[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]

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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