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<p>ACDL 2019, 2nd Advanced Course on Data Science & Machine Learning @ Certosa di Pontignano, Siena, Italy, July 15-19, 2019
https://acdl2019.icas.xyz
Early registration deadline: March 31
https://acdl2019.icas.xyz/registration/
Each Lecturer will hold three lessons on a specific topic.
Ioannis Antonoglou, Google DeepMind, UK
Phillip Isola, MIT, USA
Leslie Kaelbling, MIT - Computer Science & Artificial Intelligence Lab, USA
Ruslan Salakhutdinov, Carnegie Mellon University & AI Research at Apple, USA
Josh Tenenbaum, MIT, USA
Naftali Tishby, Hebrew University, Israel
Joaquin Vanschoren, Eindhoven University of Technology, The Netherlands
Oriol Vinyals, Google DeepMind, UK</p>
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<div style="text-align: right;"><span style="font-size:10px;"><span style="font-family:verdana,geneva,sans-serif;">Email not displaying correctly? <a clicktracking=off href="https://sg-mktg.com/MTU1Mzc2NDc1Nnw1WXY0SVdBeGM2XzVCUE0yeWV4STg5R0lHdDZIQjRFZ0ticnZEZWlmcU1oVmlIeThLX2s2a1FQUjlQSFhzNGQwY2E0cU9MRkZhRWhkNWJJTkNUZWNOS1FpWHpFNEFPZFJVdWM1MFNJS3VnRGRqVmJQMi1xZ183UUhscm8zRmUySmZVMXpwbUdjazBWcGFaaVVaV0lwSXRqZl8zRC1IUllfU09BWWExWGlhQTFSNnlGOWNxVzJUVWxRY2V6SlNwRmRfQ3F3aVR4WHVSX1BZN1c1US0tYUpORzVRRW5nNlFJWTVkMHZOTDhXb2RKVG4xR2JKbzFPbzltenE1X1R3dTQyczh6YjNWN3YtdlR4QmlpaTFvRUY0a2k1SUQ3Sk16NkdCR3VzUW5ib0RRPT18gDy_psjX1bbEL_o76wNLcwFNRR6nqoq_pdN00QFK0Po="><span style="color:#0070CD;">View it</span></a> in your browser.</span>
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<div>* Apologies for multiple copies. Please forward to anybody who might be interested *</div>
<div> </div>
<div style="text-align: left;"><span style="font-size:14px;"><span style="color:#000000;">Dear All,</span></span></div>
<div> </div>
<div>it follows the first titles (and abstracts, a little further down in this email) of the <a href="https://u9436285.ct.sendgrid.net/wf/click?upn=d4vXn57GZxbFUmLoz1hVom53k9Ty-2FY0enAVQNPvQeF7RxQcBNbcwbHWB01tKKcuu_GA3tWWLb1-2FLi-2BYT1t27j3GdrqAbJ7ujJhf7xt0kVMkAmJXbUutw-2BCoEt8jH-2F-2Fe7ZiJaM-2BEQyAMeIHJeRiBQL8sCgzsRnFZ-2FvEh-2FH1TkJJhUKB1uUm1mjnej11c7uexAY2tacGO07aO-2FP7AVqD9j2RcKXY69YJ0Z79GSjCQakzkfwYAVhvlsMQvyurNWUqj5Nue27phsTk2G9ywOxRc3oAkteZjS0Tno1h2PqkAIg8hdTFB2tf3F-2BAJ-2FyKE6-2F2Rjxn5slx9b8wIB2tbnOQIzy8y96W-2FN5nZfEXQ0e-2F2d6Inh92g3dZ2BkHiL9F-2FgVchA3wUvM9hclBgUoeBu4lUt0CQvRJXIi0Huo7mtlZdskkGvwk4cGcLSLHmyRqQt-2Fzj-2FLj408Umt5veol-2FjAh-2FH1dlStaEwWHjjy5yeMrmXAoWiSi1i-2Bm-2BArJ2L7WlRQJjrpVCB1DkKTdhV8jWPxA8USp6OQdrHiygbzMtH3hyX1lzlo-3D">ACDL 2019 Lectures</a>.</div>
<div> </div>
<div>
<div class="fullname" style="font-style: normal; font-variant-caps: normal; font-weight: bold; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; font-size: 14px;">Phillip Isola - MIT, USA</div>
</div>
<div>Lecture 1: Introduction to Generative Adversarial Networks</div>
<div>Lecture 2: Conditional GANs and Data Prediction</div>
<div>Lecture 3: GANs for Domain Translation</div>
<div> </div>
