Rao, Vinayak, Ryan P. Adams, and David D. Dunson. “Bayesian inference for Matérn repulsive processes.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79.3 (2017): 877-897. link

Wibisono, Andre, Ashia C. Wilson, and Michael I. Jordan. “A Variational Perspective on Accelerated Methods in Optimization.” arXiv preprint arXiv:1603.04245 (2016). link

Mohamed, Shakir, and Balaji Lakshminarayanan. “Learning in Implicit Generative Models.” arXiv preprint arXiv:1610.03483 (2016). link

Arora, Sanjeev, et al. “Generalization and Equilibrium in Generative Adversarial Nets (GANs).” arXiv preprint arXiv:1703.00573 (2017). link

Gulrajani, Ishaan, et al. “Improved Training of Wasserstein GANs.” arXiv preprint arXiv:1704.00028 (2017). link

Makhzani, Alireza, et al. “Adversarial autoencoders.” arXiv preprint arXiv:1511.05644 (2015). link

Burda, Yuri, Roger Grosse, and Ruslan Salakhutdinov. “Importance weighted autoencoders.” arXiv preprint arXiv:1509.00519 (2015). link

van den Oord, Aaron, Nal Kalchbrenner, and Koray Kavukcuoglu. “Pixel Recurrent Neural Networks.” Proceedings of The 33rd International Conference on Machine Learning. 2016. link

Dinh, Laurent, Jascha Sohl-Dickstein, and Samy Bengio. “Density estimation using Real NVP.” To appear at The 5th International Conference on Learning Representations. (2017). link

Bojarski, et al. “Structured adaptive and random spinners for fast machine learning computations.” To appear in AISTATS (2017). link

Wang, et. al. “Bayesian Optimization in a Billion Dimensions via Random Embeddings. link

Y. Ma, T. Chen, and E.B. Fox, “A Complete Recipe for Stochastic Gradient MCMC,” Neural Information Processing Systems (NIPS) (2015). link

Lu, Xiaoyu, et al. “Relativistic Monte Carlo.” To appear in AISTATS (2017). link

Tamar, Aviv, Sergey Levine, and Pieter Abbeel. “Value Iteration Networks.” NIPS (2016). link

Finn, Chelsea, Ian Goodfellow, and Sergey Levine. “Unsupervised Learning for Physical Interaction through Video Prediction.” NIPS (2016). link

Arjovsky, Martin, Soumith Chintala, and Léon Bottou. “Wasserstein GAN.” arXiv preprint arXiv:1701.07875 (2017). link

Mescheder, Lars, Sebastian Nowozin, and Andreas Geiger. “Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks.” arXiv preprint arXiv:1701.04722 (2017). link

Kakade, Sham, et al. “Prediction with a Short Memory.” arXiv preprint arXiv:1612.02526 (2016). link

Rezende, Danilo Jimenez, et al. “One-Shot Generalization in Deep Generative Models.” arXiv preprint arXiv:1603.05106 (2016). link

Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. “Human-level concept learning through probabilistic program induction.” Science 350.6266 (2015): 1332-1338. link

Khan, Mohammad E., et al. “Kullback-Leibler proximal variational inference.” Advances in Neural Information Processing Systems. 2015. link

Khan, Mohammad E., et al. “Faster stochastic variational inference using Proximal-Gradient methods with general divergence functions.” arXiv preprint arXiv:1511.00146 (2015). link

Jang, Eric et al. “Categorical Reparameterization with Gumbel-softmax.” (2016) link

Maddison, Chris J et al. “The concrete distribution: a continuous relaxation of discrete random variables.” (2016) link

Kusner, Matt J. and José Miguel Hernández-Lobato. “GANs for Sequences of Discrete Elements with the Gumbel-softmax Distribution.” (2016) link

Also of note: Tokui, Seiya. “Reparameterization trick for discrete variables.” arXiv preprint arXiv:1611.01239 (2016). link

A review of NIPS papers. Please check the NIPS pre-proceedings and come prepared to discuss a few of your favorites!

Johnson, Matthew J., et al. “Composing graphical models with neural networks for structured representations and fast inference.” Advances in Neural Information Processing Systems (2016). link

Gao, Yuanjun, et al. “Linear dynamical neural population models through nonlinear embeddings.” Advances in Neural Information Processing Systems (2016). link

(optional) Krishnan, Rahul G., Uri Shalit, and David Sontag. “Deep Kalman Filters.”arXiv preprint arXiv:1511.05121 (2015). link

Uria, Benigno, et al. “Neural Autoregressive Distribution Estimation.” arXiv preprint arXiv:1605.02226 (2016). link

Germain, Mathieu, et al. “MADE: masked autoencoder for distribution estimation.” International Conference on Machine Learning. 2015. link

Huang, Gao, et al. “Deep networks with stochastic depth.” arXiv preprint arXiv:1603.09382 (2016). link

Gal, Yarin, and Zoubin Ghahramani. “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning.” arXiv preprint arXiv:1506.02142 (2015). link

Advani, Madhu, and Surya Ganguli. “Statistical Mechanics of Optimal Convex Inference in High Dimensions.” Physical Review X 6.3_ (2016): 031034. link

Kawaguchi, Kenji. “Deep Learning without Poor Local Minima.” To appear in Neural Information Processing Systems (NIPS) (2016). link

Mei, Song, Yu Bai, and Andrea Montanari. “The Landscape of Empirical Risk for Non-convex Losses.” arXiv preprint arXiv:1607.06534 (2016). link

Polloc, Murray et al. “The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data” arXiv preprint arXiv:1609.03436 (2016). link

Gregor, Karol, et al. “DRAW: A recurrent neural network for image generation.” arXiv preprint arXiv:1502.04623 (2015). link

Xu, Kelvin, et al. “Show, attend and tell: Neural image caption generation with visual attention.” arXiv preprint arXiv:1502.03044 2.3 (2015). link

(optional) Eslami, S. M., et al. “Attend, Infer, Repeat: Fast Scene Understanding with Generative Models.” arXiv preprint arXiv:1603.08575 (2016). link

(optional) Mnih, Volodymyr, Nicolas Heess, and Alex Graves. “Recurrent models of visual attention.” Advances in Neural Information Processing Systems. 2014. link

Goodfellow, Ian, et al. “Generative adversarial nets.” Neural Information Processing Systems (NIPS) (2014). link

Nowozin, Sebastian, Botond Cseke, and Ryota Tomioka. “f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization.” Neural Information Processing Systems (NIPS) (2016). link

Moreno, Alexander, et al. “Automatic Variational ABC.” arXiv preprint arXiv:1606.08549 (2016). link

Meeds, Edward, Robert Leenders, and Max Welling. “Hamiltonian ABC.” arXiv preprint arXiv:1503.01916 (2015). link

(optional) Meeds, Edward, and Max Welling. “GPS-ABC: Gaussian process surrogate approximate Bayesian computation.” arXiv preprint arXiv:1401.2838 (2014). link

Rezende, Danilo Jimenez, and Shakir Mohamed. “Variational inference with normalizing flows.” arXiv preprint arXiv:1505.05770 (2015). link

Kingma, Diederik P., Tim Salimans, and Max Welling. “Improving Variational Inference with Inverse Autoregressive Flow.” arXiv preprint arXiv:1606.04934 (2016). link