| Language Models are Few-Shot Learners |
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| Neural Methods for Point-wise Dependency Estimation |
Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Russ R. Salakhutdinov |
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| Fast and Flexible Temporal Point Processes with Triangular Maps |
Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann |
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| Generalised Bayesian Filtering via Sequential Monte Carlo |
Ayman Boustati, Omer Deniz Akyildiz, Theodoros Damoulas, Adam Johansen |
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| Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time |
Kai Han, zongmai Cao, Shuang Cui, Benwei Wu |
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| Fourier Sparse Leverage Scores and Approximate Kernel Learning |
Tamas Erdelyi, Cameron Musco, Christopher Musco |
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| Bayesian Deep Ensembles via the Neural Tangent Kernel |
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh |
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| Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts |
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann |
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| A Bayesian Nonparametrics View into Deep Representations |
Michał Jamroż, Marcin Kurdziel, Mateusz Opala |
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| Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization |
Ben Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy |
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| Ultra-Low Precision 4-bit Training of Deep Neural Networks |
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| Bayesian Robust Optimization for Imitation Learning |
Daniel Brown, Scott Niekum, Marek Petrik |
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| Bayesian Multi-type Mean Field Multi-agent Imitation Learning |
Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong |
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| Continual Learning with Node-Importance based Adaptive Group Sparse Regularization |
Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon |
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| DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks |
Dennis Wei, Tian Gao, Yue Yu |
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| Searching for Low-Bit Weights in Quantized Neural Networks |
Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing XU, Chao Xu, Dacheng Tao, Chang Xu |
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| Sequential Bayesian Experimental Design with Variable Cost Structure |
Sue Zheng, David Hayden, Jason Pacheco, John W. Fisher III |
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| On the Role of Sparsity and DAG Constraints for Learning Linear DAGs |
Ignavier Ng, AmirEmad Ghassami, Kun Zhang |
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| Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations |
Sebastian Farquhar, Lewis Smith, Yarin Gal |
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| Fast Fourier Convolution |
Lu Chi, Borui Jiang, Yadong Mu |
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| Learning Structured Distributions From Untrusted Batches: Faster and Simpler |
Sitan Chen, Jerry Li, Ankur Moitra |
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| Bayesian Deep Learning and a Probabilistic Perspective of Generalization |
Andrew G. Wilson, Pavel Izmailov |
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| Efficient Low Rank Gaussian Variational Inference for Neural Networks |
Marcin Tomczak, Siddharth Swaroop, Richard Turner |
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| Probabilistic Orientation Estimation with Matrix Fisher Distributions |
David Mohlin, Josephine Sullivan, Gérald Bianchi |
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| Sparse Graphical Memory for Robust Planning |
Scott Emmons, Ajay Jain, Misha Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak |
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| Learning under Model Misspecification: Applications to Variational and Ensemble methods |
Andres Masegosa |
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| Structured Convolutions for Efficient Neural Network Design |
Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin, Fatih Porikli |
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| Bayesian Bits: Unifying Quantization and Pruning |
Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling |
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| Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective |
Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael Osborne, Frank Wood |
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| Bayesian Probabilistic Numerical Integration with Tree-Based Models |
Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, Francois-Xavier Briol |
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| Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee |
Jincheng Bai, Qifan Song and Guang Cheng |
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| Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks |
Junsouk Choi, Robert Chapkin, Yang Ni |
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