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Dynamic Online-Advertising Auctions as Stochastic Scheduling

Dynamic Online-Advertising Auctions as Stochastic Scheduling

  • phoebe-click
  • 10 Slides

.edu R. Srikant University of Illinois at Urbana-C...

Approximate Clustering without the Approximation MariaFlorina Balcan Avrim Blum

Approximate Clustering without the Approximation MariaFlorina Balcan Avrim Blum

  • phoebe-click
  • 10 Slides

This quest for better approximation algorithms is...

The Work of Martin Hairer Martin Hairer has made a major breakthrough in the study of stochastic partial dierential equations by creating a new theory that provides tools for attacking problems that u

The Work of Martin Hairer Martin Hairer has made a major breakthrough in the study of stochastic partial dierential equations by creating a new theory that provides tools for attacking problems that u

  • phoebe-click
  • 4 Slides

The subject of di64256erential equations has its ...

Journal of Machine Learning Research Su bmitted Revised Published SGDQN Careful QuasiNewton Stochastic Gradient Descent Antoine Bordes ANTOINE BORDES LIP

Journal of Machine Learning Research Su bmitted Revised Published SGDQN Careful QuasiNewton Stochastic Gradient Descent Antoine Bordes ANTOINE BORDES LIP

  • luanne-stotts
  • 18 Slides

FR LIP6 Universit Pierre et Marie Curie 104 Avenu...

Principles of Communication The communication process Sources of information communication channels modulati on process and communication networks Representation of signals and systems Signals Continu

Principles of Communication The communication process Sources of information communication channels modulati on process and communication networks Representation of signals and systems Signals Continu

  • ellena-manuel
  • 9 Slides

Stochastic processes Probability theory random pr...

Redundancy Reduction and Sustained Firing with Stochastic Depr essing Synapses Mark S

Redundancy Reduction and Sustained Firing with Stochastic Depr essing Synapses Mark S

  • giovanna-bartolotta
  • 8 Slides

Goldman 13 Pedr aldonado and L Abbott 12 olen Cen...

Exact Stochastic Simulation of Coupled Chemical Reactions Daniel T

Exact Stochastic Simulation of Coupled Chemical Reactions Daniel T

  • ellena-manuel
  • 22 Slides

Gillesple Danlel T Gillespie Research Department ...

SGDQN Careful QuasiNewton Stochastic Gradient Descent Antoine Bordes Leon Bottou Patrick Gallinari To cite this version Antoine Bordes Leon Bottou Patrick Gallinari

SGDQN Careful QuasiNewton Stochastic Gradient Descent Antoine Bordes Leon Bottou Patrick Gallinari To cite this version Antoine Bordes Leon Bottou Patrick Gallinari

  • conchita-marotz
  • 19 Slides

SGDQN Careful QuasiNewton Stochastic Gradient Des...

Stochastic Alternating Direction Method of Multipliers Hua Ouyang houyangcc

Stochastic Alternating Direction Method of Multipliers Hua Ouyang houyangcc

  • ellena-manuel
  • 9 Slides

gatechedu Niao He nhe6isyegatechedu Long Q Tran lt...

Using Dual Approximation Algorithms for Scheduling Problems Theoretical and Practical Results DORIT S

Using Dual Approximation Algorithms for Scheduling Problems Theoretical and Practical Results DORIT S

  • tatyana-admore
  • 19 Slides

HOCHBAUM University of California Berkeley Calijo...

Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI DORIT S

Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI DORIT S

  • briana-ranney
  • 7 Slides

HOCHBAUM AND WOLFGANG MAASS University of Califor...

Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science University of Toronto Toronto Ontario Canada

Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science University of Toronto Toronto Ontario Canada

  • luanne-stotts
  • 9 Slides

tangcstorontoedu Ruslan Salakhutdinov Department ...

UAV Cooperative Control with Stochastic Risk Models Alborz Geramifard Joshua Redding Nicholas Roy and Jonathan P

UAV Cooperative Control with Stochastic Risk Models Alborz Geramifard Joshua Redding Nicholas Roy and Jonathan P

  • giovanna-bartolotta
  • 8 Slides

How Abstract Risk and reward are fundamental con...

Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization Shai ShalevShwartz SHAIS CS HUJI AC IL School of Computer Science and Engineering The Hebrew University Jerusa

Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization Shai ShalevShwartz SHAIS CS HUJI AC IL School of Computer Science and Engineering The Hebrew University Jerusa

  • ellena-manuel
  • 9 Slides

Beijing China Abstract We introduce a proximal ve...

Trading Computation for Communication Distributed Stochastic Dual Coordinate Ascent Tianbao Yang NEC Labs America Cupertino CA tyangneclabs

Trading Computation for Communication Distributed Stochastic Dual Coordinate Ascent Tianbao Yang NEC Labs America Cupertino CA tyangneclabs

  • pamella-moone
  • 9 Slides

com Abstract We present and study a distributed op...

