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Professor Bryce
United States
Registrace 10. 01. 2020
Davidson College
Data Structures for Deviation Payoffs (AAMAS Talk)
Paper abstract:
We present new data structures for representing symmetric normal-form games. These data structures are optimized for efficiently computing the expected utility of each unilateral pure-strategy deviation from a symmetric mixed-strategy profile. The cumulative effect of numerous incremental innovations is a dramatic speedup in the computation of symmetric mixed-strategy Nash equilibria, making it practical to represent and solve games with dozens to hundreds of players. These data structures naturally extend to role-symmetric and action-graph games with similar benefits.
Paper link: arxiv.org/abs/2302.13232
Julia library with the paper's experiments: github.com/Davidson-Game-Theory-Research/gameanalysis.jl
More-practical python library for solving role-symmetric games: github.com/egtaonline/gameanalysis
For background, see the playlist for my Algorithmic Game Theory course, especially videos 18-20: czcams.com/play/PLgPbN3w-ia_Md9sxkXhCIAmTITSOUDJz2.html
We present new data structures for representing symmetric normal-form games. These data structures are optimized for efficiently computing the expected utility of each unilateral pure-strategy deviation from a symmetric mixed-strategy profile. The cumulative effect of numerous incremental innovations is a dramatic speedup in the computation of symmetric mixed-strategy Nash equilibria, making it practical to represent and solve games with dozens to hundreds of players. These data structures naturally extend to role-symmetric and action-graph games with similar benefits.
Paper link: arxiv.org/abs/2302.13232
Julia library with the paper's experiments: github.com/Davidson-Game-Theory-Research/gameanalysis.jl
More-practical python library for solving role-symmetric games: github.com/egtaonline/gameanalysis
For background, see the playlist for my Algorithmic Game Theory course, especially videos 18-20: czcams.com/play/PLgPbN3w-ia_Md9sxkXhCIAmTITSOUDJz2.html
zhlédnutí: 368
Video
Counterfactual Regret Minimization (AGT 26)
zhlédnutí 6KPřed rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 14 - Wednesday.
Sequential (and Perfect Bayesian) Equilibrium (AGT 25)
zhlédnutí 2,5KPřed rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 14 - Monday.
Subgame Perfection and Backwards Induction (AGT 24)
zhlédnutí 787Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 13 - Wednesday.
Extensive Form Games (AGT 23)
zhlédnutí 369Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 13 - Monday.
Action-Graph Games (AGT 22)
zhlédnutí 217Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 12 - Wednesday.
Congestion Games (AGT 21)
zhlédnutí 618Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 12 - Monday.
Data Structures for Symmetric Games (AGT 20)
zhlédnutí 241Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 11 - Wednesday. This video covers ideas from my recent paper: arxiv.org/abs/2302.13232
Gradient Descent for Nash (AGT 19)
zhlédnutí 296Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 11 - Monday.
Replicator Dynamics (AGT 18)
zhlédnutí 1KPřed rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 10 - Wednesday.
Fictitious Play and Regret Matching (AGT 17)
zhlédnutí 1,5KPřed rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 10 - Monday.
Complexity of Nash: PPAD (AGT 16)
zhlédnutí 347Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 9 - Wednesday.
Reductions and Why Zero Sum only Helps with Two Players (AGT 15)
zhlédnutí 231Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 9 - Monday.
Finding (Coarse) Correlated Equilibria with Linear Programming (AGT 14)
zhlédnutí 1,3KPřed rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 7 - Wednesday.
Finding Zero Sum Nash Equilibria with Linear Programming (AGT 13)
zhlédnutí 695Před rokem
Davidson CSC 383: Algorithmic Game Theory, S23. Week 7 - Monday.
