{"id":361979,"date":"2017-03-07T02:46:04","date_gmt":"2017-03-07T10:46:04","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=361979"},"modified":"2025-08-06T11:58:00","modified_gmt":"2025-08-06T18:58:00","slug":"artificial-intelligence-and-machine-learning-in-cambridge-2017","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/artificial-intelligence-and-machine-learning-in-cambridge-2017\/","title":{"rendered":"Artificial Intelligence and Machine Learning in Cambridge 2017"},"content":{"rendered":"\n\n

<\/h3>\n

\u00a0<\/span>Opens in a new tab<\/span><\/p>\n

Description<\/h3>\n

Cambridge and the silicon fens (opens in new tab)<\/span><\/a> have a large machine learning community across the university and many small and larger Cambridge companies.<\/p>\n

This academic workshop aims to connect this community better and to learn from each other about interesting research and applications in the broad machine learning spectrum.<\/p>\n

Attendance and Registration<\/h3>\n

Please note registration for this event has now closed.<\/p>\n

Organizers:<\/h3>\n

Sebastian Nowozin, Microsoft Research Cambridge<\/p>\n

Miguel Hernandez-Lobato, University of Cambridge<\/p>\n

Zoubin Ghahramani, University of Cambridge<\/p>\n

\u00a0<\/span>Opens in a new tab<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nTitle<\/th>\nSpeaker<\/th>\n<\/tr>\n<\/thead>\n
\n
9:15 – 9:45<\/div>\n<\/td>\n
\n
Registration & Welcome tea\/coffee<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
9:45 – 10:05<\/div>\n<\/td>\n
\n
Learning to Code: Machine Learning for Program Induction<\/div>\n<\/td>\n
\n
Alex Gaunt<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
10:05 – 10:25<\/div>\n<\/td>\n
\n
Learning Program Representations: Symbols to Vectors to Semantics<\/div>\n<\/td>\n
\n
Miltos Allamanis<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
10:25 – 10:45<\/div>\n<\/td>\n
\n
TBD<\/div>\n<\/td>\n
\n
Ryota Tomioka<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
10:45 – 11:05<\/div>\n<\/td>\n
\n
From GANs to Variational Divergence Minimization<\/div>\n<\/td>\n
\n
Sebastian Nowozin<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
11:05 – 11:30<\/div>\n<\/td>\n
\n
Coffee break<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
11:30 – 11:50<\/div>\n<\/td>\n
\n
Automatic Discovery of the Statistical Types of Variables in a Dataset<\/div>\n<\/td>\n
\n
Isabel Valera<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
11:50 – 12:10<\/div>\n<\/td>\n
\n
Approximate Inference with Amortised MCMC<\/div>\n<\/td>\n
\n
Yingzhen Li<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
12:10 – 12:30<\/div>\n<\/td>\n
\n
The Automatic Statistician: a project update<\/div>\n<\/td>\n
\n
Zoubin Ghahramani<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
12:30 – 12:50<\/div>\n<\/td>\n
\n
The Supervised Word Mover’s Distance<\/div>\n<\/td>\n
\n
Matt Kusner<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
1:00 – 2:00<\/div>\n<\/td>\n
\n
Lunch<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
2:00 – 2:20<\/div>\n<\/td>\n
\n
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks<\/div>\n<\/td>\n
\n
Jose Miguel Hernandez Lobato<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
2:20 – 2:40<\/div>\n<\/td>\n
\n
Bayesian optimisation in many dimensions with bespoke models<\/div>\n<\/td>\n
\n
Adrian Weller<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
2:40 – 3:00<\/div>\n<\/td>\n
\n
Grammar Variational Autoencoder<\/div>\n<\/td>\n
\n
Brooks Paige<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
3:00 – 3:20<\/div>\n<\/td>\n
\n
Invertible Transformations for Bayesian Neural Network Inference<\/div>\n<\/td>\n
\n
Amar Shah<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
3:20 – 4:00<\/div>\n<\/td>\n
\n
Coffee break<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
4:00 – 4:20<\/div>\n<\/td>\n
\n
AI for Healthcare<\/div>\n<\/td>\n
\n
Chris Bishop<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
4:20 – 4:40<\/div>\n<\/td>\n
\n
Project Alexandria: a web scale probabilistic program for unsupervised knowledge base construction<\/div>\n<\/td>\n
\n
John Winn<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
4:40 – 5:00<\/div>\n<\/td>\n
\n
The Malmo Collaborative AI Challenge<\/div>\n<\/td>\n
\n
Katja Hofmann<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Opens in a new tab<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

This academic workshop aims to connect this community better and to learn from each other about interesting research and applications in the broad machine learning spectrum.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2017-03-28","msr_enddate":"2017-03-28","msr_location":"Microsoft Research Cambridge UK","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"09:00-17:00","msr_hide_region":true,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-region":[239178],"msr-event-type":[197944],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-361979","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-region-europe","msr-event-type-hosted-by-microsoft","msr-locale-en_us"],"msr_about":"\n\n

