Dear colleagues,
You are cordially invited to attend the next lecture in the KdVI General Mathematics Colloquium on Friday, April 29 at 16.00.
Max Welling (UvA) will speak about "How GNNs and Symmetries can help to solve PDEs" (see abstract below).
The lecture will be given in room C1.110 (Science Park 904) and can also be joined via Zoom https://uva-live.zoom.us/j/84560653677<https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fuva-live.…>
If you are interested in receiving the announcements for future talks at the KdVI General Mathematics Colloquium, please subscribe to our mailing list https://list.uva.nl/mailman/listinfo/kdvi-math-colloq.
We hope to see you then!
Best wishes,
Eni
(on behalf of the colloquium organizers)
-----------------------------------------------------
Abstract: Deep learning has seen amazing advances over the past years, completely replacing traditional methods in fields such as speech recognition, natural language processing, image and video analysis and so on. A particularly versatile deep architecture that has gained much traction lately is the graph neural network (GNN), of which transformers represent a special case. GNNs have the desirable property that they can process graph structured data while respecting permutation symmetry. Recently, GNNs have found new applications in scientific computation, for instance to predict the properties of molecules or to predict the forces that act on atoms when they evolve (e.g. fold). In this application it is also key that geometric symmetries, such as translation and rotation symmetries are taken into consideration. Professor Max Welling will report on yet another exciting application of using GNNs to solve partial differential equations (PDEs). It turns out that GNNs are an excellent tool to develop neural PDE integrators. Moreover, PDEs are full of surprising symmetries that can be leveraged to train neural integrators with less data. Professor Max Welling will discuss this very exciting new chapter in deep learning. He will end with a discussion of whether reversely, PDEs can also serve as a model for new deep architectures.
Joint work with Johannes Brandstetter and Daniel Worrall.
Dear all,
Today, April 22, we have Tor Lattimore from DeepMind speaking in the
thematic seminar.
*Tor Lattimore *(DeepMind, http://tor-lattimore.com)
*Friday April 22*, 16h00-17h00
Online on Zoom: *https://uva-live.zoom.us/j/88233925917*
Meeting ID: 882 3392 5917
*Minimax Regret for Partial Monitoring: Infinite Outcomes and
Rustichini's Regret *
The information ratio developed by Russo and Van Roy (2014) is a
powerful tool that was recently used to derive upper bounds on the
regret for challenging sequential decision-making problems. I will talk
about how a generalised version of this machinery can be used to derive
lower bounds and give an application showing that a version of mirror
descent is minimax optimal for partial monitoring using Rustichini's
definition of regret.
Seminar organizers:
Tim van Erven
Botond Szabo
https://mschauer.github.io/StructuresSeminar/
*Upcoming talks:
*Jun. 10,***Julia Olkhovskaya
<https://sites.google.com/view/julia-olkhovskaya/home>*, Vrije Universiteit
--
Tim van Erven<tim(a)timvanerven.nl>
www.timvanerven.nl
Dear all,
Happy Easter!
This Friday, April 22, we have Tor Lattimore from DeepMind speaking in
the thematic seminar.
*Tor Lattimore *(DeepMind, http://tor-lattimore.com)
*Friday April 22*, 16h00-17h00
Online on Zoom: *https://uva-live.zoom.us/j/88233925917*
Meeting ID: 882 3392 5917
*Minimax Regret for Partial Monitoring: Infinite Outcomes and
Rustichini's Regret *
The information ratio developed by Russo and Van Roy (2014) is a
powerful tool that was recently used to derive upper bounds on the
regret for challenging sequential decision-making problems. I will talk
about how a generalised version of this machinery can be used to derive
lower bounds and give an application showing that a version of mirror
descent is minimax optimal for partial monitoring using Rustichini's
definition of regret.
Seminar organizers:
Tim van Erven
Botond Szabo
https://mschauer.github.io/StructuresSeminar/
*Upcoming talks:
*Jun. 10,***Julia Olkhovskaya
<https://sites.google.com/view/julia-olkhovskaya/home>*, Vrije Universiteit
--
Tim van Erven<tim(a)timvanerven.nl>
www.timvanerven.nl
(apologies for cross posting)
At Delft University of Technology, we have a vacancy for a
3 year PostDoc on Reinforcement Learning in the Real World
This is in the context of the Mercury Machine Learning Lab, jointly with
the University of Amsterdam and booking.com. We will focus on
fundamental techniques in reinforcement learning, moticated by
real-world problems. Possible directions of interest are:
Bayesian reinforcement learning
Multiagent / concurrent reinforcement learning
Causal reinforcement learning
Full vacancy text can be found here:
https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?…
Please forward to potential candidates, and contact Matthijs Spaan or
myself in case of questions.
--
_______________________________________________
Dr. Frans Oliehoek
Associate Professor
Delft University of Technology
E-mail: f.a.oliehoek(a)tudelft.nl
www.fransoliehoek.net
Dear all,
On Friday April 22 we have Tor Lattimore from DeepMind speaking in the
thematic seminar.
*Tor Lattimore *(DeepMind, http://tor-lattimore.com)
*Friday April 22*, 16h00-17h00
Online on Zoom: *https://uva-live.zoom.us/j/88233925917*
Meeting ID: 882 3392 5917
*Minimax Regret for Partial Monitoring: Infinite Outcomes and
Rustichini's Regret *
The information ratio developed by Russo and Van Roy (2014) is a
powerful tool that was recently used to derive upper bounds on the
regret for challenging sequential decision-making problems. I will talk
about how a generalised version of this machinery can be used to derive
lower bounds and give an application showing that a version of mirror
descent is minimax optimal for partial monitoring using Rustichini's
definition of regret.
