Learning Graphical Models
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin, Francis Bach, Simon Lacoste-Julien
Exponential number of classes: this brings both additional computational and statistical challenges. By exponential, we mean exponentially large in the size of the natural dimension of output, e.g., the
Independence clustering (without a matrix)
Since mutual independence is the target, pairwise similarity measurements are of no use, and thus traditional clustering algorithms are inapplicable.
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, xuejun Liao, Lawrence Carin
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions.
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu
Neural Information Processing Systems http://nips.cc/
Flexible Models for Microclustering with Application to Entity Resolution
Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna Wallach, Abbas Zaidi, Beka Steorts
However, for some applications, this assumption is inappropriate.
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Alex M. Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
GibbsNet is the best of both worlds both in theory and in practice.
Predictive State Recurrent Neural Networks
Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li
Due to their probabilistic grounding, BFs and PSRs possess a strong statistical theory leading to efficient learning algorithms.
A multi-agent reinforcement learning model of common-pool resource appropriation
Humanity faces numerous problems of common-pool resource appropriation.
Experimental Design for Learning Causal Graphs with Latent Variables
Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
Causality shapes how we view, understand, and react to the world around us.
Deep Dynamic Poisson Factorization Model
A new model, named as deep dynamic poisson factorization model, is proposed in this paper for analyzing sequential count vectors.