Stochastic Neighbor Compression Matt J

Stochastic Neighbor Compression Matt J

  • tatyana-admore
  • 9 Slides

Kusner MKUSNER WUSTL EDU Stephen Tyree SWTYREE WU...

Dirichlet Process Yee Whye Teh University College London Related keywords Bayesian nonparametrics stochastic processes clustering innite mixture model BlackwellMacQueen urn scheme Chinese restaurant

Dirichlet Process Yee Whye Teh University College London Related keywords Bayesian nonparametrics stochastic processes clustering innite mixture model BlackwellMacQueen urn scheme Chinese restaurant

  • pamella-moone
  • 13 Slides

De64257nition The Dirichlet process is a stochast...

SIGNAL APPROXIMATION USING THE BILINEAR TRANSFORM Archana Venkataraman Alan V

SIGNAL APPROXIMATION USING THE BILINEAR TRANSFORM Archana Venkataraman Alan V

  • tawny-fly
  • 4 Slides

Oppenheim MIT Digital Signal Processing Group 77 ...

Accelerated Gradient Methods for Stochastic Optimization and Online Learning Chonghai Hu James T

Accelerated Gradient Methods for Stochastic Optimization and Online Learning Chonghai Hu James T

  • tatyana-admore
  • 9 Slides

Kwok Weike Pan Department of Computer Science an...

ARALLEL STOCHASTIC HILL CLIMBING WITH SMALL TEAMS Brian Gerk ey Sebastian Thrun rticial Intel ligenc ab Stanfor University Stanfor d CA USA gerk eyai

ARALLEL STOCHASTIC HILL CLIMBING WITH SMALL TEAMS Brian Gerk ey Sebastian Thrun rticial Intel ligenc ab Stanfor University Stanfor d CA USA gerk eyai

  • tawny-fly
  • 12 Slides

stanfo rdedu thrunstanfo rdedu Geo Gordon Center f...

JumpDiusion Stock Return Models in Finance Stochastic Process Density with UniformJump Amplitude Floyd B

JumpDiusion Stock Return Models in Finance Stochastic Process Density with UniformJump Amplitude Floyd B

  • alexa-scheidler
  • 7 Slides

Hanson Laboratory for Advanced Computing Universi...

AG Gaussian pr ocesses The class of Gaussian processes is one of the most widely used families of stochastic processes for mod eling dependent data observed over time or space or time and space

AG Gaussian pr ocesses The class of Gaussian processes is one of the most widely used families of stochastic processes for mod eling dependent data observed over time or space or time and space

  • luanne-stotts
  • 6 Slides

The popularity of such processes stems primarily ...

Dynamic Programming for Partially Observable Stochastic G ames Daniel S

Dynamic Programming for Partially Observable Stochastic G ames Daniel S

  • danika-pritchard
  • 6 Slides

Bernstein Dept of Computer Science University of ...

EE Winter Lecture Linear Quadratic Stochastic Control linearquadratic stochastic control problem solution via dynamic programming Linear stochastic system linear dynamical system over nite time h

EE Winter Lecture Linear Quadratic Stochastic Control linearquadratic stochastic control problem solution via dynamic programming Linear stochastic system linear dynamical system over nite time h

  • jane-oiler
  • 16 Slides

N is the process noise or disturbance at time ...

L Vandenberghe EEA Fall Lecture Piecewiselinear optimization piecewiselinear minimization and norm approximation examples modeling software Linear and ane functions linear function a function is

L Vandenberghe EEA Fall Lecture Piecewiselinear optimization piecewiselinear minimization and norm approximation examples modeling software Linear and ane functions linear function a function is

  • kittie-lecroy
  • 24 Slides

Vandenberghe EE236A Fall 201314 Lecture 2 Piecewi...

Communicated by Halbert White Universal Approximation Using RadialBasisFunction Networks J

Communicated by Halbert White Universal Approximation Using RadialBasisFunction Networks J

  • lindy-dunigan
  • 12 Slides

Park I W Sandberg Department of Electrical arid C...

Chapter APPROXIMATION BY RADIAL BASIS FUNCTION NETWORKS pplication to Option Pricin A

Chapter APPROXIMATION BY RADIAL BASIS FUNCTION NETWORKS pplication to Option Pricin A

  • lois-ondreau
  • 12 Slides

Lendasse J Lee E de Bodt V Wertz M Verleysen ...

