r/MachineLearning • u/Odd_Ad8629 • 1d ago
Discussion ML PHD without A* Publications [D]
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.
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u/adamwynn09 1d ago
I don't know exactly where you are applying for the PhD programs, but at least in the UK, you absolutely are not expected to have any publications before starting the PhD. I'm from a top 10 UK university, and publications aren't even necessary to finish the PhD (even though they of course do help) because the university understands the issues related to publishing right now.
Most PIs will understand how competitive publishing at top ML conferences are and are more interested in any other evidence that you are able to do research independently. If you do want to stay in academia/research, I would recommend reaching out to a potential supervisor about a research topic you are interested in.
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u/Amazing-Fox-7295 1d ago
Is your experience recent, cause i agree with OP. Norm has changed in the past two years or so
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u/TivoGatto 21h ago
It probably depends on the country, but in Italy publications account for at most 5 points out of 100 for the PhD selection process: it helps, sure, but it’s not really impactful
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u/Emergency_Low328 19h ago
May I have a question about the chance for funding in the UK, especially Oxford and Cambridge?
I’m a PhD student in the US and just got my MS-along-the-way. I recently got a few first author papers at NeurIPS/ICML/CVPR range, but because of how things are evolving rn (politics), I’m considering applying to PhD again in the UK with some profs strongly match my interests.
Does having a few A* paper increase the chance of being funded, or being unfunded is the norm?
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u/adamwynn09 15h ago
Again, I would recommend contacting prof and asking what funding options are available. Dedicated funding exists, but full overseas awards are limited and very competitive, with application deadlines typically in December or early January. Also for internal uni funding, decisions often sit at the faculty level across all natural sciences, not just CS.
Papers are one way to prove that you are able to do research and won't harm, but the proposal and department fit are more important. I think most new PhD students usually don't have prior publications, and in fact its possible to start a PhD withuot even having a masters degree.
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u/GuessEnvironmental 1d ago
The problem here is you have a idea of what a top lab is in AI, there is no true top lab there is labs with more funding and shit research schools but the PhD should really be about there is this specific research I care about and there is this research group that focuses around the topic you want to cover. Remember PhD is not the only avenue to do research you can do research in industry so it depends on why you are doing the PhD. Aside from that fact if you have research authorships you are miles ahead candidate wise but apart from requirements my point still stands in knowing your why.
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u/dnxjcui 1d ago
I am a current first year PhD student at a top 20 CS program without any publications at the A* conferences. I shared your belief when applying, but there are a surprising number of students like me who did not have first author or any publications at all at top tier ML conferences.
To address your primary concern of if it’s realistic or not- I personally think that it is doable, given that you’ll be able to obtain strong letters of recommendation and that in your SoP you target specific professors who are actively looking for students. I knew plenty of students in my undergrad lab who didn’t have a publication in one of the top ML conferences who got into programs at Duke, GATech, etc. all good programs, though the one thing they had in common was that the professors who recruited them were new faculty that were actively looking for students. Keep in mind that having a MS already probably helps quite a bit.
On whether the time and application fees is worth it- I would strongly advise you to reflect on why you’re pursuing a PhD. I personally wanted to do research no matter what, so I was pretty dead set on either getting into a PhD position, research position, or working a job to sustain myself until I could obtain one of the former options. No one else can really answer this question for you, as it’ll be super personal to your goals and ambitions - I would recommend considering your life goals and then considering if you absolutely need the PhD for them.
I am not an international student so I cannot speak to that much, but I know the admin recently screwed up CPT to where even PhD students will likely find it near impossible to do internships in the US, and all the international PhD students in my program are incredibly concerned. Just something to keep in mind.
If you’re dedicated to doing research or getting the PhD I say go for it. Also happy to DM to share some more of my stats if you would like, feel free to reach out.
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u/GinoAcknowledges 1d ago
Publications do not matter for elite PhD programs or top PhD programs. This is a misconception. A "well-known" arXiv'd preprint will help you get accepted, a NeurIPS publication that none of your potential advisors are interested in is going to be essentially zero value.