<div><strong>Leslie Kaelbling - <a href="#" rel="noopener" style="color: rgb(107, 107, 107); transition-property: all;" target="_blank">MIT - Computer Science & Artificial Intelligence Lab, USA</a></strong></div>
<div>Lecture 1: Learning in the factory and in the wild: designing robot systems that learn</div>
<div>Lecture 2: Learning factored transition models for planning in complex hybrid spaces</div>
<div>Lecture 3: Learning to speed up planning in complex hybrid spaces</div>
<div> </div>
<div><strong>Ruslan Salakhutdinov - Carnegie Mellon University & AI Research at Apple, USA</strong></div>
<div>Lecture 1: Introduction to Deep Learning, Neural Networks & Convolutional Neural Networks</div>
<div>Lecture 2: Deep Unsupervised Learning</div>
<div>Lecture 3: Recent Advances and New Challenges for Deep Learning</div>
<div>Lecture 4: Deep Learning for Natural Language Processing/Reading Comprehension</div>
<div> </div>
<div>Soon we will publish the titles and the abstracts of the other <a href="https://u9436285.ct.sendgrid.net/wf/click?upn=d4vXn57GZxbFUmLoz1hVom53k9Ty-2FY0enAVQNPvQeF4Jymn6kazAptEH8IoQCAQp_GA3tWWLb1-2FLi-2BYT1t27j3GdrqAbJ7ujJhf7xt0kVMkAmJXbUutw-2BCoEt8jH-2F-2Fe7ZiJaM-2BEQyAMeIHJeRiBQL8sCgzsRnFZ-2FvEh-2FH1TkJJhUKB1uUm1mjnej11c7uexAY2tacGO07aO-2FP7AVqD9j2RcKXY69YJ0Z79GSjCQakzkfwYAVhvlsMQvyurNWUqj5Nue27phsTk2G9ywOxRc3oAkteZjS0Tno1h2PqkAIg8hdTFB2tf3F-2BAJ-2FyKE6-2F2Rjxn5slx9b8wIB2tbnOQIzy88ooOHvzQiTenQkj9gLob5CJTvvmFdP7FwgOv4lZj4cu4lgaM7NGi-2FTGKx2a6TOJ5m5Y0RrXEjnH80XhRr24jbc4-2BZEvsG-2FyCeMOnc9dk85-2Bx5r-2F6kiH-2BVAKqwqrUzsaZa0KdQNtYkgoMHK3nClaQIyOYlt-2B0mV-2BC-2BTU89YN3RYR0T49ltRUIQI4L6w5kuLSndnOltWh1nyv-2B1ocDir9yuA-3D">ACDL 2019 lectures</a>.</div>
<div> </div>
<div>Hopefully see you in Siena in July!</div>
<div> </div>
<div> ACDL Organizing Committee.</div>
<div> </div>
<div>* Apologies for multiple copies. Please forward to anybody who might be interested *</div>
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<div><a href="https://u9436285.ct.sendgrid.net/wf/click?upn=d4vXn57GZxbFUmLoz1hVom53k9Ty-2FY0enAVQNPvQeF4Jymn6kazAptEH8IoQCAQp_GA3tWWLb1-2FLi-2BYT1t27j3GdrqAbJ7ujJhf7xt0kVMkAmJXbUutw-2BCoEt8jH-2F-2Fe7ZiJaM-2BEQyAMeIHJeRiBQL8sCgzsRnFZ-2FvEh-2FH1TkJJhUKB1uUm1mjnej11c7uexAY2tacGO07aO-2FP7AVqD9j2RcKXY69YJ0Z79GSjCQakzkfwYAVhvlsMQvyurNWUqj5Nue27phsTk2G9ywOxRc3oAkteZjS0Tno1h2PqkAIg8hdTFB2tf3F-2BAJ-2FyKE6-2F2Rjxn5slx9b8wIB2tbnOQIzy85gFED6EGfbODcL2lVngXRlZES4TcVmPIcceVh7EgVBKzirli35L2tUFMQ24qWAYKwRiUA3H-2FJ5FCZejhaEJgcW9D43DDy-2FKMg9D-2BCU9283U3WFAE4aDf8Ny5Fdx9gaFsTQanL5TCgqVlRZrXwUhKLg8aMjOqr8ZWapwXynq6AuCntmKrh4TGCHNbEdL71EV2byq02maBSVn2uhi-2BLzB2lk-3D">ACDL 2019, 2nd Advanced Course on Data Science & Machine Learning @ Certosa di Pontignano, Siena, Italy, July 15-19, 2019</a></div>
