1
1.2 Research Questions and Approaches 4
1.2.1 Revisiting Comparative Layouts: Design Space, Guidelines, and Future Directions 5
1.2.2 Understanding How InfoVis Novices Compare Visual Encoding Recommendation 6
1.2.3 Designing XCluSim: a Visual Analytics System for Comparing Multiple Clustering Results 7
1.3 Dissertation Outline 8
CHAPTER 2. Related Work 9
2.1 Visual Comparison Tasks 9
2.2 Visualization Designs for Comparison 10
2.2.1 Gleicher et al.s Comparative Layout 11
2.3 Understanding InfoVis Novices 12
2.4 Visualization Recommendation Interfaces 13
2.5 Comparative Visualizations for Cluster Analysis 14
CHAPTER 3. Comparative Layouts Revisited: Design Space, Guidelines, and Future Directions 19
3.1 Introduction 19
3.2 Literature Review 21
3.2.1 Method 22
3.3 Comparative Layouts in The Wild 23
3.3.1 Classifying Comparison Tasks in User Studies 25
3.3.2 Same LayoutIs Called Differently 26
3.3.3 Lucid Classification of Comparative Layouts 28
3.3.4 Advantages and Concerns of Using Each Layout 30
3.3.5 Trade-offs between Comparative Layouts 36
3.3.6 Approaches to Overcome the Concerns 38
3.3.7 Comparative Layout Explorer 42
3.4 Discussion 42
3.4.1 Guidelines for Comparative Layouts 44
3.4.2 Promising Directions for Future Research 48
3.5 Summary 49
CHAPTER 4. Understanding How InfoVis Novices Compare Visual Encoding Recommendation 51
4.1 Motivation 51
4.2 Interface 53
4.2.1 Visualization Goals 53
4.2.2 Recommendations 54
4.2.3 Representation Methods for Recommendations 54
4.2.4 Interface 58
4.2.5 Pilot Study 61
4.3 User Study 62
4.3.1 Participants 62
4.3.2 Interface 62
4.3.3 Tasks and Datasets 65
4.3.4 Procedure. 65
4.4 Findings 68
4.4.1 Poor Design Decisions 68
4.4.2 Role of Preview, Animated Transition, and Text 69
4.4.3 Challenges For Understanding Recommendations 70
4.4.4 Learning By Doing 71
4.4.5 Effects of Recommendation Order 71
4.4.6 Personal Criteria for Selecting Recommendations 72
4.5 Discussion 73
4.5.1 Design Implications 73
4.5.2 Limitations and FutureWork 75
4.6 Summary 77
CHAPTER 5. Designing XCluSim: a Visual Analytics System for Comparing Multiple Clustering Results 78
5.1 Motivation 78
5.2 Task Analysis and Design Goals 79
5.3 XCluSim 80
5.3.1 Color Encoding of Clusters Using Tree Colors 82
5.3.2 Overview of All Clustering Results 83
5.3.3 Visualization for Comparing Selected Clustering Results 86
5.3.4 Visualization for Individual Clustering Results 92
5.3.5 Implementation 100
5.4 CaseStudy 100
5.4.1 Elucidating the Role of Ferroxidase in Cryptococcus Neoformans Var. Grubii H99 (CaseStudy 1) 100
5.4.2 Finding a Clustering Result that Clearly Represents Biological Relations (CaseStudy 2) 103
5.5 Discussion 106
5.5.1 Limitations and FutureWork 108
5.6 Summary 108
CHAPTER 6. Future Research Agenda 110
6.0.1 Recommendation for Visual Comparison 110
6.0.2 Understanding the Perception of Subtle Difference 111
6.0.3 Distinguishing InfoVis Novices from Experts 112
CHAPTER 7. Conclusion. 113
Abstract (Korean) 129
Acknowledgments (Korean) 131Docto
Empathy, connectivity, authenticity, and trust: A rhetorical framework for creating and evaluating interaction design
Relationships are synergistic. Relational theories describe how we create and sustain relationships and take into consideration our own experiences, our own social location and include broad cultural signifiers. Part of our development as people is to learn about power; our own power, and others\u27 power. This thesis offers the combinational addition of Relational-Cultural Theory and the Connectivity Model to the spectrum of interaction design. Since interaction design is about designing mediating tools for people and their subsequent behaviors, particular attention is needed into establishing and maintaining relationship between designer and audience.
Relational-Cultural Theory pushes against typical patriarchal structures and values in the United States. These typical power over values/structures include men over women, whites over blacks, logic over emotion, provider over nurturer, and so on. Relational-Cultural Theory seeks a flatness of power. It creates a sense of shared power, or power with others. This idea of shared power can lead to collaborative creation in interaction design to produce useful and good designs.
Empathy, mutuality, and authenticity are essential in recognizing our own limits and strengths in connection with others. Building trust requires a mix of all three of these tenets, as well as evolution through conflict. Interaction designers can move toward creating an inclusive theory for this discipline by becoming vulnerable and sharing power with the people with whom they design interactions. Therefore, the rhetorical framework of empathy, connectivity, authenticity, and trust (e-CAT) is presented as a means of creating and evaluating interaction design
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