@Yjg_oo There is some work out there on DP being used as a means to a statistical end (e.g. at the interface of privacy and robustness). In the above work, the end is "statistical inference for a population but with DP protections on the collected data".
At ICML at 11am Hawaiian time, I'll have a poster on differentially private confidence sets at board 801.
Come hang out if interested!
Paper: https://t.co/cnQYIG0owu
Very excited about this new paper by Tiger Zeng (https://t.co/fwyuSRaazD)
We study causal inference w/ high-dimensional discrete confounders
We give new bias/variance results & minimax lower bounds, which characterize fundamental limits of causal inference in high dimensions
For the Stats crowd: unlike P-values, E-values (https://t.co/nhHuBKQBvp) can be combined easily under arbitrary dependence. But can we say the same for their sequential analog, e-processes?
Sadly the answer is *no*, motivating our recent work: https://t.co/ChO8uwYzD4
(A thread)
@sp_monte_carlo A boring construction: X_1, X_1, X_1, ...
Perhaps you want to impose that (X_t)_t has infinite variation (otherwise desideratum 4 leads to constant martingales).
@Apoorva__Lal Yussef Dayes!
Here are some of my fav tracks. The first one takes a while to heat up but it's so worth the wait.
https://t.co/BaIOEcHFn0
https://t.co/p2R8NsaJra
https://t.co/Pa6anQSr2z
https://t.co/raUNXX4l3m
Join us in person or online for our Discussion Meeting next Tuesday, 23 May. Your comments are keenly invited on the paper ‘Estimating means of bounded random variables by betting'.
Register for the meeting at https://t.co/jOVmvRDxYi
Shantanu Gupta, Emre Yolcu and Minji Yoon of @SCSatCMU and Ian Waudby-Smith of @CMU_DietrichHSS have been named 2023 @amazon Graduate Research Fellows.
https://t.co/vfuBG0J0el
I am on the job market for faculty and research scientist positions.
I develop statistical theory and algorithms to make data-based decisions that balance the need for robustness in high-stakes settings with strong performance in practice.
Some highlights below:
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@lakens@GregoryFaletto@LajeunesseLab This is true for simple settings (such as point nulls/alternatives in parametric problems) but in practice one might care about composite nulls/alternatives under nonparametric assumptions. For those settings, a bit more machinery is needed.
My advisor @edwardhkennedy and I have posted an article on continuous trt effects estimation:
https://t.co/LGXVu6FOMg We study nonparam models where the dose-response curve has its own smoothness, which may differ from that of the outcome regression or cond dens of trt given X.
Super excited to share new work "Comparing methods for statistical inference with model uncertainty" with @AdrianRaftery1 that just got published in PNAS (@PNASNews) ! #Statistics#DataScience#BMA (1/n)
Paper: https://t.co/RjACkbXmLd