<div><a href="https://u9436285.ct.sendgrid.net/wf/click?upn=d4vXn57GZxbFUmLoz1hVom53k9Ty-2FY0enAVQNPvQeF4Jymn6kazAptEH8IoQCAQp_GA3tWWLb1-2FLi-2BYT1t27j3GdrqAbJ7ujJhf7xt0kVMkAmJXbUutw-2BCoEt8jH-2F-2Fe7ZiJaM-2BEQyAMeIHJeRiBQL8sCgzsRnFZ-2FvEh-2FH1TkJJhUKB1uUm1mjnej11c7uexAY2tacGO07aO-2FP7AVqD9j2RcKXY69YJ0Z79GSjCQakzkfwYAVhvlsMQvyurNWUqj5Nue27phsTk2G9ywOxRc3oAkteZjS0Tno1h2PqkAIg8hdTFB2tf3F-2BAJ-2FyKE6-2F2Rjxn5slx9b8wIB2tbnOQIzy86SKwdowSCjJKcxY9h9YAJuWc0CORr6oC7oiZNXsU3gM-2Fnu-2FBtzkuDIkbwWZmjyWoJBJKUZB66JvUeYMQTf0t1EeGbgcQTWkkipcP-2BiBX560cKrth73WE-2BwXCYUoYV-2BpAx5-2Bt6td-2FlXQrygP7M4F-2ByBW906As6T5NZjk2ImZERZEgcncA-2FQqEKGnCJwqE7nCxa76BFn7ckoZxKk5zVI8Sq0-3D">https://acdl2019.icas.xyz </a></div>
<div> </div>
<div><a href="https://u9436285.ct.sendgrid.net/wf/click?upn=d4vXn57GZxbFUmLoz1hVom53k9Ty-2FY0enAVQNPvQeF5EoPrvl8d-2F07eP9WG-2BHSFG_GA3tWWLb1-2FLi-2BYT1t27j3GdrqAbJ7ujJhf7xt0kVMkAmJXbUutw-2BCoEt8jH-2F-2Fe7ZiJaM-2BEQyAMeIHJeRiBQL8sCgzsRnFZ-2FvEh-2FH1TkJJhUKB1uUm1mjnej11c7uexAY2tacGO07aO-2FP7AVqD9j2RcKXY69YJ0Z79GSjCQakzkfwYAVhvlsMQvyurNWUqj5Nue27phsTk2G9ywOxRc3oAkteZjS0Tno1h2PqkAIg8hdTFB2tf3F-2BAJ-2FyKE6-2F2Rjxn5slx9b8wIB2tbnOQIzy89sKdRnITJPkEAVN8SCZVW3U8PwqJnf6SPqcl5F-2BzGgEVdk5YFeC8LM5NORHTIxZeXagMo5SuvlmbzbMcTRrOxRiVD9TNM0t4R-2FsbEfK6M4dbWrhZTgvpXR5bShkXDYOg-2BQ0Cb7FNrZHFj2rxaEvorwhjUIaLYGZ7rU-2F7kxh-2BDSWIJnnGbrcyan0QCrXziXXpIggMc5UsNtKdgjy91bmR0I-3D">Early registration deadline: March 31<br />
https://acdl2019.icas.xyz/registration/</a><br />
<br />
Each Lecturer will hold three lessons on a specific topic.</div>
<div> </div>
<div>Ioannis Antonoglou, Google DeepMind, UK<br />
<br />
Marco Gori, University of Siena, Italy</div>
<div> </div>
<div>Phillip Isola, MIT, USA </div>
<div> </div>
<div>Leslie Kaelbling, MIT - Computer Science & Artificial Intelligence Lab, USA</div>
<div> </div>
<div>Ilias S. Kotsireas, Wilfrid Laurier University, Canada </div>
<div> </div>
<div>Dolores Romero Morales, Copenhagen Business School, Denmark </div>
<div> </div>
<div>Panos Pardalos, University of Florida, USA</div>
<div> </div>
<div>Ruslan Salakhutdinov, Carnegie Mellon University & AI Research at Apple, USA</div>
<div> </div>
<div>Josh Tenenbaum, MIT, USA</div>
<div> </div>
<div>Naftali Tishby, Hebrew University, Israel</div>
<div> </div>
<div>Joaquin Vanschoren, Eindhoven University of Technology, The Netherlands</div>
<div> </div>
<div>Oriol Vinyals, Google DeepMind, UK </div>
<div> </div>
<div><a href="https://u9436285.ct.sendgrid.net/wf/click?upn=d4vXn57GZxbFUmLoz1hVom53k9Ty-2FY0enAVQNPvQeF4Jymn6kazAptEH8IoQCAQp_GA3tWWLb1-2FLi-2BYT1t27j3GdrqAbJ7ujJhf7xt0kVMkAmJXbUutw-2BCoEt8jH-2F-2Fe7ZiJaM-2BEQyAMeIHJeRiBQL8sCgzsRnFZ-2FvEh-2FH1TkJJhUKB1uUm1mjnej11c7uexAY2tacGO07aO-2FP7AVqD9j2RcKXY69YJ0Z79GSjCQakzkfwYAVhvlsMQvyurNWUqj5Nue27phsTk2G9ywOxRc3oAkteZjS0Tno1h2PqkAIg8hdTFB2tf3F-2BAJ-2FyKE6-2F2Rjxn5slx9b8wIB2tbnOQIzy8wIDpm1a0IGJ2eNKbmPqkjrKS7VRnWwz0fgZu3Jda4w7-2BJNLFe-2BADRvy9lGK7OhfGT4I-2BE1kHiPtQOnglyQe54HTLx4tUorvm8X3oTCvnj6Dw-2FXq0v53BuaAtCqsDrlWmfOF0rNfj67tAi7OJZM4UfcmkLhoGDG4c5j3Ffs9PGL3vIOHwGw35iOXdIUPE9nCckYIkmzU9UWUTSu1ru-2FDj4I-3D">https://acdl2019.icas.xyz </a></div>
<div> </div>
<div>Best,<br />
ACDL 2019 Organizing Committee. </div>
<div> </div>
<div>* Apologies for multiple copies. Please forward to anybody who might be interested *</div>
