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url('/binaries/content/gallery/global/tintpattern.png') repeat 0 0;"></div> <div class="row"> <div class="columns large-12 medium-12 banner-content"> <p class="banner-heading"> <small>FODS-2020</small> ACM-IMS Foundations of Data Science Conference</p> <h2 style="margin-left: 16px;">October 19 - 20, 2020</h2></div> </div> </div> </div> </div> </div> <div class="article" id="maincontent"> <article class="has-edit-button" id="SkipTarget" tabindex="-1"> <div class="row"> <div class="columns small-12 zone-1"> <div> </div> </div> </div> <div class="row"> <div class="columns large-8 medium-8 small-12 zone-2"> <div> <div> <div class="articles"> <div class="landing"> <h1> Welcome to FODS-2020</h1> <div class="clearfix"> <p style="color:red"><strong>The FODS-2020 Conference will offer a virtual format. Program times are US Eastern Time (EDT). </strong></p> <p>The Association for Computing Machinery (ACM) and the Institute of Mathematical Statistics (IMS) have come together to launch a conference series on the Foundations of Data Science. Our inaugural event, the ACM-IMS Interdisciplinary Summit on the Foundations of Data Science, took place in San Francisco in 2019. Starting in 2020 we will have an annual conference with refereed conference proceedings. The 2020 event takes place virtually October 19-20, 2020 and the submission deadline was May 15, 2020. This interdisciplinary event will bring together researchers and practitioners to address foundational data science challenges in prediction, inference, fairness, ethics and the future of data science.</p> <p>Jeannette Wing and David Madigan<br /> FODS-2020 Conference Co-chairs<br /> <a href="/cdn-cgi/l/email-protection#65030a01165755575525060a091008070c044b000110"><span class="__cf_email__" data-cfemail="f89e979c8bcac8cac8b89b97948d959a9199d69d9c8d">[email protected]</span></a></p> <hr /> <p> </p></div><!-- MainContent --> </div> </div> </div> <div> <div class="articles"> <div class="landing"> <h1> Program</h1> <div class="clearfix"> <p><strong><a href="https://easychair.org/smart-program/FODS2020/" target="_blank">Click here for an interactive version of the program</a>.</strong></p> <p><strong style="color:red">Program times are US Eastern Time (EDT).</strong></p> <h2 id="h-monday,-october-19th">Monday, October 19th</h2> <p><strong>09:00-12:00 EDT Session 1: Tutorial - Causal Reasoning Tutorial</strong></p> <p style="margin-left: 40px;">09:00 EDT - <strong>David Blei</strong> - <em>Causal Reasoning Tutorial</em></p> <p><strong>12:30-13:30 EDT Session 2: Keynote </strong></p> <p style="margin-left: 40px;">12:30 EDT - <strong>Mihaela van der Schaar</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416879" target="_blank"><em>AutoML and interpretability: powering the machine learning revolution in healthcare</em></a></p> <p><strong>14:00-15:30 EDT Session 3: Plenary Session - Methodology</strong></p> <p style="margin-left: 40px;">14:00 EDT - <strong>Yu Gui</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416881" target="_blank"><em>ADAGES: adaptive aggregation with stability for distributed feature selection</em></a></p> <p style="margin-left: 40px;">14:20 EDT - <strong>Chenglin Fan and Ping Li</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416886" target="_blank"><em>Classification Acceleration via Merging Decision Trees</em></a></p> <p style="margin-left: 40px;">14:40 EDT - <strong>Sarah Tan, Matvey Soloviev, Giles Hooker and Martin T. Wells</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416893" target="_blank"><em>Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable</em></a></p> <p style="margin-left: 40px;">15:00 EDT - <strong>Miguel Carreira-Perpinan and Arman Zharmagambetov</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416882" target="_blank"><em>Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting</em></a></p> <p><strong>15:45-17:15 EDT Session 4: Plenary Session - Fairness, Privacy, Interpretability</strong></p> <p style="margin-left: 40px;">15:45 EDT - <strong>Collin Burns, Jesse Thomason and Wesley