Seminar organizers:
Tim van Erven
Botond Szabo
https://mschauer.github.io/StructuresSeminar/
*Upcoming talks:
*Jun. 10,***Julia Olkhovskaya
<https://sites.google.com/view/julia-olkhovskaya/home>*, Vrije Universiteit
--
Tim van Erven<tim(a)timvanerven.nl>
www.timvanerven.nl
Dear all,
This Friday we have Nicolò Cesa-Bianchi from Università degli Studi di
Milano in the thematic seminar.
Note the unusual time, at *11h00* in the morning!
*Nicolò Cesa-Bianchi *(Università degli Studi di Milano,
https://cesa-bianchi.di.unimi.it/)
*Friday April 8*, 11h00-12h00
Online on Zoom: https://uva-live.zoom.us/j/83684658173
Meeting ID: 836 8465 8173
*The power of cooperation in networks of learning agents *
We study the power of cooperation in a network of communicating agents
that solve a learning task. Agents use an underlying communication
network to get information about what the other agents know. In the
talk, we show the extent to which cooperation allows to prove
performance bounds that are strictly better than the known bounds for
non-cooperating agents. Our results are formulated in the setting of
sequential decision-making with partial feedback.
Seminar organizers:
Tim van Erven
Botond Szabo
https://mschauer.github.io/StructuresSeminar/
*Upcoming talks:
*Apr. 22, *Tor Lattimore
<https://mschauer.github.io/StructuresSeminar/#Lattimore>*, DeepMind
Jun. 10,***Julia Olkhovskaya
<https://sites.google.com/view/julia-olkhovskaya/home>*, Vrije Universiteit
--
Tim van Erven<tim(a)timvanerven.nl>
www.timvanerven.nl
Dear Colleagues,
The following event might be appealing our communities.
All the best,
Rui
—
Dept. of Mathematics and Computer Science, TU/e
http://www.win.tue.nl/~rmcastro | +31 40 247 2499
Begin forwarded message:
From: statmathappli <statmathappli(a)inrae.fr<mailto:statmathappli@inrae.fr>>
Subject: StatMathAppli 2022, August 29 till September 2, Fréjus, France : registration is open!
Date: 28 March 2022 at 14:07:03 CEST
To: statmathappli <statmathappli(a)inrae.fr<mailto:statmathappli@inrae.fr>>
Dear colleague,
Please find below the annoucement for the conference StatMathAppli 2022.
Thank you in advance for forwarding it to your lab.
Best regards,
Christophe Giraud and Estelle Kuhn, for the organizing committee
-------------------------------
Dear colleague,
The next edition of StatMathAppli 2022 will take place at Villa Clythia in Fréjus, France, from August 29 till September 2:
statmathappli.mathnum.inrae.fr<https://eur02.safelinks.protection.outlook.com/?url=http%3A%2F%2Fstatmathap…>
The conference provides to European statisticians, including PhD and young researchers, the opportunity to meet, present their work, and initiate collaborations. The program includes (selected) contributed talks, and two courses given by world-renowned researchers.
For the 2022 Edition, we are delighted to welcome as guest lecturer
Rebecca Willett<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwillett.p…> (Chicago University) on "Regularization Induced by Neural Network Architectures" and
Richard Nickl<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.dpmms…> (Cambridge University) on "Bayesian non-linear statistical inverse problems".
Registration is open till Friday, June 17. (https://statmathappli.mathnum.inrae.fr/fr/inscription<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstatmatha…>)
Please note that, the number of places being limited, the application process consists in a pre-registration on the website, followed by a registration after validation of your application by email from the committee. First arrived, first served.
Abstract submission will be open from Monday, April 25 till Friday, June 24.
Looking forward to welcome you in Fréjus this summer!
Christophe Giraud and Estelle Kuhn, for the organizing committee
Dear all,
This Friday we have Nicolò Cesa-Bianchi from Università degli Studi di
Milano in the thematic seminar.
Note the unusual time, at *11h00* in the morning!
*Nicolò Cesa-Bianchi *(Università degli Studi di Milano,
https://cesa-bianchi.di.unimi.it/)
*Friday April 8*, 11h00-12h00
Online on Zoom: https://uva-live.zoom.us/j/83684658173
Meeting ID: 836 8465 8173
*The power of cooperation in networks of learning agents *
We study the power of cooperation in a network of communicating agents
that solve a learning task. Agents use an underlying communication
network to get information about what the other agents know. In the
talk, we show the extent to which cooperation allows to prove
performance bounds that are strictly better than the known bounds for
non-cooperating agents. Our results are formulated in the setting of
sequential decision-making with partial feedback.
Seminar organizers:
Tim van Erven
Botond Szabo
https://mschauer.github.io/StructuresSeminar/
*Upcoming talks:
*Apr. 22, *Tor Lattimore
<https://mschauer.github.io/StructuresSeminar/#Lattimore>*, DeepMind
Jun. 10,***Julia Olkhovskaya
<https://sites.google.com/view/julia-olkhovskaya/home>*, Vrije Universiteit
--
Tim van Erven<tim(a)timvanerven.nl>
www.timvanerven.nl