24 citations · 109 across the 67 of their papers we have counts for
11 papers · 1 filter
SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning
Albert Zhao, Tong He, Yitao Liang +3
We describe a policy learning approach to map visual inputs to driving controls conditioned on turning command that leverages side tasks on semantics and object affordances via a l…
On Tractable Computation of Expected Predictions
Pasha Khosravi, YooJung Choi, Yitao Liang +2
Computing expected predictions of discriminative models is a fundamental task in machine learning that appears in many interesting applications such as fairness, handling missing v…
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing
Zhe Zeng, Fanqi Yan, Paolo Morettin +2
Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world hybrid scenarios where varia…
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
YooJung Choi, Golnoosh Farnadi, Behrouz Babaki +1
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods of…
Smoothing Structured Decomposable Circuits
Andy Shih, Guy Van den Broeck, Paul Beame +1
We study the task of smoothing a circuit, i.e., ensuring that all children of a plus-gate mention the same variables. Circuits serve as the building blocks of state-of-the-art infe…
On Constrained Open-World Probabilistic Databases
Tal Friedman, Guy Van den Broeck
Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a mo…