As far as I know, we cannot choose the exact number of papers we review at top conferences. This means I review much less than I would actually like to. I would sincerely be happy to review two papers for each top conference (ICLR, NeurIPS, ICML, AISTATS, etc.), but being asked to review five to seven papers per conference is far too much.
we show identifiability of latent switching dynamical systems with arbitrary time lag
proof based on double induction, applications to deep generative models for finance๐ and climate๐data
the best result re non-linear SDS from us so far๐lead by the marvellous @BalsellsRodas ๐
On the Identifiability of Regime-Switching Models with Multi-Lag Dependencies
Carles Balsells-Rodas, Toshiko Matsui, Pedro A. M. Mediano, Yixin Wang, Yingzhen Li
https://t.co/tngiUKXLzP [๐๐๐๐.๐ผ๐ป ๐๐.๐ป๐ถ]
Wanna understand the sources of uncertainty in LLMs when performing in-context learning ๐ค?
๐ We introduce a variational uncertainty decomposition framework for in-context learning without explicitly sampling from the latent parameter posterior.
๐ Paper: https://t.co/RQTqfF3Iiy
๐ป GitHub: https://t.co/0ZPmWMcbRc
#AISTATS2026 call for papers is out! We welcome solid Stats and AI/ML work from you ๐ค
(2026 conference will have further exciting initiatives, watch this space ๐
If you are attending ICML 2025 and interested in Causal Discovery, I will be presenting our work today at 11am! See you at the East Exibition Hall - Poster 1303. #icml#icml2025
Excited to share that our paper "Causal discovery from Conditionally Stationary Time Series" has been accepted to ICML 2025!๐ฅณ
Pre-print: https://t.co/tPlw7p5Ja4
Thank you very much to all my collaborators, persistence pays off!
#icml#icml2025
Excited to share that our paper "Causal discovery from Conditionally Stationary Time Series" has been accepted to ICML 2025!๐ฅณ
Pre-print: https://t.co/tPlw7p5Ja4
Thank you very much to all my collaborators, persistence pays off!
#icml#icml2025
Gonna present 3 papers at #ICLR2025 and #AABI, come and connect at๐
1. Oral session 1c on FIRST DAY Morning: Improving Diffusion Model with Optimal Diagonal Covariance Matching (https://t.co/YjcGro9GR1)
Is there an elementary way to understand matrix/tensor factorization, and their algebraic connection with transformer models?๐ค
Our answer is YES!
All you need is undergraduate-level linear algebra, and a little geometry intuition! https://t.co/BSbxJSef2l.
#geometry#tensor
1/7
๐ขSubmit by Nov 2 to CLeaR, if you are researching on Causal Discovery, Causal Inference, Intersections of Causality and Machine Learning, and many others! Check out our Call for Papers https://t.co/eyzQQsBopt