Lijun
Ding
Title: Low rank matrix optimization
Abstract: This talk
consists of two parts:
(1) semidefinite programming with low rank solution; (2)
statistical low-rank matrix recovery.
In the first part, I will present a storage optimal and time
efficient algorithm, called CSSDP (complementary slackness SDP), in solving
weakly constrained semidefinite programs with low rank solutions.
I shall present the algorithm, the use of complementary
slackness in designing it, and a comparison of complexities with past
solvers.
In the second part, I will present an algorithm, called AVPG
(averaging projected gradient), for solving statistical rank constrained
problems. I shall present its main application in generalized linear model
with rank constraints, the advantage of it over existing algorithms, and
idea of the proof for its global linear convergence.
Angela
Zhou
Title: Robust Personalization from
Observational Data
Abstract: Learning
to make decisions from datasets in realistic environments is subject to
practical challenges such as unobserved confounders, missingness, and bias,
which may undermine the otherwise beneficial impacts of data-driven
decisions. In this talk, I introduce a methodological framework for learning
causal-effect maximizing personalized decision policies in the presence of
unobserved confounders. Recent work unilaterally assumes unconfoundedness: that
there are no unobserved confounders affecting treatment and outcome, which is often
untrue for widely available observational data. I develop a
methodological framework that accounts for possible unobserved confounding by
minimizing the worst-case estimated regret over an ambiguity set for propensity
weights. I prove generalization guarantees and a semi-synthetic case study
on personalizing hormone replacement therapy based on the parallel
WHI observational study and clinical trial. Hidden confounding can
lead to unwarranted harm, while the novel robust approach guarantees
safety and focuses on well-evidenced improvement.
In the second part of this talk, I highlight follow-up work
on leveraging these ideas for developing robust bounds for off-policy policy
evaluation in batch (offline) reinforcement learning in the infinite-horizon setting.