Prologue a few weeks ago, i listened to Sebastian Seung’s mini-lecture at Flatiron Institute (CCM) about the recently completed fruit fly brain connectome. near the end of the mini-lecture, sebastian talked about the necessity of graph node clustering based on the type-level connectivity patterns instead of node-level connectivity patterns. i thought that would be obviously easy to solve with latent variable modeling and ChatGPT. i was so wrong, because ChatGPT misled me into every possible wrong corner of the solution space over the next two weeks or so. eventually, i implemented a simple variational inference approach to latent variable clustering,
Author: kyunghyuncho
i sensed anxiety and frustration at NeurIPS’24
last week at NeurIPS’24, one extremely salient thing was the anxiety and frustration felt and expressed by late-year PhD students and postdocs who were confused by the job market that looks and feels so much different from what they expected perhaps when they were applying for PhD programs five or so years ago. and, some of these PhD students and postdocs are my own under my supervision. this makes me reflect upon what is going on or what has been going on in artificial intelligence research and development. this post will be more of less a stream of thoughts rather
<The Atomic Human> by Neil Lawrence
i can’t recall exactly but it was sometime in 2013 when Neil Lawrence visited Aalto University (it was january, apparently!). he gave a talk in a pretty small lecture room which was completely packed (and i was there as well.) he talked about his years-long effort in introducing probabilistic interpretation (and thereby extensions) to (hierarchical) unsupervised learning, which was back then being consumed by deep learning based approaches. that’s when i first learned clearly the intuition and motivation behind so-called GP-LVM (Gaussian process latent variable models). that was beautiful, or to be precise, how neil delivered his inspiration, motivation and
An outrageous idea: a society-level forever clinical trial
when i got tenure earlier, i thought that would change how i work and live. it was true, but it wasn’t because of tenure but because of my thyroid cancer (see https://kyunghyuncho.me/sharing-some-good-news-and-some-bad-news/ if you’re curious.) when i was promoted to become a full professor, i thought that would change how i work and live, but to be frank, it didn’t. though, i started to think about what i should be able to think about, now that i have become a full professor with tenure, implying (at least in my mind) that i have an obligation not only to carry on
Continued musing on DPO
This post continues from the earlier post on fixing DPO (https://kyunghyuncho.me/a-proper-preference-optimization-loss-and-its-gradient/). by the way, the dinner reservation was at Ramro (https://www.ramronyc.com/, https://maps.app.goo.gl/jwpyPvy2pjNsxS6h9), and i recommend you try it out. a very interesting cuisine! Direct Preference Optimization let’s start by stating the direct preference optimization (DPO) loss for each example $(x,y_+, y_-)$: \[\log \left( 1 + \exp \left(-\left(\beta \log \frac{\pi(y_+)}{\pi(y_-)}-\gamma \log \frac{\pi_0(y_+)}{\pi_0(y_-)}\right) \right) \right).\] this takes a slightly different form from the original DPO loss. in the original DPO loss, $\gamma = \beta$ was forced, which leaves the scale (or entropy) of the reference model $\pi_0$ uncontrollable. this formulation above is