<\/h3>\n

\u00a0<\/span>Opens in a new tab<\/span><\/p>\n

Description<\/h3>\n

Cambridge and the silicon fens (opens in new tab)<\/span><\/a> have a large machine learning community across the university and many small and larger Cambridge companies.<\/p>\n

This academic workshop aims to connect this community better and to learn from each other about interesting research and applications in the broad machine learning spectrum.<\/p>\n

Attendance and Registration<\/h3>\n

Please note registration for this event has now closed.<\/p>\n

Organizers:<\/h3>\n

Sebastian Nowozin, Microsoft Research Cambridge<\/p>\n

Miguel Hernandez-Lobato, University of Cambridge<\/p>\n

Zoubin Ghahramani, University of Cambridge<\/p>\n

\u00a0<\/span>Opens in a new tab<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nTitle<\/th>\nSpeaker<\/th>\n<\/tr>\n<\/thead>\n
\n
9:15 – 9:45<\/div>\n<\/td>\n
\n
Registration & Welcome tea\/coffee<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
9:45 – 10:05<\/div>\n<\/td>\n
\n
Learning to Code: Machine Learning for Program Induction<\/div>\n<\/td>\n
\n
Alex Gaunt<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
10:05 – 10:25<\/div>\n<\/td>\n
\n
Learning Program Representations: Symbols to Vectors to Semantics<\/div>\n<\/td>\n
\n
Miltos Allamanis<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
10:25 – 10:45<\/div>\n<\/td>\n
\n
TBD<\/div>\n<\/td>\n
\n
Ryota Tomioka<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
10:45 – 11:05<\/div>\n<\/td>\n
\n
From GANs to Variational Divergence Minimization<\/div>\n<\/td>\n
\n
Sebastian Nowozin<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
11:05 – 11:30<\/div>\n<\/td>\n
\n
Coffee break<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
11:30 – 11:50<\/div>\n<\/td>\n
\n
Automatic Discovery of the Statistical Types of Variables in a Dataset<\/div>\n<\/td>\n
\n
Isabel Valera<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
11:50 – 12:10<\/div>\n<\/td>\n
\n
Approximate Inference with Amortised MCMC<\/div>\n<\/td>\n
\n
Yingzhen Li<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
12:10 – 12:30<\/div>\n<\/td>\n
\n
The Automatic Statistician: a project update<\/div>\n<\/td>\n
\n
Zoubin Ghahramani<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
12:30 – 12:50<\/div>\n<\/td>\n
\n
The Supervised Word Mover’s Distance<\/div>\n<\/td>\n
\n
Matt Kusner<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
1:00 – 2:00<\/div>\n<\/td>\n
\n
Lunch<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
2:00 – 2:20<\/div>\n<\/td>\n
\n
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks<\/div>\n<\/td>\n
\n
Jose Miguel Hernandez Lobato<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
2:20 – 2:40<\/div>\n<\/td>\n
\n
Bayesian optimisation in many dimensions with bespoke models<\/div>\n<\/td>\n
\n
Adrian Weller<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
2:40 – 3:00<\/div>\n<\/td>\n
\n
Grammar Variational Autoencoder<\/div>\n<\/td>\n
\n
Brooks Paige<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
3:00 – 3:20<\/div>\n<\/td>\n
\n
Invertible Transformations for Bayesian Neural Network Inference<\/div>\n<\/td>\n
\n
Amar Shah<\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
3:20 – 4:00<\/div>\n<\/td>\n
\n
Coffee break<\/strong><\/div>\n<\/td>\n
<\/td>\n<\/td>\n<\/tr>\n
\n
4:00 – 4:20<\/div>\n<\/td>\n
\n
AI for Healthcare<\/div>\n<\/td>\n
\n
Chris Bishop<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
4:20 – 4:40<\/div>\n<\/td>\n
\n
Project Alexandria: a web scale probabilistic program for unsupervised knowledge base construction<\/div>\n<\/td>\n
\n
John Winn<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n
\n
4:40 – 5:00<\/div>\n<\/td>\n
\n
The Malmo Collaborative AI Challenge<\/div>\n<\/td>\n
\n
Katja Hofmann<\/a><\/div>\n<\/td>\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Opens in a new tab<\/span><\/p>\n","tab-content":[{"id":0,"name":"About","content":"

Description<\/h3>\r\nCambridge and the silicon fens<\/a> have a large machine learning community across the university and many small and larger Cambridge companies.\r\n\r\nThis academic workshop aims to connect this community better and to learn from each other about interesting research and applications in the broad machine learning spectrum.\r\n

Attendance and Registration<\/h3>\r\nPlease note registration for this event has now closed.\r\n