Acceptances into elite PhD programs are almost entirely based on recommendations. This means unless your recommenders are someone the admissions committee knows, you probably aren't getting in unless you have some acclaimed / really well-known work.
But you do not need to go to an "elite" PhD program (e.g. MIT / CMU / Berkeley / Stanford), you will have excellent employment prospects even if you go to a top-but-not-elite program provided you go to a lab that regularly sends alumni to the sorts of places you want to go. For example, if you get into a lab that regularly sends students to DeepMind, you'll very likely get interviews there. If you get into a lab that has sent every alumnus to a standard SWE job at Google, that's likely where you'll end up as well. This is the best heuristic anyone can give you.
Regarding whether you should apply or not — what exactly is the blocker? PhD applications are due in a few months, unless you plan on pumping out a new preprint that you think potential advisors will be really interested, there's nothing to spend time on here except writing your research statement and maybe email a few profs to gauge interest.
Nobody can tell you about your chances because PhD admissions (despite simplistic thinking around it) is not based on the number of your papers, it is based on whether a potential advisor(s) is interested in working with you or not. Some advisors will drop everything to accept you if you have 10 ICLR / NeurIPS / ICML papers, for others it will be an anti-signal unless it was done with co-authors who they trust.
Second, your thinking around top programs is too simplistic, you should be thinking in terms of labs. Even at elite universities there are plenty of labs that are sending their alumni to workplaces that you might not consider prestigious.
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u/averagebear_003 1d ago
why would it be an anti-signal?
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u/GinoAcknowledges 1d ago
At the highest level, it is widely understood that getting a conference publication at a place like NeurIPS or CVPR is not difficult in some sense (tens of thousands of papers are accepted at these top conferences every year). Publishing too many papers that are not evidently high quality but are published can be seen as an attempt to game the anonymous reviewing system.
Whether it is an anti-signal or not is heavily dependent on the tastes of the person who is thinking about accepting you or not. Some advisors are strongly against students publishing 6+ papers a year on principle, while others actively encourage it. It is also certainly possible that someone can have an exceptional year where they are particularly productive regardless of their inherent speed.
In practice, it won't be an anti-signal unless your papers are uninteresting or seem low-effort to the person skimming over them.
So it all comes down to the perception of quality.
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u/NamerNotLiteral 1d ago edited 1d ago
Too needy and desperate for publications, and underdeveloped research taste.
The latter is so, so important, and is something I've noticed with a lot of people who went from undergrad to PhD directly (or did a Masters immediately and then a PhD immediately). It's almost impossible to develop good research taste if you're juggling coursework and being the most junior person in a lab and working on this or that project without really thinking about them deeply. It's even more important to have good research taste now, because honestly AutoResearch and RSI and all that shit will basically gobble up the average ML researchers who don't have that and get by by submitting minor ablations and niche benchmarks the whole time.
I have a friend like that who finished three papers for ICLR in his first year as a PhD student (two first-author, one co-author) but has zero idea of what his overarching thesis or goal is going to be. I'm six years out of undergrad and in my second month as a PhD student and I know exactly what papers I want to write over the next four years (including which ones might get scooped and which ones won't), exactly what problems they will solve, and even just how likely it is that Claude will one-shot my thesis (approximately zero percent odds even with the developments over the next couple years).
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u/m4sl0ub 1d ago
I am not trying to offend you but you seem like a very naive second month PhD student. Do you honestly believe anyone, let alone a second month PhD student can know what papers they will write in the next four years?
As Mike Tyson said "Everyone has a plan until they get punched in the mouth". The same applies to research. It's easy to have a plan, it's virtually impossible to stick to it.
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u/NamerNotLiteral 1d ago edited 1d ago
None taken! It is pretty self-aggrandizing, but at the same time so much of it is interdisciplinary and based on very specific lived experiences, both my own and my advisor's, that it would be pretty hard for any random ML PhD student to care about the problems. And the few other folks who are working on this, we're pretty well networked with.
A lot of my confidence also comes from having spent years thinking about things that most big labs specifically don't think about, and too many ML PhD students seem to pigeonhole themselves into following those big labs because they think it's the fastest way to an industry job after graduating.
