Learning Graphical Models
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
Structure learning is the task of finding the dependency graph of a Markov random field (MRF) given i.i.d.
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Amanda Gentzel, Dan Garant, David Jensen
Anactivecommunity ofresearchers developsandenhances algorithms that learn causal models from data, and this work has produced a series of impressivetechnicaladvances.
Policy-Conditioned UncertaintySetsfor RobustMarkovDecisionProcesses
What policy should be employed in a Markov decision process with uncertain parameters?
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
Neural Information Processing Systems http://nips.cc/
Mitigating Source Bias for Fairer Weak Supervision
Theoretically, we show that it is possible for our approach to simultaneously improve both accuracy and fairness--in contrast to standard fairness approaches that suffer from tradeoffs. Empirically, we show that our technique improves accuracy on weak supervision baselines by as much as 32% while reducing demographic parity gap by 82.5%.
Tensor Monte Carlo: Particle Methods for the GPU era
Neural Information Processing Systems http://nips.cc/
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang, Guosheng Yin, Francesca Dominici
Neural Information Processing Systems http://nips.cc/
GroundedAnswersforMulti-agentDecision-making ProblemthroughGenerativeWorldModel
Theempirical results demonstrate that this framework can improve the answers for multi-agent decision-making problems by showing superior performance on the training and unseen tasks of the StarCraft Multi-Agent Challenge benchmark.