54 research outputs found
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
We consider the problem of learning the exact skeleton of general discrete
Bayesian networks from potentially corrupted data. Building on distributionally
robust optimization and a regression approach, we propose to optimize the most
adverse risk over a family of distributions within bounded Wasserstein distance
or KL divergence to the empirical distribution. The worst-case risk accounts
for the effect of outliers. The proposed approach applies for general
categorical random variables without assuming faithfulness, an ordinal
relationship or a specific form of conditional distribution. We present
efficient algorithms and show the proposed methods are closely related to the
standard regularized regression approach. Under mild assumptions, we derive
non-asymptotic guarantees for successful structure learning with logarithmic
sample complexities for bounded-degree graphs. Numerical study on synthetic and
real datasets validates the effectiveness of our method. Code is available at
https://github.com/DanielLeee/drslbn.Comment: NeurIPS 2O23 Spotlight. More empirical results adde
When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans
In order to collaborate safely and efficiently, robots need to anticipate how
their human partners will behave. Some of today's robots model humans as if
they were also robots, and assume users are always optimal. Other robots
account for human limitations, and relax this assumption so that the human is
noisily rational. Both of these models make sense when the human receives
deterministic rewards: i.e., gaining either 130 with certainty. But in
real world scenarios, rewards are rarely deterministic. Instead, we must make
choices subject to risk and uncertainty--and in these settings, humans exhibit
a cognitive bias towards suboptimal behavior. For example, when deciding
between gaining 130 only 80% of the time, people tend
to make the risk-averse choice--even though it leads to a lower expected gain!
In this paper, we adopt a well-known Risk-Aware human model from behavioral
economics called Cumulative Prospect Theory and enable robots to leverage this
model during human-robot interaction (HRI). In our user studies, we offer
supporting evidence that the Risk-Aware model more accurately predicts
suboptimal human behavior. We find that this increased modeling accuracy
results in safer and more efficient human-robot collaboration. Overall, we
extend existing rational human models so that collaborative robots can
anticipate and plan around suboptimal human behavior during HRI.Comment: ACM/IEEE International Conference on Human-Robot Interactio
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