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-rw-r--r--posts/2019-03-14-great-but-manageable-expectations.md8
1 files changed, 4 insertions, 4 deletions
diff --git a/posts/2019-03-14-great-but-manageable-expectations.md b/posts/2019-03-14-great-but-manageable-expectations.md
index 889f674..ff2beee 100644
--- a/posts/2019-03-14-great-but-manageable-expectations.md
+++ b/posts/2019-03-14-great-but-manageable-expectations.md
@@ -588,8 +588,8 @@ DP-SGD with the Gaussian mechanism:
2. Example 1 (RDP for the Gaussian mechanism) -\> Claim 22 (Moment
Composition Theorem) -\> Example 3 (Moment composition applied to
the Gaussian mechanism)
-3. Conjecture 0 (RDP for Gaussian mechanism with specific magnitudes
- for subsampling rate) -\> Conjecture 3 (Moment Composition Theorem
+3. Claim 26 (RDP for Gaussian mechanism with specific magnitudes
+ for subsampling rate) -\> Claim 28 (Moment Composition Theorem
and translation to conventional DP)
Which one is the best?
@@ -727,7 +727,7 @@ Here is a list of what I think may be interesting topics or potential
problems to look at, with no guarantee that they are all awesome
untouched research problems:
-1. Prove Conjecture 2
+1. Prove Conjecture 1
2. Find a theoretically definitive answer whether the methods in Part 1
or Part 2 yield better privacy guarantees.
3. Study the non-Gaussian cases, general or specific. Let $p$ be some
@@ -735,7 +735,7 @@ untouched research problems:
$L(p(y) || p(y + \alpha))$ for $|\alpha| \le 1$? Can you find
anything better than Gaussian? For a start, perhaps the nice tables
of Rényi divergence in Gil-Alajaji-Linder 2013 may be useful?
-4. Find out how useful Conjecture 0 is. Perhaps start with computing
+4. Find out how useful Claim 26 is. Perhaps start with computing
the constant $C$ nemerically.
5. Help with [the aforementioned
issue](https://github.com/tensorflow/privacy/issues/23) in the