Papers
4,025 papers found
Learning Pareto-Efficient Decisions with Confidence
Sofia Ek, Dave Zachariah, Peter Stoica
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
Nghia Hoang, Anoop Deoras, Tong Zhao et al.
Learning Proposals for Practical Energy-Based Regression
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Youngsuk Park, Danielle Maddix, François-Xavier Aubet et al.
Learning Revenue-Maximizing Auctions With Differentiable Matching
Michael J. Curry, Uro Lyi, Tom Goldstein et al.
Learning Sparse Fixed-Structure Gaussian Bayesian Networks
Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala et al.
Learning Tensor Representations for Meta-Learning
Samuel Deng, Yilin Guo, Daniel Hsu et al.
Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback
Anirban Santara, Gaurav Aggarwal, Shuai Li et al.
Leveraging Time Irreversibility with Order-Contrastive Pre-training
Monica N. Agrawal, Hunter Lang, Michael Offin et al.
Lifted Division for Lifted Hugin Belief Propagation
Moritz P. Hoffmann, Tanya Braun, Ralf Möller
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
Kiran K. Thekumparampil, Niao He, Sewoong Oh
LIMESegment: Meaningful, Realistic Time Series Explanations
Torty Sivill, Peter Flach
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
LocoProp: Enhancing BackProp via Local Loss Optimization
Ehsan Amid, Rohan Anil, Manfred Warmuth
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
Benjamin Letham, Phillip Guan, Chase Tymms et al.
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape
Devansh Bisla, Jing Wang, Anna Choromanska
Maillard Sampling: Boltzmann Exploration Done Optimally
Jie Bian, Kwang-Sung Jun
Many processors, little time: MCMC for partitions via optimal transport couplings
Tin D. Nguyen, Brian L. Trippe, Tamara Broderick
Marginalising over Stationary Kernels with Bayesian Quadrature
Saad Hamid, Sebastian Schulze, Michael A. Osborne et al.
Marginalized Operators for Off-policy Reinforcement Learning
Yunhao Tang, Mark Rowland, Remi Munos et al.
Margin-distancing for safe model explanation
Tom Yan, Chicheng Zhang
Masked Training of Neural Networks with Partial Gradients
Amirkeivan Mohtashami, Martin Jaggi, Sebastian Stich
Mean Nyström Embeddings for Adaptive Compressive Learning
Antoine Chatalic, Luigi Carratino, Ernesto De Vito et al.