Communicated by Halbert White Universal Approximation using RadialBasisFunction Networks J

Communicated by Halbert White Universal Approximation using RadialBasisFunction Networks J

  • giovanna-bartolotta
  • 12 Slides

Park I W Sandberg Department ot Electrical and Co...

ight Approximation Algorithms for Maximum General Assignment Problems Lisa Fleischer Michel X

ight Approximation Algorithms for Maximum General Assignment Problems Lisa Fleischer Michel X

  • lois-ondreau
  • 10 Slides

Goemans ahab S Mirrokni Maxim Sviridenk Abstract ...

Computing Stackelberg Equilibria in Discounted Stochastic Games Corrected Version Yevgeniy Vorobeychik Sandia National Laboratories Livermore CA yvorobesandia

Computing Stackelberg Equilibria in Discounted Stochastic Games Corrected Version Yevgeniy Vorobeychik Sandia National Laboratories Livermore CA yvorobesandia

  • test
  • 7 Slides

gov Satinder Singh Computer Science and Engineerin...

The Risky SteadyState By Nicolas Coeurdacier H el ene Rey and Pablo Winant I

The Risky SteadyState By Nicolas Coeurdacier H el ene Rey and Pablo Winant I

  • lindy-dunigan
  • 4 Slides

Approximation around a risky steadystate A The ri...

Variance Reduction for Stochastic Gradient Optimization Chong Wang Xi Chen Alex Smola Eric P

Variance Reduction for Stochastic Gradient Optimization Chong Wang Xi Chen Alex Smola Eric P

  • briana-ranney
  • 9 Slides

Xing Carnegie Mellon University University of Cal...

Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks Keith Winstein Anirudh Sivaraman and Hari Balakrishnan MIT Computer Science and Articial Intelligence Laboratory Camb

Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks Keith Winstein Anirudh Sivaraman and Hari Balakrishnan MIT Computer Science and Articial Intelligence Laboratory Camb

  • olivia-moreira
  • 13 Slides

keithwanirudhhari mitedu Abstract Sprout is an en...

A Tight Linear Time Approximation for Unconstrained Submodular Maximization Niv Buchbinder Statistics and Operations Research Dept

A Tight Linear Time Approximation for Unconstrained Submodular Maximization Niv Buchbinder Statistics and Operations Research Dept

  • pamella-moone
  • 10 Slides

Tel Aviv University Tel Aviv Israel nivbuchbinder...

Copyright by Karl Sigman IEOR Notes on Brownian Motion We present an introduction to Brownian motion an important continuoustime stochastic pro cess that serves as a continuoustime analog to the

Copyright by Karl Sigman IEOR Notes on Brownian Motion We present an introduction to Brownian motion an important continuoustime stochastic pro cess that serves as a continuoustime analog to the

  • yoshiko-marsland
  • 11 Slides

Throughout we use the following notation for the ...

Doubly Stochastic Variational Bayes for nonConjugate Inference Michalis K

Doubly Stochastic Variational Bayes for nonConjugate Inference Michalis K

  • karlyn-bohler
  • 9 Slides

Titsias MTITSIAS AUEB GR Department of Informatic...

EE Winter Lecture Linear Quadratic Stochastic Control with Partial State Observation partially observed linearquadratic stochastic control proble estimationcontrol separation principle solution via

EE Winter Lecture Linear Quadratic Stochastic Control with Partial State Observation partially observed linearquadratic stochastic control proble estimationcontrol separation principle solution via

  • ellena-manuel
  • 20 Slides

N with state input and process noise linear ...

A PolynomialTime Approximation Algorithm for Joint Probabilistic Data Association Songhwai Oh and Shankar Sastry Abstract Joint probabilistic data association JPDA is a powerful tool for solving dat

A PolynomialTime Approximation Algorithm for Joint Probabilistic Data Association Songhwai Oh and Shankar Sastry Abstract Joint probabilistic data association JPDA is a powerful tool for solving dat

  • tatyana-admore
  • 6 Slides

However the exact compu tation of association pro...

Chance constrained optimization chance constraints and percentile optimization chance constraints for logconcave distributions convex approximation of chance constraints sources Rockafellar Uryasev

Chance constrained optimization chance constraints and percentile optimization chance constraints for logconcave distributions convex approximation of chance constraints sources Rockafellar Uryasev

  • olivia-moreira
  • 22 Slides

e EE364A Chance Constrained Optimization brPage 7...

Detecting High LogDensities an Approximation for Densest Subgraph Aditya Bhaskara Moses Charikar Eden Chlamtac Uriel Feige Aravindan Vijayaraghavan Abstract In the Densest Subgraph problem given a

Detecting High LogDensities an Approximation for Densest Subgraph Aditya Bhaskara Moses Charikar Eden Chlamtac Uriel Feige Aravindan Vijayaraghavan Abstract In the Densest Subgraph problem given a

  • phoebe-click
  • 24 Slides

There is a signi64257cant gap between the best kn...

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