Two (out of two) papers accepted at ICML:
- Extending Prediction-Powered Inference through Conformal Prediction
- Avoid What You Know: Divergent Trajectory Balance for GFlowNets
Links:
- https://t.co/2A3dwVkaG2 -- work done in collaboration with Pedro Dall'Antonia, Claudio Struchiner and Guilherme T. Goedert
- https://t.co/jAbU1yFXMK -- work done in collaboration with Pedro Dall'Antonia [first author], Tiago da Silva, Salem Lahlou and Diego Mesquita
@lreyzin We just need to start writing better papers. In the new equilibrium, nothing that that GPT can prove directly will be publication worthy. But there will be a larger set of things that we can prove with 6 months worth of effort using gpt.
Now accepted at AISTATS: Differentially Private E-Values!
TL;DR: we introduce mechanisms to convert any e-value into a differentially private version of itself, while preserving its statistical properties
Okay so, we just found that over 50 papers published at @Neurips 2025 have AI hallucinations
I don't think people realize how bad the slop is right now
It's not just that researchers from @GoogleDeepMind, @Meta, @MIT, @Cambridge_Uni are using AI - they allowed LLMs to generate hallucinations in their papers and didn't notice at all.
It's insane that these made it through peer reviewπ
automatic and silent rank promotion&broadcasting is the root of all evil and should never have been a thing.
just do `export JAX_NUMPY_RANK_PROMOTION=raise` and be free
@_sanjoydas I think it would be better to be more formal wrt what you mean by programs&compilers here. Due to the 'quine-like' structure of the program P, your definition of a program must be powerful enough to do that. Maybe try working atop some (perhaps augmented) lambda calculus?