Papers
Latent Derivative Bayesian Last Layer Networks
Joe Watson, Jihao Andreas Lin, Pascal Klink et al.
Latent Gaussian process with composite likelihoods and numerical quadrature
Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki
Latent variable modeling with random features
Gregory Gundersen, Michael Zhang, Barbara Engelhardt
Learning Bijective Feature Maps for Linear ICA
Alexander Camuto, Matthew Willetts, Chris Holmes et al.
Learning Complexity of Simulated Annealing
Avrim Blum, Chen Dan, Saeed Seddighin
Learning Contact Dynamics using Physically Structured Neural Networks
Andreas Hochlehnert, Alexander Terenin, Steindor Saemundsson et al.
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints
Robin Vogel, Aurélien Bellet, Stephan Clémençon
Learning GPLVM with arbitrary kernels using the unscented transformation
Daniel de Souza, Diego Mesquita, João Paulo Gomes et al.
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino et al.
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Chen-Yu Wei, Mehdi Jafarnia Jahromi, Haipeng Luo et al.
Learning Matching Representations for Individualized Organ Transplantation Allocation
Can Xu, Ahmed Alaa, Ioana Bica et al.
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haußmann, Sebastian Gerwinn, Andreas Look et al.
Learning Prediction Intervals for Regression: Generalization and Calibration
Haoxian Chen, Ziyi Huang, Henry Lam et al.
Learning Shared Subgraphs in Ising Model Pairs
Burak Varici, Saurabh Sihag, Ali Tajer
Learning Smooth and Fair Representations
Xavier Gitiaux, Huzefa Rangwala
Learning Temporal Point Processes with Intermittent Observations
Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya et al.
Learning the Truth From Only One Side of the Story
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
Learning to Defend by Learning to Attack
Haoming Jiang, Zhehui Chen, Yuyang Shi et al.
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Jiaqi Ma, Xinyang Yi, Weijing Tang et al.
Learning User Preferences in Non-Stationary Environments
Wasim Huleihel, Soumyabrata Pal, Ofer Shayevitz
Learning with Gradient Descent and Weakly Convex Losses
Dominic Richards, Mike Rabbat
Learning with Hyperspherical Uniformity
Weiyang Liu, Rongmei Lin, Zhen Liu et al.
Learning with risk-averse feedback under potentially heavy tails
Matthew Holland, El Mehdi Haress
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Vikas Garg, Adam Tauman Kalai, Katrina Ligett et al.
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads
Hossein Shokri Ghadikolaei, Sebastian Stich, Martin Jaggi