Organizers:<\/h3>\r\nSebastian Nowozin, Microsoft Research Cambridge\r\n\r\nMiguel Hernandez-Lobato, University of Cambridge\r\n\r\nZoubin Ghahramani, University of Cambridge\r\n\r\n\u00a0<\/span>"},{"id":1,"name":"Agenda","content":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Time<\/th>\r\nTitle<\/th>\r\nSpeaker<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
\r\n
9:15 - 9:45<\/div><\/td>\r\n
\r\n
Registration & Welcome tea\/coffee<\/strong><\/div><\/td>\r\n
<\/td>\r\n<\/td>\r\n<\/tr>\r\n
\r\n
9:45 - 10:05<\/div><\/td>\r\n
\r\n
Learning to Code: Machine Learning for Program Induction<\/div><\/td>\r\n
\r\n
Alex Gaunt<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
10:05 - 10:25<\/div><\/td>\r\n
\r\n
Learning Program Representations: Symbols to Vectors to Semantics<\/div><\/td>\r\n
\r\n
Miltos Allamanis<\/a><\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
10:25 - 10:45<\/div><\/td>\r\n
\r\n
TBD<\/div><\/td>\r\n
\r\n
Ryota Tomioka<\/a><\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
10:45 - 11:05<\/div><\/td>\r\n
\r\n
From GANs to Variational Divergence Minimization<\/div><\/td>\r\n
\r\n
Sebastian Nowozin<\/a><\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
11:05 - 11:30<\/div><\/td>\r\n
\r\n
Coffee break<\/strong><\/div><\/td>\r\n
<\/td>\r\n<\/td>\r\n<\/tr>\r\n
\r\n
11:30 - 11:50<\/div><\/td>\r\n
\r\n
Automatic Discovery of the Statistical Types of Variables in a Dataset<\/div><\/td>\r\n
\r\n
Isabel Valera<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
11:50 - 12:10<\/div><\/td>\r\n
\r\n
Approximate Inference with Amortised MCMC<\/div><\/td>\r\n
\r\n
Yingzhen Li<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
12:10 - 12:30<\/div><\/td>\r\n
\r\n
The Automatic Statistician: a project update<\/div><\/td>\r\n
\r\n
Zoubin Ghahramani<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
12:30 - 12:50<\/div><\/td>\r\n
\r\n
The Supervised Word Mover's Distance<\/div><\/td>\r\n
\r\n
Matt Kusner<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
1:00 - 2:00<\/div><\/td>\r\n
\r\n
Lunch<\/strong><\/div><\/td>\r\n
<\/td>\r\n<\/td>\r\n<\/tr>\r\n
\r\n
2:00 - 2:20<\/div><\/td>\r\n
\r\n
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks<\/div><\/td>\r\n
\r\n
Jose Miguel Hernandez Lobato<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
2:20 - 2:40<\/div><\/td>\r\n
\r\n
Bayesian optimisation in many dimensions with bespoke models<\/div><\/td>\r\n
\r\n
Adrian Weller<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
2:40 - 3:00<\/div><\/td>\r\n
\r\n
Grammar Variational Autoencoder<\/div><\/td>\r\n
\r\n
Brooks Paige<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
3:00 - 3:20<\/div><\/td>\r\n
\r\n
Invertible Transformations for Bayesian Neural Network Inference<\/div><\/td>\r\n
\r\n
Amar Shah<\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
3:20 - 4:00<\/div><\/td>\r\n
\r\n
Coffee break<\/strong><\/div><\/td>\r\n
<\/td>\r\n<\/td>\r\n<\/tr>\r\n
\r\n
4:00 - 4:20<\/div><\/td>\r\n
\r\n
AI for Healthcare<\/div><\/td>\r\n
\r\n
Chris Bishop<\/a><\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
4:20 - 4:40<\/div><\/td>\r\n
\r\n
Project Alexandria: a web scale probabilistic program for unsupervised knowledge base construction<\/div><\/td>\r\n
\r\n
John Winn<\/a><\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n
\r\n
4:40 - 5:00<\/div><\/td>\r\n
\r\n
The Malmo Collaborative AI Challenge<\/div><\/td>\r\n
\r\n
Katja Hofmann<\/a><\/div><\/td>\r\n
<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>"}],"msr_startdate":"2017-03-28","msr_enddate":"2017-03-28","msr_event_time":"09:00-17:00","msr_location":"Microsoft Research Cambridge UK","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"March 28, 2017","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"This academic workshop aims to connect this community better and to learn from each other about interesting research and applications in the broad machine learning spectrum.","msr_research_lab":[199561],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[],"related-videos":[384776,384842,385247,385256,385265,385289,385301,385313,385322,385331],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/361979","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/361979\/revisions"}],"predecessor-version":[{"id":1147179,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/361979\/revisions\/1147179"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=361979"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=361979"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=361979"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=361979"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=361979"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=361979"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=361979"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=361979"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=361979"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}