Papers
938 papers found
Heteroskedastic Geospatial Tracking with Distributed Camera Networks
Colin Samplawski, Shiwei Fang, Ziqi Wang et al.
How to use dropout correctly on residual networks with batch normalization
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang et al.
Human Control: Definitions and Algorithms
Ryan Carey, Tom Everitt
Human-in-the-Loop Mixup
Katherine M. Collins, Umang Bhatt, Weiyang Liu et al.
Improvable Gap Balancing for Multi-Task Learning
Yanqi Dai, Nanyi Fei, Zhiwu Lu
Incentivising Diffusion while Preserving Differential Privacy
Fengjuan. Jia, Mengxiao. Zhang, Jiamou. Liu et al.
Incentivizing honest performative predictions with proper scoring rules
Caspar Oesterheld, Johannes Treutlein, Emery Cooper et al.
Increasing effect sizes of pairwise conditional independence tests between random vectors
Tom Hochsprung, Jonas Wahl, Andreas Gerhardus et al.
Inference and sampling of point processes from diffusion excursions
Ali Hasan, Yu Chen, Yuting Ng et al.
Inference for mark-censored temporal point processes
Alex Boyd, Yuxin Chang, Stephan Mandt et al.
Inference for probabilistic dependency graphs
Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa
Inference of a rumor’s source in the independent cascade model
Petra Berenbrink, Max Hahn-Klimroth, Dominik Kaaser et al.
Information theoretic clustering via divergence maximization among clusters
Sahil Garg, Mina Dalirrooyfard, Anderson Schneider et al.
In- or out-of-distribution detection via dual divergence estimation
Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard et al.
Interpretable differencing of machine learning models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei et al.
Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals
Michael Jahn, Matthias Scheutz
Is the volume of a credal set a good measure for epistemic uncertainty?
Yusuf Sale, Michele Caprio, Eyke Hüllermeier
Jana: Jointly amortized neural approximation of complex Bayesian models
Stefan T. Radev, Marvin Schmitt, Valentin Pratz et al.
Keep-Alive Caching for the Hawkes process
Sushirdeep Narayana, Ian A. Kash
Knowledge Intensive Learning of Cutset Networks
Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan
KrADagrad: Kronecker approximation-domination gradient preconditioned stochastic optimization
Jonathan Mei, Alexander Moreno, Luke Walters
Learning Choice Functions with Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective
Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge et al.
Learning good interventions in causal graphs via covering
Ayush Sawarni, Rahul Madhavan, Gaurav Sinha et al.