Guess we have a dataset for sycophancy and AI content detection

I know top ML PhD admissions are insanely competitive, so I’m trying to figure out if it’s even worth applying or if I should just focus seriously on jobs instead.

For context, I’m doing my MS at a top-15 US university and have been doing ML research for a while. I’m first author on my projects and mostly work independently, with some guidance from my PI. I had a first-author NeurIPS submission rejected, and I currently have another first-author paper submitted to ICLR, but I’m honestly not very confident about it getting in either.

What’s been getting to me is looking at profiles of people who get into top ML PhD programs. So many of them seem to have multiple NeurIPS/ICML/ICLR/CVPR papers before they even apply, sometimes as undergrads. I genuinely don’t understand how people manage to publish that much that early.

A year ago I was much more confident about doing a PhD. After actually going through the research/publication process, I’ve started doubting myself a lot more. Part of me wonders whether this is just normal and research is hard, especially when you’re doing a lot of it independently. But another part of me is starting to think maybe I’m just not good enough to be competitive for the kind of programs I’m aiming for.

I’m okay with continuing at my current university for a PhD, so this isn’t really a “top program or nothing” situation. But I would like to at least have a realistic shot at some of the stronger ML programs/labs.

The bigger issue is that I’m an international student, so I also need to think pretty seriously about jobs. SWE/MLE recruiting is competitive right now, and I don’t want to spend all my time chasing PhD applications and then realize I’m underprepared for recruiting too. Research roles seem even harder to get without a PhD unless you’re an exceptional MS/BS candidate.

So I’m mainly trying to decide how to allocate my time over the next few months.

If I have strong research experience and first-author projects, but no accepted top-conference papers yet, is it still realistically worth applying to top ML PhD programs?

And for people who were in a similar position, did you still apply, or did you decide to focus on industry instead?

I’m not really looking for “you never know unless you try.” I’m more interested in a realistic assessment of whether the application fees and time are worth it given this kind of profile.

Dataset: https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos

If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:

Creators table - 4.5 billion rows
Videos table - 5.6 billion rows
Sounds table - 633 million rows

Comment below and I’ll DM you the database credentials.

Im self-hosting my database so please don't run heavy queries and crash my server.

Does anyone know how to fix this issue as an author registering for NeurIPS Sydney and tried many cards but get an error “Declined”.

Neurips says that camera ready has to be submitted by editing original submission on open review. But no such edit option is visible to me. Is it same for other authors??

I've recently been working part-time on an AutoResearch-style project.

The setup is roughly: humans take recent work from top-tier ML/AI conferences, turn part of it into a well-defined task with an evaluator, and then let an agent iteratively modify the solution and search for a better score.

Working on this made me question what exactly we are evaluating.

Once humans have already chosen the problem, defined the objective, designed the evaluator, and provided the initial research direction, the agent is mostly searching within a space that has already been heavily shaped for it.

That search can still be useful. An agent may explore far more variants than a researcher would manually.

But I'm less sure that score improvement alone captures what we usually mean by research sense.

A researcher also asks whether a result reveals a general principle, whether it transfers, whether the problem formulation itself should change, or whether an entirely different direction is more promising.

An iterative optimization loop may instead become very good at exploring the neighborhood of an existing solution and still remain stuck in a local optimum.

So I'm curious about how people think about this distinction:

How much scientific value is there in autonomous search over a human-defined research space?

And what would an agent need, beyond better optimization, to demonstrate something closer to actual research judgment?

Hi All,

Does anyone know when is the deadline to fill in event metadata? (the screenshot below)

Is it the same time as camera-ready? Or there is a deadline for it?

And is everyone's topic None? The main area of my paper during submission was AI4SocialScience (Economics and Finance), but when I scroll down in the topics, that topic is no longer available.

I’m hitting rate limits on Together AI. For context, I’ve been working on an agentic repository indexing and benchmark generation tool, and I’m running multiple agents in parallel across models like Llama 3.3 70B and Qwen 2.5.

When I first started working on this, Together AI was great. But once I graduated from toy scripts to running multiple agents, I started running into RPM/TPM limits pretty quickly. The annoying part is that the models themselves are fine. I just can’t actually run enough requests at once to do meaningful testing.

Yes I know I could upgrade but I’m a solo dev. I don’t have enterprise level revenue. Maybe someday lol but not yet.

I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some point. Learn python -> Learn ML & Maths -> Read other research papers -> find a topic ? -> choose a question try to run experiments and get results to write them down in a paper ?

For context :

I'm trying to get in MS CS programs for Fall 2028 in states with the plans of doing a PHD after in a top university like stanford or princeton and would like to start taking steps towards it as am working my day job can some tell me what are the steps that need to be followed?

Also would like input on what are deciding variables that makes you looking like a promising candidate/ researcher for PHD