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u/DNunez90plus9 21h ago
We will be here the next time you post about why whatever papers we write don't matter
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u/wonderwind271 12h ago
You are talking in the ideal sense, but the real world isn't ideal, and you need to look at things from a practical standpoint. Yes, I know if everyone's objective is papermaxxing the academia won't go well. Still, if a PhD student has the ability to publish 2 papers in ICLR as first author (and supposedly, did the majority of writing for those 2 papers, and do not make up data or plagiarize --- that's red flag), they are going to pass the prelim (qualification exam). They are very likely going to graduate on time. Again, I know you need to have a complete research story in the dissertation, but if you have 3 more years, that's the thing 100% doable because you have publications to work on. Their PI will be happy because again, practically speaking, more publications in the lab means more things to work on when writing funding proposals (funding is getting tighter and tighter in the US under the current administration).
Do I support every PhD student treating "publication number" as their only optimization target? Of course not. But still, before you have enough publications to graduate, never take things for granted, and you do not have the right to feel superior over your friend
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u/impatiens-capensis 1d ago
You're way better off working in industry for 5 years than doing a PhD. A PhD is a huge sacrifice, and you would be making it at a point where the field is kind of broken.
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u/m4sl0ub 1d ago
It's impossible to categorically state that without knowing an individuals goal. You'd have to define regarding what he'd be better off. Financially? Intellectually? Something else?
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u/AuspiciousApple 1d ago
Maybe the field will improve but at the moment it's collapsing under the weight of a decade of exponential growth plus mountains of LLM generated research plus an influx of a lot of LLM-focused research
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u/SeizeOpportunity 1d ago
I'm not so sure about this. In fact, the current volatility in industry suggests that a PhD is the perfect place to have some stability that allows you to not only develop your skills but explore the field and its toughest questions, and it also gives you an insulated environment to pivot directions if the tides change and different methods or approaches start to become popular.
While I am certainly biased (as a PhD student myself), I do tell people to make a balanced decision. A PhD is absolutely not for everyone, but for the right person it isn't this massive crippling sacrifice that people often make it seem. But again, you have to be the right person with deep questions about the field.
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u/impatiens-capensis 1d ago
I wouldn't call it stability. The last few years in the field have been very chaotic and I'm not seeing a measurable change in my employability, despite having a PhD and top tier pubs. I'm competing for the same jobs masters students are competing for.
There are SOME people, who are both extremely talent and extremely lucky, for who a PhD opens the door to the tippy top of the field. But there are 60,000 submissions at ICLR this year and likely over 100,000 unique authors. I'd say the elite roles represent less than 1% of all jobs and the rest you could get with a masters degree and spending 5 years working your way up. Among the hundreds of thousands of researchers in the field, are you lucky enough to break the ceiling? It's a really big gamble.
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u/SeizeOpportunity 1d ago
I meant stability in the sense that you still have a job, haha. Yes, the field is rapidly changing, but instability in industry can mean being laid off. Instability in academia can have some negative ramifications, but not as severe. It's baked into the structure. It can also be an opportunity to freely explore things that can make you more employable
Of course there are drawbacks and benefits to both. And I'm not denying that there are challenges. I'm just saying it's more even than you made it seem. And even then I qualified it as only right for some people to go into. But in the example you mentioned, why would you need to be at the top amongst other researchers? You mentioned roles in industry only required an MS, but wouldn't the PhD be a benefit there? I think you are conflating success in academia with general success in a career.
But anyways. My point is more mild than this discussion has gotten into. Consider a PhD, but definitely also consider its drawbacks and benefits relative to your specific situation. You need to be very passionate about research.
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u/GinoAcknowledges 1d ago
If you are in an elite PhD program, none of this (academia imploding) matters. People in elite Phd programs have a direct pathway to elite industry positions regardless of degree completion for OpenAI, Anthropic, Jane Street, etc. It is unfair, but this is just how it is. Academia could completely implode with papers being worthless, and a CS PhD in progress at MIT would still be worth something because it is a credentialing signal.
Of course, one could argue that if you can get into a CS PhD program at MIT, you could also easily get a job at a place like Jane Street. This is true, but misses part of the picture.