Tansey</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416889" target="_blank"><em>Interpreting Black Box Models via Hypothesis Testing</em></a></p> <p style="margin-left: 40px;">16:05 EDT - <strong>Ruobin Gong and Xiao-Li Meng</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416892" target="_blank"><em>Congenial Differential Privacy under Mandated Disclosure</em></a></p> <p style="margin-left: 40px;">16:25 EDT - <strong>Roland Maio and Augustin Chaintreau</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416890" target="_blank"><em>Incentives Needed for Low-Cost Fair Data Reuse</em></a></p> <p style="margin-left: 40px;">16:45 EDT - <strong>Isabella Grasso, David Russell, Jeanna Matthews, Abigail Matthews and Nick Record</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416897" target="_blank"><em>Applying Algorithmic Accountability Principles and Frameworks to Ecosystem Forecasting: A Case Study in Forecasting Shellfish Toxicity in the Gulf of Maine</em></a></p> <h2 id="h-tuesday,-october-20th">Tuesday, October 20th</h2> <p><strong>09:00-12:00 EDT Session 5: Tutorial - Fairness, Privacy, and Ethics in Data Science Tutorial</strong></p> <p style="margin-left: 40px;">09:00 EDT - <strong>Michael Kearns</strong> - <em>Fairness, Privacy, and Ethics in Data Science Tutorial</em></p> <p><strong>12:30-13:30 EDT Session 6: Keynote</strong></p> <p style="margin-left: 40px;">12:30 EDT - <strong>Oren Etzioni</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416880" target="_blank"><em>Semantic Scholar, NLP, and the Fight Against COVID-19</em></a></p> <p><strong>14:00-15:50 EDT Session 7: Plenary Session - Data Science Theory</strong></p> <p style="margin-left: 40px;">14:00 EDT - <strong>Alex Fout, Bailey Fosdick and Matthew Hitt</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416887" target="_blank"><em>Non-Uniform Sampling of Fixed Margin Binary Matrices</em></a></p> <p style="margin-left: 40px;">14:20 EDT - <strong>Claire Mathieu and Michel De Rougemont</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416884" target="_blank"><em>Large very dense subgraphs in a stream of edges</em></a></p> <p style="margin-left: 40px;">14:40 EDT - <strong>Xiangyi Chen, Xiaoyun Li and Ping Li</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416891" target="_blank"><em>Toward Communication Efficient Adaptive Gradient Method</em></a></p> <p style="margin-left: 40px;">15:00 EDT - <strong>Tianyu Wang, Weicheng Ye, Dawei Geng and Cynthia Rudin</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416885" target="_blank"><em>Towards Practical Lipschitz Bandits</em></a></p> <p style="margin-left: 40px;">15:20 EDT - <strong>Devavrat Shah, Varun Somani, Qiaomin Xie and Zhi Xu</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416885" target="_blank"><em>On Reinforcement Learning for Turn-based Zero-sum Markov Games</em></a></p> <p><strong>16:00-18:00 EDT Session 8: Plenary Session - Foundations in Practice</strong></p> <p style="margin-left: 40px;">16:00 EDT - <strong>Ryan Bernstein, Matthijs Vákár and Jeannette Wing</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416896" target="_blank"><em>Transforming Probabilistic Programs for Model Checking</em></a></p> <p style="margin-left: 40px;">16:20 EDT - <strong>Haitian Chen, Bai Jiang and Hao Chen</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416895" target="_blank"><em>StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks</em></a></p> <p style="margin-left: 40px;">16:40 EDT - <strong>Lina Lin, Mathias Drton and Ali Shojaie</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416883" target="_blank"><em>Statistical significance in high-dimensional linear mixed models</em></a></p> <p style="margin-left: 40px;">17:00 EDT - <strong>Thanh Le and Vasant Honavar</strong> - <a href="https://dl.acm.org/doi/10.1145/3412815.3416894" target="_blank"><em>Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data</em></a></p> <p> </p></div><!-- MainContent --> </div> </div> </div> </div> </div> <div class="columns large-4 medium-4 small-12 zone-3"> <div> <div> <div class="articles single-cta"> <div 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