We have extended the #AABI workshop and proceedings deadlines! *New deadlines:* Workshop Track: February 14, AoE Proceedings Track: February 14, AoE approximateinference.org/call/ #ProbML #AABI #ICLR

Excited to see our chapter out! A concise and accessible introduction to Bayes Compute in deep neural networks and deep generative models. Great for statisticians curious about diving in!

I will attend #ICLR2025 this week and share our work on VLM safety, ETA, #530 (Hall 3 + Hall 2B) on April 24th from 10:00 AM to 12:30 PM. Feel free to chat and discussion! See you in Singapore🤩

Excited to present this at today’s Poster session! Quick update: poster number is 592. Whova event seems to be outdated, but ICLR website has correct info. Check out the project page if you want to read more! Link: patrickpynadath1.github.io/dab/ Time: 10am-12pm, poster number 592

The deadline for #IJCAI2025 Workshop on User-Aligned Assessment of Adaptive AI Systems is just 5 days away. If you are working on any aspect of assessment, regulation, compliance, etc., of AI systems, please check it out. More details here: bit.ly/ijcai25-aia

Excited to share our latest work on self-correcting reasoning in Vision-Language Models! - Improve reasoning with minimal annotated data - Lots of insights + strong results Kudos to Yi Ding for leading this amazing work!

Proud advisor moment: Pascal Jutras-Dubé gave a talk at MMLS to hundreds! Great work on making samplers work in just one step! Paper: arxiv.org/abs/2502.07579

Purdue IPAI is hiring postdocs in AI! If you're interested in statistical machine learning or trustworthy AI, and would like to work with me, please get in touch! Applications are due by Sept 1, 2025. purdue.edu/computes/insti…

Excited to be speaking at the #IJCAI2025 Workshop! Hope to see you there!

Sherlock is accepted to NeurIPS2025! See u in San Diego

We’re presenting three papers at #COLM2025! I’ll be here Oct 7–10. Please stop by our poster and DM me if you want to chat. I’ll also be at Mentorship Roundtables at WiML. See you there!

Excited to give a talk on Oct 14 about Gradient-Based Discrete Sampling! How can we bring the power of Langevin dynamics to discrete spaces? I’ll discuss algorithms like Discrete Langevin and its extensions for multimodal distributions and combinatorial optimization, with

Thanks for having me and for putting together such a great event! Looking forward to the next one!

Can we accelerate test-time alignment? YES! 📃paper: Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Aligner 🔗arXiv: arxiv.org/abs/2508.15044 📌EMNLP 2025

A hybrid diffusion can outperform pure discrete (masked) diffusion! We introduce CANDI: - Combine discrete structure with continuous joint updates - Achieves strong low-NFE generation - Enables simple classifier guidance How does it work? Continuous diffusion on text wasn’t