Ayush Bharti

Text: We're hiring! Postdoctoral fellows in artificial intelligence and machine learning research. Below: ELLIS Institute Finland logo

#Hiring: postdocs in artificial intelligence and machine learning research. Deadline February 9, 2026.
- Cutting-edge computational resources like @lumi-supercomputer.eu
- Close collaboration with @ellis.eu
- Ambitious, high-impact projects across Finland
➡️ www.ellisinstitute.fi/postdoc-recr...

1/ Very happy with this spotlight paper at
@neuripsconf.bsky.social where we continue our "Amortize Everything" agenda.

After the Amortized Conditioning Engine (Chang et al., AISTATS, 2025) which amortizes all sorts of inference tasks, here with ALINE we amortize both inference & design.

We jointly amortize Bayesian inference and active data acquisition within a single architecture.

Excited to share our #NeurIPS2025 ✨Spotlight✨ paper “ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition”!

www.huangdaolang.com/aline/

The recording of my talk on 'Multilevel neural simulation-based inference' at the 'One World Approximate Bayesian Inference' seminar series is now available on YouTube.

Link: www.youtube.com/watch?v=hBWd...

The abstract submission deadline for the ELLIS Workshop on Amortized Probabilistic ML has been extended to October 25th!

You still have time to submit a short abstract on your work in: Amortized Inference, Neural processes, Prior-fitted networks, Sequential Decision-Making, and Foundation models.

Thinking about a #PhD in AI + #machinelearning?

🇫🇮 Read about @ayushbharti.bsky.social's experience doing research in Finland:
www.ellisinstitute.fi/collaboratio...

Then,
👀 Apply for the @ellis.eu PhD program by October 31 🎃 : ellis.eu/news/ellis-p...

Doctoral Researcher (simulation-based inference) / Väitöskirjatutkija (simulaatio-pohjainen päättely)

I'm looking for a Doctoral Researcher (PhD student) to work with me on simulation-based inference at Data Science Research Centre, Tampere University Check the link for details and send an application before October 10th.

tuni.rekrytointi.com/paikat/?o=A_...

Just finished delivering a course on 'Robust and scalable simulation-based inference (SBI)' at Greek Stochastics. This covered an introduction to SBI, open challenges, and some recent contributions from my own group.

The slides are now available here: fxbriol.github.io/pdfs/slides-....

Great initiative!

Thank you so much! Glad you liked it.

Doing so saves hours of computation time for the radio propagation model without any degradation in performance. (5/5)

Sampling from the cost-aware proposal is done via rejection sampling, and self-normalised importance weights are used to target the SBI posterior. (4/5)

We propose to sample from a cost-aware proposal to encourage sampling from the cheaper parameterisations of the model. (3/5)

Oftentimes, this computational cost varies with the parameter value, as is the case with this model from wireless communications field where the cost increases linearly. (2/5)

Thread below:

Popular SBI methods such as Approximate Bayesian computation (ABC), neural posterior estimation (NPE) and neural likelihood estimation (NLE) require running the simulator thousands of times, which can be a computational bottleneck. (1/5)

Poster

Cost-aware simulation-based inference Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computation...

"Cost-aware simulation-based inference" is accepted at AISTATS 2025.

Check out our poster #205 on Sunday May 4th in Hall A-E if you are in Phuket. Finland's rising star @huangdaolang.bsky.social will be there to assist you :D

arxiv.org/abs/2410.07930

@fxbriol.bsky.social @samikaski.bsky.social

Looking forward to this!

Multi-Head Latent Attention vs Group Query Attention: We break down why MLA is a more expressive memory compression technique AND why naive implementations can backfire. Check it out!

Looking forward to speaking at the @approxbayesseminar.bsky.social!

Our next talk will be on Thursday the 27th February at 11am UK time. Our next speaker is Ayush Bharti (Aalto University), who will talk about "Cost-aware simulation-based inference". To receive the link, sign up here: listserv.csv.warwick...

Congrats 🎉

If you are interested in doing a #PhD with me at Imperial College London and qualify as a home student, please reach out (before end of 2024)! Potential topics: spatial statistics, applied deep generative models, probabilistic programming and more.

Congratulations Matias!

Optimizing decision utility in Bayesian experimental design is key to improving downstream decision-making.

Excited to share our #NeurIPS2024 paper on Amortized Decision-Aware Bayesian Experimental Design: arxiv.org/abs/2411.02064

@lacerbi.bsky.social @samikaski.bsky.social

Details below.

@huangdaolang.bsky.social just joined here and you should follow him if you are interested in probabilistic machine learning, (Bayesian) exp. design and AI-assisted decision making. Not to mention that he has *several* NeurIPS papers already while in his 3rd PhD year...

Looks very interesting. This goes to the top of my reading list 😀

Hi, I'd like to join if that's ok.

✋

Sure. Will do.