PPT-Policy Gradient as a Proxy for

Dynamic Oracles in Constituency Parsing Daniel Fried and Dan Klein Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing Daniel Fried and Dan Klein Policy Gradient as a Proxy for . Joshua Reich*, Michel Goraczko, . Aman Kansal, and Jitu Padhye. Columbia University*, Microsoft Research. 1. A Short Story: Sleepless in Seattle. A desktop machine. Workdays: often used, sometimes idle. Consolidated . Middlebox. Architecture. 1. Vyas Sekar. Sylvia . Ratnasamy. Michael Reiter. Norbert . Egi. . Guangyu. Shi. Need for Network Evolution. 2. New devices. New applications. Evolving . threats. Practical Convergence of Middleboxes and . Software. -Defined Networking. Vyas Sekar. . Joint work with: . Seyed. Kaveh Fayazbakhsh, . Zafar. . Qazi. , . Luis Chiang, . Rui. Miao, William . Tu. ,. v2.00. By Robert Auger. PayPal Information Risk Management. November 2009. Overview. What are transparent and intercepting proxies?. When are transparent proxies used?. How do they operate?. Brief intro to the SOP. :. . Enforcing Network-Wide . Policies . in . the Presence of . Dynamic . Middlebox. Actions. Seyed. K. . Fayazbakhsh. Vyas Sekar . Jeff . Mogul. Minlan. Yu . 1. Network OS. Data Plane. Web . & Concurrency. Rohith Jagannathan. April . 13. th. , 2015. 15/18-213: Introduction to Computer Systems. Recitation 13. Outline. Getting content on the web: Telnet/. cURL. Demo. How the web really works. Web . & Concurrency. Ian Hartwig. 15/18-213 - Section . E. Recitation 13. April 15. th. , 2013. Outline. Getting content on the web: Telnet/. cURL. Demo. How . the web really works. Proxy. Due . Extensions. Middleboxes. 1. Ack. : . Vyas. . Sekar. , Aaron . Gember. , . Felipe . Huici. , . Zafar. . Qazi. Need for Network Evolution. 2. New devices. New applications. Evolving . threats. Policy . and. Unconstrained Minimization. Brendan and Yifang . Feb 24 2015. Paper: Learning to Cooperate via Policy Search. Peshkin. , Leonid and Kim, . Kee-Eung. and . Meuleau. , Nicolas and . Kaelbling. , Leslie . (Some Slides taken from Alan Fern’s course). Factored MDP/RL. Representations. States made of features. Boolean vs. Continuous . Actions modify the features (probabilistically). Representations include Probabilistic STRIPS, 2-Time-slice Dynamic . Sources: . Stanford CS 231n. , . Berkeley Deep RL course. , . David Silver’s RL course. Policy Gradient Methods. Instead of indirectly representing the policy using Q-values, it can be more efficient to parameterize and learn it directly. Joshua Reich*, Michel Goraczko, . Aman Kansal, and Jitu Padhye. Columbia University*, Microsoft Research. 1. A Short Story: Sleepless in Seattle. A desktop machine. Workdays: often used, sometimes idle. Demand Side Working Group. July 9. th. , 2015. 1. TAC Motion on LMP-G. On Oct 6. th. , 2011, TAC voted “to . affirm the WMS recommendation for the further development of the LMP minus G . option”. Paper By Mi . Xianghang. , . Siyuan. Tang, Zhengyi Li, . Xiaojing. Liao, Feng Qian, and . XiaoFeng. Wang. Presented by Dennis Xu. Contents of this Seminar. The motivation for this paper. There is a rising proxy service called .