Please Note: This seminar will be given online.
|
Department seminar
Jeffrey
Negrea Link to join seminar: Hosted on Zoom |
Adapting to failure of the IID assumption
Assumptions
on
data
are
used
to
develop
statistical
methods
with
optimistic
performance
guarantees.
Even
if
these
assumptions
don’t
hold,
we
often
believe
that
if
our
models
are
“nearly
correct”,
then
our
methods
will
have
performance
similar
to
those
optimistic
guarantees.
How
can
we
use
models
that
we
know
to
be
wrong,
but
expect
to
be
nearly
correct,
in
a
way
that
is
robust
and
reliable?
In
order
to
provide
robustness
to
the
failure
of
our
models,
we
must
quantify
the
degree
to
which
our
simplifying
models
fail
to
explain
observed
data,
and
develop
statistical
methods
that
adapt
to
the
degree
of
this
failure.
In
this
seminar,
I
will
discuss
my
work
on
the
canonical
problem
of
statistical
aggregation,
i.e.,
combining
predictions
from
a
large
number
of
models
or
experts.
We
define
a
continuous
spectrum
of
relaxations
of
the
IID
assumption
for
prediction
problems
with
sequential
data,
with
IID
data
at
one
extreme
and
mechanisms
that
select
worst-case
responses
to
one's
actions
at
the
other.
We
develop
methods
for
statistical
aggregation
with
sequential
data
that
adapt
to
the
level
of
failure
of
the
IID
assumption.
We
quantify
the
difficulty
of
statistical
aggregation
in
all
scenarios
along
the
spectrum
we
introduce,
demonstrate
that
the
prevailing
methods
do
not
adapt
to
this
spectrum,
and
present
new
methods
that
are
adaptively
minimax
optimal.
More
broadly,
this
work
shows
that
it
is
possible
to
develop
methods
that
are
both
adaptive
and
robust:
they
realize
the
benefits
of
the
IID
assumption
when
it
holds,
without
ever
compromising
performance
when
the
IID
assumption
fails,
and
without
having
to
know
the
degree
to
which
the
IID
assumption
fails
in
advance.
This
seminar
is
based
on
the
following
two
research
papers:
1.
https://arxiv.org/abs/2007.06552
2.
https://proceedings.neurips.cc/paper/2021/hash/dcd2f3f312b6705fb06f4f9f1b55b55c-Abstract.html