ACMI Lab (CMU)

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ACMI Lab (CMU)

ACMI Lab (CMU)

@acmi_lab

The Approximately Correct Machine Intelligence (ACMI) Lab at @mldcmu at @SCSatCMU. Growing the ML sandbox to address more of the real world. PI @zacharylipton

Pittsburgh, PA Katılım Şubat 2020
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
RecSys often assumes static rewards but preferences evolve! Consider “satiation”: 🍕for meal 1: 😄, 🍕for meal 2: 🤔, 🍕for meal 3: 😭… [no🍕] … 🍕for meal 100: 😄. In “Rebounding Bandits” we model dynamic rewards w linear dynamical systems arxiv.org/abs/2011.06741 #neurips2021
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
New paper "Domain Adaptation under Open Set Label Shift" by @acmi_lab PhD student Saurabh Garg w coadvisors @zacharylipton & Siva Balakrishnan. Lays out theoretical foundations & practical algorithm, for one scenario where open set adaptation can work. arxiv.org/abs/2207.13048
Zachary Lipton@zacharylipton

Excited to share new @acmi_lab paper introducing the first(?) theoretically coherent setting for open set classification. Under the label shift assumption, we can now handle both label shift (among prev seen classes) & arrival of a never-before-seen class arxiv.org/abs/2207.13048

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Simran Kaur
Simran Kaur@kaur_simran25·
Is flatness indicative of generalization? Not necessarily. Our experimental study calls the relationship between flatness (as measured by the max Hessian eigenvalue) and generalization into question. arxiv.org/abs/2206.10654
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
New work by @acmi_lab PhD student @dkaushik96 tackles the thorny issue of when to designate ML crowdworkers as human subjects. Our analysis reveals nuances, ambiguities, a loophole, & practical guidance authors: DK, @zacharylipton & @AlexJohnLondon paper: arxiv.org/abs/2206.04039
Divyansh Kaushik@dkaushik96

Preprint alert 🚨 With ML’s growing reliance on crowdsourcing, in this paper, @zacharylipton, @AlexJohnLondon, and I seek to resolve the human subject status of ML’s crowdworkers. More in the thread🧵 1/15 arxiv.org/abs/2206.04039

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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
Congrats to our nephew-turned-son Riccardo Fogliato on a great thesis proposal. Riccardo's tackles deep questions re (i) the performance and fairness properties of criminal risk assessment instruments; and (ii) {human+model} hybrid decision-making systems. acmilab.org/people/riccard…
Carnegie Mellon Statistics & Data Science@CMU_StatDS

Congratulations to @CMU_Stats Riccardo Fogliato on his successful PhD thesis proposal on “Data and Humans in Algorithmic Risk Assessment”!! Co-advised by Alexandra Chouldechova @HeinzCollege and Zachary Lipton @zacharylipton @mldcmu @teppercmu

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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
New work by @saurabh_garg67 (ICLR 2022) shows that in general, OOD accuracy is identified only when the optimal predictor is identified. Thus, any guarantee requires assumptions on nature of shift. Also discovers a simple method that works surprisingly well on many benchmarks.
Saurabh Garg@saurabh_garg67

"Can we predict OOD performance given access to unlabeled target data?" We investigate methods to predict target domain performance and find a simple method that does surprisingly well. Paper: arxiv.org/abs/2201.04234 with Siva B, @zacharylipton, @bneyshabur, @HanieSedghi 1/

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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
Congrats to our 2nd ever PhD, the soon-to-be-minted Doctor Danish, who defended his dissertation this week. Danish joined this lab before it was a lab and helped build it from the ground up. We're proud of all you've accomplished and excited to see your future unfold. 👨‍🎓📜💻
Danish Pruthi@danish037

Excited to end the year on a high: I passed my PhD defense today! *Absolutely* loved my PhD years @LTIatCMU—I could have spent another 3 years! Major thanks to @zacharylipton, @gneubig, @professorwcohen for being wonderful advisors. [1/n]

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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
@postmachines Eventually, they do. But potential is not the same thing as mastery. Scientific values and practice are propagated through an apprenticeship model. They come to learn the craft.
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Arip
Arip@machinestein·
@acmi_lab Why should such people join your lab? Such people create their own labs
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
Research experience is great, published papers can be impressive, but (generally) they are neither necessary nor sufficient for joining our lab. Some of our criteria:
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
@kuchhal_dhruv @mynkgoel Great Q & there's too much to cover in a tweet (e.g. there's getting past departmental admit processes vs our lab's process). Some things that help: written statements (we really read them), interviews (both PI & students), record of projects coming alive (even outside research)
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Dhruv Kuchhal
Dhruv Kuchhal@kuchhal_dhruv·
@mynkgoel @acmi_lab I'd typed out the same tweet and then found it's already been asked :) I think whenever someone talks about admissions, it is mostly about abstract qualities, and rarely about how their evidence/application is interpreted in terms of concrete signals - which would be more useful.
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
Pubs on a CV signal many things. It's not easy to bang out papers pre-PhD. But merely knowing that a researcher has been published or even that they have reliably have contributed to projects that met the bar for conference peer review carries little signal re the above.
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
4. Fire—will this person bring some attitude to the lab? Can they forcefully disagree when appropriate? Will they spot flaws in a research direction? can can they cut against consensus? Will they spark creative directions, and do they have the drive to push them into reality? 🔥
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
In addition to modeling satiation, our key technical innovation—modeling rewards as dynamical systems—may have broader applications & (given a different parameterization) be used to model other phenomena, such as brand loyalty & binging, & may prove useful beyond RecSys. (6/n)
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ACMI Lab (CMU)
ACMI Lab (CMU)@acmi_lab·
RecSys often assumes static rewards but preferences evolve! Consider “satiation”: 🍕for meal 1: 😄, 🍕for meal 2: 🤔, 🍕for meal 3: 😭… [no🍕] … 🍕for meal 100: 😄. In “Rebounding Bandits” we model dynamic rewards w linear dynamical systems arxiv.org/abs/2011.06741 #neurips2021
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