“X papers accepted at Y”
~ What people don’t tell about paper acceptances! ~ A thread #AcademicChatter#phdlife
1/N Many papers that you see accepted now would have been very likely a second or a third submission! That doesn’t mean they are not worthy instead they actually took a lot more work including feedback from multiple reviewers, revisions, skilled rebuttals & perseverance!
2/N Yes, many would have made it in first attempt! They are likely the result of dedicated collaborations with some including structured teams! Again not a bad thing, but don’t feel bad because you are comparing this to you toiling the night oil all by yourself+advisor!
3/N Sure there will be many with a dedicated author or two alone who would have made it in first attempt! That must take experience, expertise, time, hard work, excellence and confidence! Again don’t compare!
4/N Noise! There was a paper where our reviewers did not even acknowledge the rebuttal! Not sure what the AC was doing in this case! I ln the past, I have also seen 2 line reviews w/ very high confidence! Rebuttals & writing to AC might help! No guarantees! Not in your control!
5/N Caveat: This is definitely not an exhaustive list of all scenarios! These are just examples of how we end up comparing to the superficial binary 0/1 accept/non accept with partially observable data of others! Yes we all do it! Its humane!
6/N Good reviewers help a lot in improving papers! It takes time to see this i.e if you are lucky to get decent quality reviews! 1/3 papers of mine in 2020 was a 3rd time submission after revisions from 2 conferences! It only got better with age! Was frustrating regardless!
7/N If you see someone tweeting more than 3-4 papers that were accepted! Don’t panic! They are professors and not PhD students learning to do research ! So again don’t compare! See for example
8/N Luck! There is a lot of luck involved in this process I believe due to stochasticity! The topic you are working + the timing, the reviewers you get, the AC you get, their degree of commitment to champion your paper in discussions, the position of sun, moon and venus!
9/N Now a bit controversial issue! Despite double blind, papers uploaded to arxiv, blog posts written and publicized, PR machines much before peer reviews, etc. *might* play some role! I don’t know what’s the right thing to do! Its is indeed a complex problem!
10/N As I am learning myself so I am sure there are more things people don’t tell about paper acceptances that I still need to learn! Dont at me! I would love to hear from senior researchers who are AC, PC, SAC, etc. Happy to learn more or correct my understanding!
12/N Why I felt the urge to write this? Papers are bread and butter for academia apparently! Despite noise in both reviewing & citations (also huge biases here), they determine a lot in academic careers afaik! So the worry and comparison is understandable!
13/N Time and again, we need to remind ourselves that we are more than our papers! A wise genius told me quality matters more than quantity! I genuinely cannot thank my advisor Doina Precup enough for instilling this belief in me! It took some time + I am still working on it!
14/N I am no saint and I also have shared paper acceptances without going into details! More recently I do see many people talking about the story of a paper acceptance! Like a BTS (behind the scenes) of a movie which I absolutely love! Keep them coming!
Ok! Apparently I don’t understand sarcasm! Thanks for educating @nitarshan 😂 The example I gave from @roydanroy is a meme apparently, but well the point still holds for people tweeting about a large # of papers accepted to a single venue!
15/N Adding to this an insightful discussion I just came across from @hima_lakkaraju 👇🏽Very well said! Thank you!
Applying for grad school? Sharing here a few pointers on grad school applications! Pl feel free to share w/ folks who might benefit! Disclaimer: I am not an expert! I came across some questions from different people and I see a pattern, so here we go: #AcademicChatter 0/N
Where to apply? How to do this? Start with a list of schools 3 dream schools, 4 would love to go, 3 safe( will get in most likely)! Google sheets, to do lists, color codes, then marie kondo the hell out of this process! You got this! 1/N
Webpage! I cannot emphasize how nice it is to see a candidates webpage with all information consolidated at one place! I have some students tell me that they don’t have enough content to create a webpage! You need it even more! 2/N
Why is there zero *explicit* training on almost everything that matters a lot in academia except research? Be it reviewing, writing, communication, presentation skills, or even networking. They are left for each to hone & champion majorly by themselves! 1/N
The advisor and peers ofcourse help one develop such skills and get better at these over time! But it is often after many attempts people in later career stages say “I wish someone told me this earlier..” I don’t even believe the entire burden should be on the advisor alone! 2/N
For instance, we complain about reviews, but how much training is given to anyone for reviewing? Poor quality review comments like “sota chasing” “not novel enough” “not deep enough” and even the scale on the reviews is not very helpful for anyone! 3/N
Humans have a remarkable understanding of which states afford which behaviors. We provide a framework that enables RL agents to represent and reason about their environment through the lens of affordances bit.ly/31qjlSv
In this work, we develop a theory of affordances for agents who learn and plan in Markov Decision Processes. Affordances play a dual role. On one hand, they allow faster planning. On the other hand, they facilitate more efficient learning of transition models from data. 2/4
We establish these properties through theoretical results as well as illustrative examples. We also propose an approach to learn affordances from data and use it to estimate partial models that are simpler and generalize better. 3/4
Humans have a remarkable understanding of which states afford which behaviors. We provide a framework that enables RL agents to represent and reason about their environment through the lens of affordances bit.ly/31qjlSv
In this work, we develop a theory of affordances for agents who learn and plan in Markov Decision Processes. Affordances play a dual role. On one hand, they allow faster planning. On the other hand, they facilitate more efficient learning of transition models from data. 2/4
We establish these properties through theoretical results as well as illustrative examples. We also propose an approach to learn affordances from data and use it to estimate partial models that are simpler and generalize better. 3/4