skip to main content

This content will become publicly available on July 1, 2023

Title: Interactive Correlation Clustering with Existential Cluster Constraints
We consider the problem of clustering with user feedback. Existing methods express constraints about the input data points, most commonly through must-link and cannot-link constraints on data point pairs. In this paper, we introduce existential cluster constraints: a new form of feedback where users indicate the features of desired clusters. Specifically, users make statements about the existence of a cluster having (and not having) particular features. Our approach has multiple advantages: (1) constraints on clusters can express user intent more efficiently than point pairs; (2) in cases where the users’ mental model is of the desired clusters, it is more natural for users to express cluster-wise preferences; (3) it functions even when privacy restrictions prohibit users from seeing raw data. In addition to introducing existential cluster constraints, we provide an inference algorithm for incorporating our constraints into the output clustering. Finally, we demonstrate empirically that our proposed framework facilitates more accurate clustering with dramatically fewer user feedback inputs.
Authors:
; ; ;
Award ID(s):
1763618
Publication Date:
NSF-PAR ID:
10356090
Journal Name:
Proceedings of the 39th International Conference on Machine Learning
Sponsoring Org:
National Science Foundation
More Like this
  1. The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less
  2. CONTEXT Engineering education is an interdisciplinary research field where scholars are commonly embedded within the context they study. Engineering Education Scholars (EES), individuals who define themselves by having expertise associated with both engineering education research and practice, inhabit an array of academic positions, depending on their priorities, interests, and desired impact. These positions include, but are not limited to, traditional tenure-track faculty positions, professional teaching or research positions, and positions within teaching and learning centers or other centers. EES also work in diverse institutional contexts, including engineering disciplinary departments, first-year programs, and engineering education departments, which further vary their roles. PURPOSE OR GOAL The purpose of this preliminary research study is to better understand the roles and responsibilities of early-career EES. This knowledge will enable PhD programs to better prepare engineering education graduates to more intentionally seek positions, which is especially important given the growing number of engineering education PhD programs. We address our purpose by exploring the following research question: How can we describe the diversity of academic or faculty roles early-career EES undertake? APPROACH OR METHODOLOGY/METHODS We implemented an explanatory sequential mixed-methods study starting with a survey (n=59) to better understand the strategic actions of United States-based early-careermore »EES. We used a clustering technique to identify clusters of participants based on these actions (e.g., teaching focused priorities, research goals). We subsequently recruited 14 survey participants, representing each of the main clusters, to participate in semi-structured interviews. Through the interviews, we sought to gain a more nuanced understanding of each participant’s actions in the contexts of their roles and responsibilities. We analyzed each interview transcript to develop memos providing an overview of each early-career EES role description and then used a cross case analysis where the unit of analysis was a cluster. ACTUAL OUTCOMES Five main clusters were identified through our analysis, with three representing primarily research-focused day-to-day responsibilities and two representing primarily teaching-focused day-to-day responsibilities. The difference between the clusters was influenced by the institutional context and the areas in which EES selected to focus their roles and responsibilities. These results add to our understanding of how early-career EES enact their roles within different institutional contexts and positions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY This work can be used by graduate programs around the world to better prepare their engineering education graduates for obtaining positions that align with their goals and interests. Further, we expect this work to provide insight to institutions so that they can provide the support and resources to enable EES to reach their desired impact within their positions.« less
  3. CONTEXT Engineering education is an interdisciplinary research field where scholars are commonly embedded within the context they study. Engineering Education Scholars (EES), individuals who define themselves by having expertise associated with both engineering education research and practice, inhabit an array of academic positions, depending on their priorities, interests, and desired impact. These positions include, but are not limited to, traditional tenure-track faculty positions, professional teaching or research positions, and positions within teaching and learning centers or other centers. EES also work in diverse institutional contexts, including engineering disciplinary departments, first-year programs, and engineering education departments, which further vary their roles. PURPOSE OR GOAL The purpose of this preliminary research study is to better understand the roles and responsibilities of early-career EES. This knowledge will enable PhD programs to better prepare engineering education graduates to more intentionally seek positions, which is especially important given the growing number of engineering education PhD programs. We address our purpose by exploring the following research question: How can we describe the diversity of academic or faculty roles early-career EES undertake? APPROACH OR METHODOLOGY/METHODS We implemented an explanatory sequential mixed-methods study starting with a survey (n=59) to better understand the strategic actions of United States-based early-careermore »EES. We used a clustering technique to identify clusters of participants based on these actions (e.g., teaching focused priorities, research goals). We subsequently recruited 14 survey participants, representing each of the main clusters, to participate in semi-structured interviews. Through the interviews, we sought to gain a more nuanced understanding of each participant’s actions in the contexts of their roles and responsibilities. We analyzed each interview transcript to develop memos providing an overview of each early-career EES role description and then used a cross case analysis where the unit of analysis was a cluster. ACTUAL OUTCOMES Five main clusters were identified through our analysis, with three representing primarily research-focused day-to-day responsibilities and two representing primarily teaching-focused day-to-day responsibilities. The difference between the clusters was influenced by the institutional context and the areas in which EES selected to focus their roles and responsibilities. These results add to our understanding of how early-career EES enact their roles within different institutional contexts and positions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY This work can be used by graduate programs around the world to better prepare their engineering education graduates for obtaining positions that align with their goals and interests. Further, we expect this work to provide insight to institutions so that they can provide the support and resources to enable EES to reach their desired impact within their positions.« less
  4. Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable. This paper explores a novel setting for performing clustering on complex data while simultaneously generating explanations using interpretable tags. We propose deep descriptive clustering that performs sub-symbolic representation learning on complex data while generating explanations based on symbolic data. We form good clusters by maximizing the mutual information between empirical distribution on the inputs and the induced clustering labels for clustering objectives. We generate explanations by solving an integer linear programming that generates concise and orthogonal descriptions for each cluster. Finally, we allow the explanation to inform better clustering by proposing a novel pairwise loss with self-generated constraints to maximize the clustering and explanation module's consistency. Experimental results on public data demonstrate that our model outperforms competitive baselines in clustering performance while offering high-quality cluster-level explanations.
  5. Both historically and in terms of practiced academic organization, the anticipation should be that a flourishing synergistic interface exists between statistics and operations research in general, and between spatial statistics/econometrics and spatial optimization in particular. Unfortunately, for the most part, this expectation is false. The purpose of this paper is to address this existential missing link by focusing on the beneficial contributions of spatial statistics to spatial optimization, via spatial autocorrelation (i.e., dis/similar attribute values tend to cluster together on a map), in order to encourage considerably more future collaboration and interaction between contributors to their two parent bodies of knowledge. The key basic statistical concept in this pursuit is the median in its bivariate form, with special reference to the global and to sets of regional spatial medians. One-dimensional examples illustrate situations that the narrative then extends to two-dimensional illustrations, which, in turn, connects these treatments to the spatial statistics centrography theme. Because of computational time constraints (reported results include some for timing experiments), the summarized analysis restricts attention to problems involving one global and two or three regional spatial medians. The fundamental and foundational spatial, statistical, conceptual tool employed here is spatial autocorrelation: geographically informed sampling designs—which acknowledgemore »a non-random mixture of geographic demand weight values that manifests itself as local, homogeneous, spatial clusters of these values—can help spatial optimization techniques determine the spatial optima, at least for location-allocation problems. A valuable discovery by this study is that existing but ignored spatial autocorrelation latent in georeferenced demand point weights undermines spatial optimization algorithms. All in all, this paper should help initiate a dissipation of the existing isolation between statistics and operations research, hopefully inspiring substantially more collaborative work by their professionals in the future.« less