Data Access Models
Material mostly taken from
P.
Donmez
and J. G.
Carbonell
(2008) Proactive learning:
Cost

sensitive active learning with multiple imperfect
oracles.
In Proceedings of the 17th ACM Conference on
Information and Knowledge Management (CIKM
’08)
pp:619
–
628
Copyright © 2013 A.W. Naik. These lecture notes are provided under a Creative Commons
Attribution

NonCommercial

ShareAlike
3.0
Unported
license. See
http://creativecommons.org/licenses/by

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sa/3.0/
for complete terms of the license.
Recap
•
Already discussed: ability to request data
–
Streaming
vs
Membership query models
•
What other assumptions were we making?
–
Labels all cost the same (just minimize #labels)
–
We can always obtain a label
–
We’ll only get one label (only one oracle)
Proactive Learning generalizes Active Learning
Case: Reluctant
vs
Reliable Oracles
Imagine that we can request labels from two
kinds of oracles:
–
A
reluctant
oracle which does not always return
a label
–
A
reliable
oracle which always returns a label
Both oracles return the correct label
The reliable oracle costs more to query than
the reluctant oracle
Case: Reluctant
vs
Reliable Oracles
•
We have a fixed budget B
•
Oracle costs: reluctant (C
0
) reliable (C
1
)
•
Measure solutions by their “utility”
–
same units as cost
In principle we want to minimize the cost and maximize
the utility:
max
σ
𝔼
[
Utility[
σ
]S] + ∑
i
∈
σ
C(x
i
)
Such that ∑
i
∈
σ
C(x
i
) < B
Can’t directly maximize! Open problem: even if utility is
submodular
, does the greedy algorithm fare well here?
(Constrained adaptive
submodularity
)
Notice their method is
essentially a label
propagation method!
“One” hypothesis is acting
as a proxy to set of
decision boundaries.
Case: Accurate
vs
Inaccurate Oracles
•
Inaccurate costs less than Accurate Oracle
•
Basic intuition:
–
if inaccuracy ∝ nearness to
𝔼
[YX] boundary
–
then only pay for accuracy near
𝔼
[YX]
We relate this method to
Dasgupta
and Hsu’s
method in “Hierarchical
sampling for active
learning
.”
Larger clusters in
Dasgupta
and Hsu’s
method ≈ less reliable
oracles
Case: Cost Difference Oracles
•
Side knowledge: one oracle costs more for
“hard” cases (“
nearness to
𝔼
[YX]”)
•
Basic intuition:
–
if cost ∝ nearness to
𝔼
[YX] boundary
–
then only pay for accuracy near
𝔼
[YX]
Decision

centric Views
•
See: M. Saar

Tsechansky
and F. Provost (2007) Decision

centric active
learning of binary

outcome models. Information Systems Research,
18(1):1
–
19
•
Decision maker wants to estimate expected utility from performing an
action
•
x
i
is an example
–
“the description of a consumer”
•
f
i
(unknown) probability that the action with respect to x
i
will be
successful
–
“consumer x
i
will respond to the marketing campaign, or will renew her
contract”
•
f
i
th
is
a probability threshold for performing the action
–
if
f
i
exceeds
f
i
th
by “enough” then utility will be locally maximized
•
Solution concept:
–
Manage
𝔼
[YX] accuracy only where we can reliably estimate actions having
high utility
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