Dear ML Netherlands list,
Forwarding this message, which may be of interest.
In general: feel free to advertise machine learning related vacancies on
the list, and also do advertise your local seminars when you think they
may be of broader interest.
Best,
Tim
-------- Forwarded Message --------
Subject: [ML-news] Three 3-year positions in teaching (general AI) in
Groningen, NL
Date: Fri, 21 Oct 2022 08:48:29 -0700 (PDT)
From: H. Jaeger <h.jaeger(a)rug.nl>
To: Machine …
[View More]Learning News <ml-news(a)googlegroups.com>
The AI Department in the Bernoulli Institute for Mathematics, Computer
Science and Artificial Intelligence
(https://www.rug.nl/research/bernoulli/) at the University of Groningen
(https://www.rug.nl/) offers three 3-year positions for teaching in
general AI, with competitive salaries, in a most welcoming and
high-ranked working environment (top-100 university,
https://www.rug.nl/about-ug/profile/facts-and-figures/position-internationa…).
The application deadline is November 7, 2022. Formal prerequisites are a
PhD degree; outstanding holders of a MSc degree will also be considered.
Details can be found at
https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0…
- Herbert Jaeger
--
Dr. Herbert Jaeger
Professor of Computing in Cognitive Materials
Rijksuniversiteit Groningen
Faculty of Science and Engineering - CogniGron
Bernoulliborg
Nijenborgh 9, 9747 AG Groningen
office: Bernoulliborg 402
phone: +31 (0) 631 921588
COVID home office phone: +49 (0) 4209 930403
web: www.ai.rug.nl/minds/
--
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Dear colleagues,
On Friday 4 November we will have a seminar with the two Van Wijngaarden
award winners: Marta Kwiatkowska (University of Oxford) and Susan Murphy
(Harvard University) at the Centrum Wiskunde & Informatica (CWI) in
Amsterdam.
The meeting will take place between 15:00 and 17:00 in the Euler room
(Amsterdam Science Park Congress Center), with drinks available
afterwards. The program is as follows:
* 15:00-15:45: Marta Kwiatkowska: "Safety and robustness for deep
…
[View More]learning with provable guarantees"
* 15:45-16:00: Questions and small break
* 16:00-16:45: Susan Murphy: "Inference for Longitudinal Data After
Adaptive Sampling"
* 16:45-end: Questions followed by drinks
Abstracts of the talks are copied below,
Best regards,
Felix Lucka, Jannis Teunissen & Peter Grunwald
Marta Kwiatkowska (University of Oxford): Safety and robustness for deep
learning with provable guarantees
Abstract: Computing systems are becoming ever more complex, with
decisions increasingly often based on deep learning components. This
lecture will describe progress with developing automated verification
techniques for deep neural networks to ensure safety and robustness of
their decisions. The lecture will conclude with an overview of the
challenges in this field.
Susan Murphy (Harvard University): Inference for Longitudinal Data After
Adaptive Sampling
Abstract: Adaptive sampling methods, such as reinforcement learning (RL)
and bandit algorithms, are increasingly used for the real-time
personalization of interventions in digital applications like mobile
health and education. As a result, there is a need to be able to use the
resulting adaptively collected user data to address a variety of
inferential questions, including questions about time-varying causal
effects. However, current methods for statistical inference on such data
(a) make strong assumptions regarding the environment dynamics, e.g.,
assume the longitudinal data follows a Markovian process, or (b) require
data to be collected with one adaptive sampling algorithm per user,
which excludes algorithms that learn to select actions using data
collected from multiple users. These are major obstacles preventing the
use of adaptive sampling algorithms more widely in practice. In this
work, we provide theory for common Z-estimators based on adaptively
sampled data. The inference is valid even when observations are
non-stationary and highly dependent over time, and (b) allow the online
adaptive sampling algorithm to learn using the data of all users.
Furthermore, our inference method is robust to miss-specification of the
reward models used by the adaptive sampling algorithm. This work is
motivated by our work in designing the Oralytics oral health clinical
trial in which an RL adaptive sampling algorithm will be used to select
treatments, yet valid statistical inference is essential for conducting
primary data analyses after the trial is over.
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Dear all,
TU Delftis proud to invite applications for an Assistant Professor
(Development Track) position in integrative data science with focus on
health. We welcome candidates engaged in all aspects of data science,
with preference on applications in health disciplines. The Assistant
Professor will be appointed at the Faculty of EEMCS, in Delft Institute
of Applied Mathematics, in the Applied Probability group.
With this position, you will be part to the national ‘RECENTRE
<https://…
[View More]www.4tu.nl/recentre/>: Risk-based lifEstyle Change: daily-lifE
moNiToring and Recommendations’ program, funded by the 4TU.Federation
<https://www.4tu.nl/>. Within RECENTRE, the aim is to empower patients
to play a leading role in their lifestyle and health within their own
environment. Integrated smart sensor systems and dynamic risk profiles
will be designed to support high patient engagement.This can lead to
personalized adaptive recommendations with considerable potential for
adoption and lifestyle change. To get authentic and up-to date risk
profiles, sensor and historic data will be complemented by expert and
patient subjective assessments, as well as by patient personal
information.Novel methods will be consequently needed to integrate these
disparate data sources.
Here
<https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details/…>you
can fine more details on the position and how to submit your
application. The deadline for application is October 30th.
With best regards,
Tina Nane
Delft Institute of Applied Mathematics
Delft University of Technology
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Hi all,
we warmly invite you to submit your application if interested in joining us as a tenure track Assistant Professor in Computer Vision and Machine Learning at the University of Amsterdam, one of the most exciting AI ecosystems at the moment.