Nash Algorithm Starting Points (AGT 12)
zhlédnutí 288Před rokem
Nash Algorithm Starting Points (AGT 12)
Symmetric Games and Sperners Lemma (AGT 11)
zhlédnutí 379Před rokem
Symmetric Games and Sperners Lemma (AGT 11)
Nash's Theorem: Every Game has an Equilibrium (AGT 10)
zhlédnutí 1,1KPřed rokem
Nash's Theorem: Every Game has an Equilibrium (AGT 10)
Equilibria with Pre-Commitment: Stackelberg & Coarse Correlated (AGT 09)
zhlédnutí 420Před rokem
Equilibria with Pre-Commitment: Stackelberg & Coarse Correlated (AGT 09)
Nash Refinements: Trembling Hand and Evolutionary Stability (AGT 08)
zhlédnutí 1,4KPřed rokem
Nash Refinements: Trembling Hand and Evolutionary Stability (AGT 08)
Nash Approximation: ε-Equilibria (AGT 07)
zhlédnutí 503Před rokem
Nash Approximation: ε-Equilibria (AGT 07)
Predicting Joint Behavior with Correlated Equilibria (AGT 06)
zhlédnutí 1,3KPřed rokem
Predicting Joint Behavior with Correlated Equilibria (AGT 06)
Predicting Strategies with Mixed Nash Equilibria (AGT 05)
zhlédnutí 526Před rokem
Predicting Strategies with Mixed Nash Equilibria (AGT 05)
Predicting Actions with Dominance and Pure-Nash (AGT 04)
zhlédnutí 430Před rokem
Predicting Actions with Dominance and Pure-Nash (AGT 04)
Von Neumann-Morgenstern Utility (AGT 02)
zhlédnutí 1,7KPřed rokem
Von Neumann-Morgenstern Utility (AGT 02)
Approximation Algorithms (Algorithms 25)
zhlédnutí 3,4KPřed rokem
Approximation Algorithms (Algorithms 25)
Do you mean that RESNET is just a skip connection not an individual network ?????????
That crazy, i can't imagine for games with many possibilities
Thanks you, it's crazy. I take notes
great video, will finish the deep learning playlist, found it's really explained , just one question: why is that ''the gradient vector will point in the direction that increase the loss''? 19:08
Thanks for such a detailed explanation/
nice explanation, thank you very much Professor Bryce
Hi, thank you do you have any pytorch application?
Awesome Dude. I love your videos!!
Amazing expalinaton. Thank you sir
Great video! "Better response function" is a brilliant description.
Brilliant explanation! Thank you so much, Professor Bryce!
great!
I am writing a thesis on content-based image retrieval and I had to understand the ResNet architecture in-depth and by far this is the most transparent explanation ever!!
I absolutely appreciate your methodic approach to this subject. You provided not only the "big picture" but also showed each frame and explained what was happening; you even took the time to do a recursive function. I am learning Java as my first high-level programming language, and stack diagrams are part of the exercises. Thank you for your work.
Another example of a random youtuber with very less subscriber explaining a complex topic so brilliantly... Thankyou so much sir
this video is goated
Thank You❤
Thanks for your video.
Brilliant explanation, the 3D diagrams were excellent and I could understand some tricky concepts, thank you so much!
Nice watch.
Amazing video! Thank you so much!
Hey! I happened to stumble across these videos before my final exam, and everything is starting to click. Thank you so much for your awesome stack diagrams!
Superb intro for a non-mathematican! Essence is well explained.
Best lecturer in this field ever seen. Thank you for these knowledge.
Kis kis ko lgta hai ki Ashish sir se behtr toh Baba juice ka maalik hai 🫄🏿🥹
So clear and well explained. Thank you!
I want to thank you. Thank you from Vietnam. I hope that you love what you do and achieve more success. Hope that there is a course about randomize algorithm in the future
ur illustration is so clear, thank you a lot!
Thank you, wish I found this channel earlier in my Algorithms course haha
give me your email
So good explanation
What an explanation <3 <3 <3 I wish my professor had explained like this at the uni
Thanks ❤
Thanks prof! Studying for my algorithm exam for UBC
This tutorial is so clear that I can follow along as a non-native English speaker. Thanks a lot!
Hope sir that you can make more new videos on deep learning - your videos are succinct and also full of information
This is by far the best explained CFR lesson. Thank you for doing this!
Best explanation i came across resnet so far.
Brilliant explanation!!!
great explanation, simple and straightforward.
For the case of undirected graph is not it better to write E={{1,2}, ..., {2.4}} rather than E={(1,2), ..., (2.4)}?
Thanks
Awesome explanations, thanks
Thanks. This is the clearest explanation on this topic
Awesome explanation. Got me through a learning hurdle that several others could not.
when's the new drop coming?
More videos please! Great work, you make hard things easier to visualise. I hope you get back to making CZcams videos ❤
You are fucking awesome Bro 🙏
thank you for the great explanation