I am happy to announce that our work about Evidential Deep Learning methods for Uncertainty Quantification with @ch_hardmeier
and @jesfrellsen
got accepted at #TMLR! 🥳 (1/3) 🧵 https://openreview.net/forum?id=xqS8k9E75c

I also need to mention the #RLDM and #TMLR communities. Having venues such as these, that welcome cross-disciplinary work, is such a benefit to our ML research community.

Our paper can be seen at: https://openreview.net/forum?id=oKlEOT83gI

And our code (soon!): https://github.com/MLforHealth/DistDeD

(7/7)

🎉Last week, amid the #ICML2023 rush, we were informed that our paper (w/ Sonali Parbhoo and Marzyeh Ghassemi): "Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning" was accepted to
#TMLR! 🎉 #ReinforcementLearning #Healthcare

The AutoML conference is happy to announce the partnership with #TMLR. As part of the journal track of #AUTOML23, you can also submit papers accepted at #TMLR. After a lightweight review, you can get an "AutoML certificate" at #TMLR and present your paper at #AUTOML23.

A connection between sparse and low rank matrices. Let S be a sparse similarity matrix, for example the distances of the 3 nearest neighbours in a low dimensional manifold. Can you recover S if you have a low rank (dense) matrix L from in a high dimensional space? This paper provides a geometric interpretation for S = max(0,L). It proposes a decomposition algorithm, that can be modelled as a ReLU neural network layer.

#MachineLearning #SparseDecomposition #LowRank #TMLR
https://openreview.net/forum?id=p8gncJbMit

Controllable Generative Modeling via Causal Reasoning

Joey Bose, Ricardo Pio Monti, Aditya Grover

https://openreview.net/forum?id=Z44YAcLaGw

#NewPaper #TMLR

A Stochastic Optimization Framework for Fair Risk Minimization

Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami

https://openreview.net/forum?id=P9Cj6RJmN2

#NewPaper #TMLR