[CMU AI Seminar] February 7 at 12pm (GHC 6115 & Zoom) -- Saurabh Garg (CMU) -- Domain Adaptation under Relaxed Label Shift -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Reminder this is happening soon in GHC 6115.

On Sat, Feb 4, 2023 at 5:42 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you *this Tuesday (2/7)* from *1**2:00-1:00 PM
> (U.S. Eastern time)* for the next talk of this semester's *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/>.
>
> This Tuesday (2/7), *Saurabh Garg* (CMU MLD) will be giving a talk titled
> *"**Domain Adaptation under Relaxed Label Shift**".*
>
> *Title*: Domain Adaptation under Relaxed Label Shift
>
> *Talk Abstract*: Despite the emergence of principled methods for domain
> adaptation under label shift, the sensitivity of these methods for minor
> shifts in the class conditional distributions remains precariously under
> explored. Meanwhile, popular deep domain adaptation heuristics tend to
> falter when faced with shifts in label proportions. While several papers
> attempt to adapt these heuristics to accommodate shifts in label
> proportions, inconsistencies in evaluation criteria, datasets, and
> baselines, make it hard to assess the state of the art. In this paper, we
> introduce RLSbench, a large-scale \emph{relaxed label shift} benchmark,
> consisting of $>$500 distribution shift pairs that draw on 14 datasets
> across vision, tabular, and language modalities and compose them with
> varying label proportions. First, we evaluate 13 popular domain adaptation
> methods, demonstrating more widespread failures under label proportion
> shifts than were previously known. Next, we develop an effective two-step
> meta-algorithm that is compatible with most deep domain adaptation
> heuristics: (i) *pseudo-balance* the data at each epoch; and (ii) adjust
> the final classifier with (an estimate of) target label distribution. The
> meta-algorithm improves existing domain adaptation heuristics often by
> 2--10\% accuracy points under extreme label proportion shifts and has
> little (i.e., $<$0.5\%) effect when label proportions do not shift. We hope
> that these findings and the availability of RLSbench will encourage
> researchers to rigorously evaluate proposed methods in relaxed label shift
> settings.
>
> *Speaker Bio:* Saurabh Garg is a fourth-year Ph.D. student in the Machine
> Learning Department at Carnegie Mellon University, advised by Zachary
> Lipton and Sivaraman Balakrishnan. Saurabh is interested in building robust
> and deployable machine learning systems. The primary focus of his research
> is to improve and evaluate deep learning models in the face of distribution
> shifts.  Before Saurabh started his Ph.D., he received his bachelor’s
> degree from the Indian Institute of Technology (IIT) Bombay, majoring in
> Computer Science and Engineering.
>
> *In person: *GHC 6115
> *Zoom Link*:
> https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
>
> Thanks,
> Asher Trockman
>
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