From awd at cs.cmu.edu  Mon Feb  3 10:05:39 2025
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Mon, 3 Feb 2025 10:05:39 -0500
Subject: Fwd: [ML-news] Call for Papers: Machine Learning for Healthcare |
 August 15-16 2025 | Mayo Clinic Rochester, MN
In-Reply-To: <CAP9-Rb4bHWoLduX3YRJu5tYyUQcY5in9KbmXcQZUqRhGuQWakQ@mail.gmail.com>
References: <CAP9-Rb4bHWoLduX3YRJu5tYyUQcY5in9KbmXcQZUqRhGuQWakQ@mail.gmail.com>
Message-ID: <CAJvAoyuQRQ6EMj1atHkWnHrmdyRc1k8Ar9p3213snYzoUE9Tuw@mail.gmail.com>

A very good venue for all Healthcare AI work, and one of those Auton Lab
helped to grow.

Artur

---------- Forwarded message ---------
From: Madalina Fiterau <ina.fiterau at gmail.com>
Date: Mon, Feb 3, 2025 at 8:59?AM
Subject: [ML-news] Call for Papers: Machine Learning for Healthcare |
August 15-16 2025 | Mayo Clinic Rochester, MN
To: <ml-news at googlegroups.com>


The Machine Learning for Healthcare Conference (MLHC) is a leading venue on
the intersection between machine learning breakthroughs, the growth of
digital health technologies, and the involvement of tech-savvy clinicians
to pave the way for advancing machine learning for healthcare. Since its
inception, MLHC has brought together thousands of researchers in machine
learning and clinical fields to share pioneering work (archived in the
Proceedings of Machine Learning Research) and foster new partnerships.

MLHC invites submissions to a full, archival Research Track and a
non-archival Clinical Abstracts Track:

   -

   The research track is organized with three main research themes: (i)
   Novel methods, (ii) Experimental design, validation studies or pilot
   evaluations of machine learning solutions integrated into clinical practice
   or workflow, and (iii) Benchmark and reproducibility studies.
   Submissions in this track should be between 10-15 pages (excluding
   references and appendix), and those accepted will be published through the
   Proceedings of Machine Learning Research.
   -

   The Clinical Abstracts track welcomes submissions (of up to 2 pages) of
   work primarily led by clinicians, i.e., the first or senior author and
   presenter of a clinical abstract track submission must be a clinician.
   These abstracts will not be archived or indexed, but will have the
   opportunity to be presented as a poster and/or spotlight talk at MLHC.
   Clinical abstracts typically pitch clinical problems ripe for machine
   learning advances or describe translational achievements. This year we are
   piloting a program to provide the top 25% of the accepted Clinical
   Abstracts an optional opportunity to submit an enhanced version of the work
   to the Mayo Clinic Proceedings (MCP) Digital Health. The MLHC 2025
   submission and reviewing process is the same for all Clinical Abstracts,
   and this opportunity will be offered, after the fact, to the top 25% of the
   accepted Clinical Abstract submissions.
   -

   The Research Track and Clinical Abstracts Track submissions shall be
   made via OpenReview:
   https://openreview.net/group?id=mlforhc.org/MLHC/2025/Conference

Deadlines:

   -

   OpenReview account creation deadline: March 20th, 2025
   -

   Pre-submission intent deadline: April 4th, 2025*
   -

   Full submission deadline: April 11th, 2025
   -

   Review period: April 11th ? May 14th, 2025
   -

   Author Rebuttal period: May 26th ? June 9th, 2025
   -

   Reviewer - AC discussion: June 9th ? June 23th, 2025
   -

   Paper decision notifications: July 3rd, 2025
   -

   Conference dates: August 15-16th, 2025


*All authors submitting to either track require an active OpenReview
profile. Authors without an active OpenReview profile cannot be added to
the submission.

Full details for each track including paper suitability, required LaTeX
templates, reviewing and attendance requirements, desk reject policy, and
dual submission policy are available here:

https://www.mlforhc.org/paper-submission

We note that one author per paper must be available to review.



We look forward to receiving your submissions!



Sent on behalf of the MLHC 2025 Organizers
<https://www.mlforhc.org/organizers>

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.
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From pbartosi at andrew.cmu.edu  Tue Feb  4 15:12:29 2025
From: pbartosi at andrew.cmu.edu (Piotr Bartosiewicz)
Date: Tue, 4 Feb 2025 15:12:29 -0500
Subject: Main file server maintenance
Message-ID: <CAN_-bZJffqqVRqj2c4E_-n0xr1WGf3FZs3Lfsq0PFWuYFuLYoA@mail.gmail.com>

Hi,
  our main file server will be offline for maintenance on Saturday 8/2/2025.
During this downtime you won't be able to log in to autonlab nodes so,
please, save all your work no later than Friday night.

Piotr.
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From pbartosi at andrew.cmu.edu  Sat Feb  8 13:56:19 2025
From: pbartosi at andrew.cmu.edu (Piotr Bartosiewicz)
Date: Sat, 8 Feb 2025 13:56:19 -0500
Subject: Main file server maintenance
In-Reply-To: <CAN_-bZJffqqVRqj2c4E_-n0xr1WGf3FZs3Lfsq0PFWuYFuLYoA@mail.gmail.com>
References: <CAN_-bZJffqqVRqj2c4E_-n0xr1WGf3FZs3Lfsq0PFWuYFuLYoA@mail.gmail.com>
Message-ID: <CAN_-bZJrqjVpEi+eU_GEgA1nNBXpE1W0xC=1G9edp=5kAHj2ug@mail.gmail.com>

Hi,
  the server is back online and everything is working as expected.
If there are any problems with computing nodes, please let me know.

