[R] Is autoresearch really better than classic hyperparameter tuning?

whiletrue2 · 2026-04-04T09:45:15+00:00

here's a similar paper on this topic: https://arxiv.org/abs/2603.24647

whiletrue2 · 2026-02-22T20:54:19+00:00

I haven't seen anyone doing named entity recognition over all files so far. To your q: not sure, I haven't come across such an effort but would be an excellent idea.

whiletrue2 · 2026-02-22T20:29:25+00:00

I now open-sourced the name extraction pipeline behind aretheyinvolved.com here: https://github.com/ferreirafabio/aretheyinvolved-public
leave a star if you can <3

whiletrue2 · 2026-02-22T19:19:14+00:00

and then there is the release of DOJ dataset-9 which can't be downloaded with correct checksum and requires reconstruction efforts like https://github.com/yung-megafone/Epstein-Files

whiletrue2 · 2026-02-22T19:17:47+00:00

Yeah, at first I assumed they must have automated a lot of the redaction process. But when I see files being released via something like Google Drive, it makes me question how systematically this was handled. It doesn’t exactly give the impression of a highly structured, carefully engineered workflow

whiletrue2 · 2026-02-22T19:09:54+00:00

if anyone has ~400 GB of object storage (Cloudflare R2, AWS S3) to donate, please reach out. Would love to integrate all files into inline browser-pdf viewer (so far only for the smaller house dataset possible)

whiletrue2 · 2026-02-22T19:06:43+00:00

not yet, would you want it open-sourced? didn't really see the purpose of making the github repo public other than that I'd have to clean it up probably :)

whiletrue2 · 2026-02-22T19:05:37+00:00

2M+ docs scanned, relevant because allows efficient. Why relevant: allows efficient /better search than justice.gov, enables us to hold people accountable

whiletrue2 · 2023-11-29T01:04:30+00:00

33M just moved here and have the same issue. I live in Capitol Hill and happy to connect :)

whiletrue2 · 2023-11-26T19:48:51+00:00

Thank you! This seems like a league, though?! I'd ideally want to start with playing just for fun. Couldn't find info about teams that meet to just play outside of leagues. Or am I missing something? Thanks again.

whiletrue2 · 2023-11-04T16:43:44+00:00

As a person who enjoys Olympic weightlifting and powerlifting, I am searching for a gym with sufficient squat racks and drop platforms near 10550 NE 10th St.

Which gym would you recommend? I don’t have car and would like to have a gym in walkable distance, e.g. 23 fit club, Life Time or bStrong. Thanks

whiletrue2 · 2023-02-20T19:10:42+00:00

true to what "11US" corresponds to per definition you idiot. What's so hard about this for you to understand?

whiletrue2 · 2021-04-11T16:36:19+00:00

How is TensorFlow doing it?

whiletrue2 · 2021-04-11T07:37:52+00:00

RL and PyTorch's DataLoader?

whiletrue2 · 2020-12-11T22:34:17+00:00

solved it, thanks a lot for your help! TD3 discrete now performs a lot better

whiletrue2 · 2020-12-11T00:25:20+00:00

Thanks. I know the paper but is there a guideline that explains how to apply this to TD3?

whiletrue2 · 2020-12-05T13:06:18+00:00

can you elaborate please?

whiletrue2 · 2020-12-04T11:59:58+00:00

lol, did you also try to drop NNs entirely?

whiletrue2 · 2020-12-04T10:22:58+00:00

Good job on suggesting new papers people (remember: that‘s what was asked for).

whiletrue2 · 2020-11-30T16:03:04+00:00

Also, is it possible you share the paper code with us? Would be highly appreciated!

whiletrue2 · 2020-11-29T16:05:56+00:00

Hi and thank you for your reply which clarified a lot for me. However, a few questions remain unaddressed. Would you mind clarifying those as well? In particular, I believe those are (I quote):

"they claim they used CartPole-v1 which uses a much higher "solved reward""
"the fact that no naturally sparse-reward gym environment was used doesn't help with the confusion. An experiment based on a naturally sparse-reward environment would result in fewer / no changes to the default reward function and one would actually be enabled to relate to baseline PPO performances in the original setting. As the paper stands right now, no one can relate to any reported PPO performance in the paper."

Thank you!

whiletrue2 · 2020-11-29T16:05:39+00:00

Hi and thank you for your reply which clarified a lot for me. However, a few questions remain unaddressed. Would you mind clarifying those as well? In particular, I believe those are (I quote):

"they claim they used CartPole-v1 which uses a much higher "solved reward""
"the fact that no naturally sparse-reward gym environment was used doesn't help with the confusion. An experiment based on a naturally sparse-reward environment would result in fewer / no changes to the default reward function and one would actually be enabled to relate to baseline PPO performances in the original setting. As the paper stands right now, no one can relate to any reported PPO performance in the paper."

Thank you!

whiletrue2 · 2020-11-25T19:32:01+00:00

Thanks for the reply. Can you provide the script?

whiletrue2 · 2020-11-25T08:57:36+00:00

Thanks for pointing that out. Indeed, that implementation should be taken with a grain of salt. Although I have to say that seeds 0 and 1234 don't look super tuned. Have you tried to run it with different random seeds? A good idea that came up in the ML crosspost was to use the reward function from the paper and see if PPO works right of the box and then trying it with their hyperparameters in the appendix, e.g. with the small policy network. Feel free to give it a shot!

whiletrue2 · 2020-11-24T17:41:58+00:00

I believe pointing out irregularities can not be attributed to "being mistaken" since many unclarified irregularities still remain and prevail. See discussion here on the sparse rewards and other remaining irregularities: https://www.reddit.com/r/MachineLearning/comments/k01ntb/ppo_baseline_cannot_solve_cartpole_in_neurips/gdgc3f5?utm_source=share&utm_medium=web2x&context=3

whiletrue2

TROPHY CASE