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<div class="fullname" style="font-style: normal; font-variant-caps: normal; font-weight: bold; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; font-size: 14px; text-align: center;">Phillip Isola</div>
<div class="affiliation" style="font-variant-caps: normal; font-style: italic; font-size: smaller; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; text-align: center;">MIT, USA</div>
<div class="affiliation" style="font-variant-caps: normal; font-style: italic; font-size: smaller; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; text-align: center;"> </div>
<div class="affiliation" style="font-variant-caps: normal; font-style: italic; font-size: smaller; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; text-align: center;"> </div>
</div>
<div><strong>Lecture 1: Learning in the factory and in the wild: designing robot systems that learn </strong></div>
<div> </div>
<div>We examine the general problem of designing robot systems from a decision-theoretic perspective that makes it clear when an individual needs to learn when it is actually performing its tasks (in the wild) and when the AI engineers need to use learning methods to design a good robot learner. We will examine several learning paradigms from this perspective and talk about methods for meta-learning, including modular meta-learning and graph element networks.</div>
<div> </div>
<div> </div>
<div><strong>Lecture 2: Learning factored transition models for planning in complex hybrid spaces </strong></div>
<div> </div>
<div>Many robotics problem distributions are better addressed by learning models and using them to do online reasoning (an approach also known as model-predictive control) than by learning a policy or value function. We begin by discussing this claim, and then study the forms of models that are most appropriate for different types of planning problems. We then examine two new approaches for learning models that are appropriate for planning in complex hybrid (mixed discrete and continuous) problems, such as robot task and motion planning. One approach is based on Gaussian-process active learning and another on an extension of graph neural networks. </div>
<div> </div>
<div><strong>Lecture 3: Learning to speed up planning in complex hybrid spaces </strong></div>
<div> </div>
<div>An important role for learning is to speed up search: this is the critical role that learning plays in methods such as Alpha-Zero. We will examine several different mechanisms that can be used (including learning heuristic or static evaluation functions and learning to bias the action sampling distribution), with a focus on problems that require choosing actions from a continuous or hybrid space.</div>
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<img class="max-width" width="300" src="https://marketing-image-production.s3.amazonaws.com/uploads/f5aed276a29856acb4568dd4a34ae18d9158774168c79c8244dc744f5a9096aec6f953d7286c75bb4267772b627e4b99ec1c352f92949a97a85e7e53654e2c67.jpg" alt="" border="0" style="display:block;color:#000;text-decoration:none;font-family:Helvetica, arial, sans-serif;font-size:16px;max-width:100% !important;width:100%;height:auto !important;">