Piotr.


On Tue, Feb 4, 2025 at 3:12?PM Piotr Bartosiewicz <pbartosi at andrew.cmu.edu>
wrote:

> Hi,
>   our main file server will be offline for maintenance on Saturday
> 8/2/2025.
> During this downtime you won't be able to log in to autonlab nodes so,
> please, save all your work no later than Friday night.
>
> Piotr.
>
>
>
>
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From awd at cs.cmu.edu  Tue Feb 18 11:40:25 2025
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Tue, 18 Feb 2025 11:40:25 -0500
Subject: Fwd: Feb 18 at 12pm (GHC 6115) -- Keegan Harris (CMU) -- Should You
 Use Your Large Language Model to Explore or Exploit?
In-Reply-To: <CAA7JjMx8m7xMFvNOCoTd5=2ONWhWSi7W-PHE8uiWy1ua5yw+nw@mail.gmail.com>
References: <CAA7JjMx6kwu44ddw1qKg38V03ikG+VcuxHP=tqz-KJhK8FHC4w@mail.gmail.com>
 <CAA7JjMx8m7xMFvNOCoTd5=2ONWhWSi7W-PHE8uiWy1ua5yw+nw@mail.gmail.com>
Message-ID: <CAJvAoyt-f5C9aZFuXhxZb1bkS=imJb4EbfiMYra8mBgH-bpyaw@mail.gmail.com>

potentially of interest to many of us

---------- Forwarded message ---------
From: Victor Akinwande <vakinwan at andrew.cmu.edu>
Date: Tue, Feb 18, 2025 at 11:37?AM
Subject: Re: Feb 18 at 12pm (GHC 6115) -- Keegan Harris (CMU) -- Should You
Use Your Large Language Model to Explore or Exploit?
To: <ai-seminar-announce at cs.cmu.edu>, <ml-students at cs.cmu.edu>, <
ml-faculty at cs.cmu.edu>, <cs-postdoc at cs.cmu.edu>, <lti-students at cs.cmu.edu>
Cc: Eungyeup Kim <eungyeuk at andrew.cmu.edu>


Reminder: Keegan's talk is happening in about 30 minutes.

On Thu, Feb 13, 2025 at 3:42?PM Victor Akinwande <vakinwan at andrew.cmu.edu>
wrote:

> Dear all,
>
> We look forward to seeing you next *Tuesday (02/18) from 12:00-1:00 PM
> (ET)* for the next talk of CMU AI Seminar, sponsored by SambaNova Systems
> <https://sambanova.ai/>. The seminar will be held in *GHC 6115* with
> pizza provided and will be streamed on Zoom.
>
> To learn more about the seminar series or to see the future schedule,
> please visit the seminar website (http://www.cs.cmu.edu/~aiseminar/).
>
> Next Tuesday (02/18) Keegan Harris (CMU) will be giving a talk titled:
> "Should You Use Your Large Language Model to Explore or Exploit?".
>
>
> *Abstract*
> In-context (supervised) learning is the ability of an LLM to perform new
> prediction tasks by conditioning on examples provided in the prompt,
> without any updates to internal model parameters. Although supervised
> learning is an important capability, many applications demand the use of ML
> models for downstream decision making. Thus, in-context reinforcement
> learning (ICRL) is a natural next frontier. In this talk, we investigate
> the extent to which contemporary LLMs can solve ICRL tasks. We begin by
> deploying LLMs as agents in simple multi-armed bandit environments,
> specifying the environment description and interaction history entirely
> in-context. We experiment with several frontier models and find that they
> do not engage in robust decision making behavior without substantial
> task-specific mitigations. Motivated by this observation, we then use LLMs
> to explore and exploit in silos in various (contextual) bandit tasks. We
> find that while the current generation of LLMs often struggle to exploit,
> in-context mitigations may be used to improve performance on small-scale
> tasks. On the other hand, we find that LLMs do help at exploring large
> action spaces with inherent semantics, by suggesting suitable candidates to
> explore. This talk is based on joint work with Alex Slivkins, Akshay
> Krishnamurthy, Dylan Foster, and Cyril Zhang.
>
>
> *Speaker bio: *
> Keegan Harris is a final-year Machine Learning PhD candidate at CMU,
> where he is advised by Nina Balcan and Steven Wu, and does research on
> machine learning for decision making. He has been recognized as a Rising
> Star in Data Science and his research is supported by an NDSEG Fellowship.
> He is also the head editor of the ML at CMU blog. Previously, Keegan spent
> two summers as an intern at Microsoft Research and graduated from Penn
> State with BS degrees in Computer Science and Physics.
>
>
> *In person: GHC 6115*
> Zoom Link:* https://cmu.zoom.us/j/93599036899?pwd=oV45EL19Bp3I0PCRoM8afhKuQK7HHN.1
> <https://cmu.zoom.us/j/93599036899?pwd=oV45EL19Bp3I0PCRoM8afhKuQK7HHN.1>*
>
>
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