Top Tweets for #StatML
Sad after #AISTATS2025 and #ICLR2025 notifications? When a door closes, a bigger one opens π
If you have a fantastic paper on #uncertainty #AI #ML #causality #statML #probabilisticmodels #reasoning #impreciseprobabilities etc consider submitting to #UAI2025π§π· deadline 10 Feb π₯
The 41st Conference on #Uncertainty in #AI will be held in Rio de Janeiro π§π·, July 21-25!
The CfP is out πhttps://t.co/bFnMjEGWDe
π¨ Feb 10: Paper submission
π£οΈ Apr 3-10: rebuttal period
π/π May 6: Author notification
#UAI2025 #ML #stats #learning #reasoning #uncertainty
π¨ Fully-funded PhD Positions in Statistical ML! π¨
Passionate about bringing epistemic intelligence to machine learning? Join my lab @ CCDS NTU Singapore πΈπ¬!
π Learn more: https://t.co/k0lCzXNNq1
π RTs appreciated!
#PhDRecruitment #StatML
New preprint out now on arXiv!
Dropout Regularization in Extended GLMs based on Double Exponential Families
by Benedikt LΓΌtke Schwienhorst, Lucas Kock, David J. Nott and Nadja Klein from @RCTrustworthy @uniallianz (1,2,4) & @NUSingapore (3)
#statml #machinelearning
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families. (arXiv:2305.06625v1 [https://t.co/zjV5HgYw5a]) https://t.co/EoQqBshEs9
Check out the latest work from our #StatML student Michael Hutchinson (@MHutchinson141), to appear in #NeurIPS2021.
https://t.co/A9C4xRljym
Ever wondered how to create vector-valued Gaussian processes on arbitrary manifolds? Wonder no more!
Our latest paper Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels does exactly that!
Links to all: https://t.co/zYuzy5698J
π€ Neural Ordinary Differential Equations
βοΈ Tian Qi Chen et al.
π https://t.co/uLwPdbtbAr
π Tweeted by @DavidDuvenaud et al.
#Learning #csLG #csAI #statML
π€ A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
βοΈ Saurabh Arora, Prashant Doshi
π https://t.co/p6N8VaX0hA
π Tweeted by @icoxfog417 et al.
#Learning #csLG #statML
π€ Polynomial Regression As an Alternative to Neural Nets
βοΈ Xi Cheng et al.
π https://t.co/sefzQnjrVY
π Tweeted by @alex_yakubovich, @xamat et al.
#Learning #csLG #statML
π€ Rotation Equivariant CNNs for Digital Pathology
βοΈ Bastiaan S. Veeling et al.
π https://t.co/eElvUrs8od
π Tweeted by @BasVeeling, @jmtomczak et al.
#ComputerVisionandPatternRecognition #csCV #csLG #statML
π€ Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
βοΈ Kurtland Chua et al.
π https://t.co/aE3Kii0MZQ
π Tweeted by @RogerGrosse, @v_vashishta et al.
#Learning #csLG #csAI #csRO #statML
π€ Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
βοΈ Lechao Xiao et al.
π https://t.co/03My6rcfSH
π Tweeted by @johnplattml, @yasamanbb et al.
#MachineLearning #statML #csLG
π€ Autoregressive Quantile Networks for Generative Modeling
βοΈ Georg Ostrovski et al.
π https://t.co/VKwoL2MRSI
π Tweeted by @DeepMindAI, @mosko_mule, @Miles_Brundage et al.
#Learning#autoregressive #generativemodel #machinelearning #quantileregression #csLG #statML
Large-Scale Stochastic Sampling from the Probability Simplex (Baker et al.): stochastic gradient MCMC on the probability simplex via the stochastic CIR process https://t.co/MSh30PToGS #MCMC #MachineLearning #StatML #BayesianInference
π€ Differentiable Compositional Kernel Learning for Gaussian Processes
βοΈ Shengyang Sun et al.
π https://t.co/eOK4keMWfm
π Tweeted by @RogerGrosse et al.
#Learning#MachineLearning #ICML #csLG #statML
π€ Yes, but Did It Work?: Evaluating Variational Inference
βοΈ Yuling Yao et al.
π https://t.co/SzjeGqXFyk
π Tweeted by @_hylandSL et al.
#MachineLearning #statML #statCO
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