The position is together with being a lab manager of the POP-AART lab, which is set up between the University of Amsterdam, the renowned Netherlands Cancer Institute, and Elekta, the leader manufacturer of Adaptive Radiotherapy equipment.
The …
[View More]position comes with 6 strong PhDs working on generative models, geometry, segmentation, registration, reinforcement learning, inverse models, all focusing on adaptive radiotherapy.
https://vacatures.uva.nl/UvA/job/Assistant-Professor-in-Computer-Vision-and…
For any questions, please do not hesitate to contact me at egavves(a)uva.nl.
Best,
Stratis
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Dear all,
In November we will have two in person talks in the thematic seminar in
rapid succession at the UvA.
- On Thursday November 10 Damien Garreau from the Université Côte d'Azur
will speak about his analysis of the popular LIME method for explainable
machine learning.
- And on November 14 or 15, Umut Şimşekli from INRIA/École Normale
Supérieure will speak about his new generalization bounds for deep
neural networks.
*Damien Garreau *(Université Côte d'Azur,
https://sites.google.…
[View More]com/view/damien-garreau/home)
*Thursday November 10*, 16h00-17h00
In person, at the University of Amsterdam
Room: TBA
*
**What does LIME really see in images?
*The performance of modern algorithms on certain computer vision tasks
such as object recognition is now close to that of humans. This success
was achieved at the price of complicated architectures depending on
millions of parameters and it has become quite challenging to understand
how particular predictions are made. Interpretability methods propose to
give us this understanding. In this talk, I will present a recent result
about LIME, perhaps one of the most popular methods. *
*
*Upcoming talks:
*- Nov. 14 or 15, Umut Şimşekli <https://www.di.ens.fr/~simsekli/> from
INRIA/École Normale Supérieure will speak about new generalization
bounds for deep neural networks.
**
Seminar organizers:
Tim van Erven
Botond Szabo
https://mschauer.github.io/StructuresSeminar/
--
Tim van Erven<tim(a)timvanerven.nl>
www.timvanerven.nl
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Forwarding on behalf of the YEQT organizers:
Subject:
Workshop YEQT XV "Machine Learning for Stochastic Networks"
From:
"Verleijsdonk, Peter" <p.verleijsdonk(a)tue.nl>
Date:
06/10/2022, 15:21
To:
"machine-learning-nederland(a)list.uva.nl"
<machine-learning-nederland(a)list.uva.nl>
CC:
"Sanders, Jaron" <jaron.sanders(a)tue.nl>
Dear recipient,
It is our pleasure to invite you to theannual ‘Young European Queueing
Theorists’ (YEQT) workshop.The15th edition,…
[View More]YEQT 2022, will be hosted
byEindhovenUniversity of Technology and held
from2-4November 2022atEurandom.The event provides an excellent
opportunity for developing researchers to interact and exchange ideas in
an informal, friendly, yet research-focused setting inEindhoven, the
Netherlands.
This year’s theme for the YEQT workshop is Machine Learning for
Stochastic Networks. The aim of YEQT XV is to bring together senior
researchers who have made substantial contributions to advancing machine
learning techniques and the optimization of stochastic networks. The
methodological focus will be on research that combines theoretical
stochastic modeling and optimization together with modern machine
learning techniques.
Confirmed keynote speakers are:
*
Ton Dieker (Columbia)
*
Tor Lattimore (Deepmind)
*
LaurentMassoulié (Inria)
*
AlexandreProutiere (KTH)
Pleaseseethe website for the full program, titles and abstracts, and
registration:https://www.eurandom.tue.nl/event/workshop-yeqt-xv-machine-learning-for-stochastic-networks/
<https://www.eurandom.tue.nl/event/workshop-yeqt-xv-machine-learning-for-sto…>
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Dear all,
The next speaker in our seminar on Uncertainty Quantification and Machine Learning in scientific computing is Joseph Bakarji, working at the University of Washington with Steven Brunton and Nathan Kutz (https://www.josephbakarji.com/). The topic of his talk will be the use of sparse identification and machine learning for discovering dimensionless groups, see abstract below. The (online) talk will take place on October 13th at 16h CET.
Best regards,
Wouter Edeling
Join Zoom …
[View More]Meeting
https://cwi-nl.zoom.us/j/83204615411?pwd=VmNLelBwWVZLaEU4QWFYYXNZYS9qZz09
Meeting ID: 832 0461 5411
Passcode: 669720
13 October 2022 16h00 CET: Joseph Bakarji (University of Washington) : Discovering dimensionless groups from data using constrained sparse identification and deep learning methods
Dimensional analysis is a robust technique for extracting insights and finding symmetries in physical systems, especially when the governing equations are not known. The Buckingham Pi theorem provides a procedure for finding a set of dimensionless groups from given parameters and variables. However, this set is often non-unique which makes dimensional analysis an art that requires experience with the problem at hand. In this talk, I'll propose a data-driven approach that takes advantage of the symmetric and self-similar structure of available measurement data to discover dimensionless groups that best collapse the data to a lower dimensional space according to an optimal fit. We develop three machine learning methods that use the Buckingham Pi theorem as a constraint: (i) a constrained optimization problem with a nonparametric function, (ii) a deep learning algorithm (BuckiNet) that projects the input parameter space to a lower dimension in the first layer, and (iii) a sparse identification of differential equations method to discover differential equations with dimensionless coefficients that parameterize the dynamics. I discuss the accuracy and robustness of these methods when applied to known nonlinear systems where dimensionless groups are known, and propose a few avenues for future research.
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