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<div class="fullname" style="font-style: normal; font-variant-caps: normal; font-weight: bold; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; font-size: 14px; text-align: center;">Leslie Kaelbling</div>
<div class="affiliation" style="font-variant-caps: normal; font-style: italic; font-size: smaller; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; text-align: center;"><a href="#" rel="noopener" style="color: rgb(107, 107, 107); transition-property: all;" target="_blank">MIT - Computer Science & Artificial Intelligence Lab, USA</a></div>
<div class="affiliation" style="font-variant-caps: normal; font-style: italic; font-size: smaller; caret-color: rgb(107, 107, 107); color: rgb(107, 107, 107); font-family: Nunito, sans-serif; text-align: center;"> </div>
</div>
<div><strong>Lecture 1: Learning in the factory and in the wild: designing robot systems that learn </strong></div>
<div> </div>
<div>We examine the general problem of designing robot systems from a decision-theoretic perspective that makes it clear when an individual needs to learn when it is actually performing its tasks (in the wild) and when the AI engineers need to use learning methods to design a good robot learner. We will examine several learning paradigms from this perspective and talk about methods for meta-learning, including modular meta-learning and graph element networks.</div>
<div> </div>
<div><strong>Lecture 2: Learning factored transition models for planning in complex hybrid spaces </strong></div>
<div> </div>
<div>Many robotics problem distributions are better addressed by learning models and using them to do online reasoning (an approach also known as model-predictive control) than by learning a policy or value function. We begin by discussing this claim, and then study the forms of models that are most appropriate for different types of planning problems. We then examine two new approaches for learning models that are appropriate for planning in complex hybrid (mixed discrete and continuous) problems, such as robot task and motion planning. One approach is based on Gaussian-process active learning and another on an extension of graph neural networks.</div>
<div> </div>
<div><strong>Lecture 3: Learning to speed up planning in complex hybrid spaces </strong></div>
<div> </div>
<div>An important role for learning is to speed up search: this is the critical role that learning plays in methods such as Alpha-Zero. We will examine several different mechanisms that can be used (including learning heuristic or static evaluation functions and learning to bias the action sampling distribution), with a focus on problems that require choosing actions from a continuous or hybrid space.</div>
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