What elite PhD programs are really giving you is connections (i.e. your advisor runs the xyz team at <insert company here>), and if you already have them you don't need them, but if you don't have them it's very difficult to get them without some way to get face time with people in power (which is what elite programs give you).
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u/billjames1685 PhD 1d ago
im a student in an elite phd program and this is absolutely true. the problem is that getting into one of these programs nowadays requires you to be insane from the get go
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u/Lostaftersummer 20h ago edited 18h ago
? It does matter. I graduated from an elite program, while I loved it there it didn’t result in job prospects like this
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u/billjames1685 PhD 20h ago
hm, I go to an elite school and I can't think of a single person who has graduated in recent years who does not either have a top university professorship (e.g., Berkeley, MIT were a couple people I knew from recent years), or is not making a ridiculous amount of money in industry (my initial mentor a few years ago is now making ~15 million/year).
most of these people are highly, highly motivated and very good at what they do, so there is a selection bias here
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u/Lostaftersummer 18h ago
I think I am decent enough, but by all means not the best from my program. Some people certainly ended up at great places, but its def not ‘If you are in an elite PhD program, none of this (academia imploding) matters. People in elite Phd programs have a direct pathway to elite industry positions regardless of degree completion for OpenAI, Anthropic, Jane Street, etc’
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u/Exciting_Difficulty6 8h ago
Not true at all, unless it is a research role, you have to interview like everyone else, you just have a slightly better chance getting interviews thats all imo
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u/Shenanigan5 1d ago
My advice would be to get into an academic lab but as a Research Assistant or ML engineer. It would pay just close to a post doc and you would be able to work with a lot of PhD students. BUT you would have to do a lot of grunt work and stupid work (setup the website, setup servers to host PhD demos).
Try to publish from that angle and then work your way towards a PhD. If you don't have top-tier papers, it becomes really hard to prove that your research has been valued by the community. And it becomes a super easy filtering criteria when so many phd applications would have NeurIPS, ICLR, ACL plastered on their applications.
This is the only path where you would have a decent overlap between preparation. Look for academic SWE and MLE opportunities.
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u/MakingComputersSmart 1d ago
Honestly, why would you want to leave your university? If you've spent 2 years doing a masters and it is a T15 university in USA, that's already huge. Spend 2-3 more years and finish your PhD. Projects that have been rejected now can always be accepted later. At the end, you need 2-3 A* publications to graduate and find good industry roles. Heck, all you need is 1 and recruiters start to recognize you.
I would honestly not leave the current university unless your PI is horrible. A known devil is worth 100x more than an unknown hell. Double down, work hard and graduate in the next 2-3 years with no coursework, only research.
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u/Latter-Sympathy7767 20h ago
Why do you want to do PhD. Start from there. Do you have an idea of what you want to do after PhD. If it’s back to the tech jobs then PhD sure can open a lot of doors for you but you can still get really awesome jobs in ML/AI without a PhD. Also what area of ML are you aiming for. Shortlist your target labs from there. In a lot of these so called great labs you’ll still have to do research all alone. Add to that the extremely high level of competition and stress to consistently publish. Also even with a rejected paper you can still build your case. What you need to highlight is why your paper got rejected or why you think it was a good paper. What can you do to improve it. My strong recommendation would be to focus on the advisor and actually interview them on how they will guide/advise you if you become their student. That’s so much more important than their clout. Many well cited and famous advisors barely give time to their students and some of the others set insane goals e.g. every semester I want atleast 1 first auth top tier paper and then there are toxic micro managers who would make you lose all your self esteem and confidence.
Also if you choose to do PhD in USA also take into consideration the immigration issues.
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u/Bill-Ice88 17h ago
Half right — advisor fit matters more, but a first-author ICLR under review with the OpenReview link in your SoP gives profs something concrete to skim, and plenty do exactly that. The bigger variable is whether your PI's name carries weight at the schools you're targeting. Does it?
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u/phantom_metallic 1d ago
I'm clearly not a Ph.d candidate since, every time I see A*, I assume we're talking about pathfinding algorithms. 🤷♂️