Papers
Multi-Agent Determinantal Q-Learning
Yaodong Yang, Ying Wen, Jun Wang et al.
Multi-Agent Routing Value Iteration Network
Quinlan Sykora, Mengye Ren, Raquel Urtasun
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Georgios Smyrnis, Petros Maragos
Multidimensional Shape Constraints
Maya Gupta, Erez Louidor, Oleksandr Mangylov et al.
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada et al.
Multigrid Neural Memory
Tri Huynh, Michael Maire, Matthew Walter
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Markos Georgopoulos, Grigorios Chrysos, Maja Pantic et al.
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong, Yingkai Li, Qin Zhang et al.
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki, Shion Takeno, Tomoyuki Tamura et al.
Multi-Objective Molecule Generation using Interpretable Substructures
Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
Aditya Rajagopal, Diederik Vink, Stylianos Venieris et al.
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Jung Yeon Park, Kenneth Carr, Stephan Zheng et al.
Multi-step Greedy Reinforcement Learning Algorithms
Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
Debabrata Mahapatra, Vaibhav Rajan
Mutual Transfer Learning for Massive Data
Ching-Wei Cheng, Xingye Qiao, Guang Cheng
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
Ilai Bistritz, Tavor Baharav, Amir Leshem et al.
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo, Shahin Boluki, Xinyu Gong et al.
Naive Exploration is Optimal for Online LQR
Max Simchowitz, Dylan Foster
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
David Woodruff, Amir Zandieh
Near-linear time Gaussian process optimization with adaptive batching and resparsification
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric et al.
Nearly Linear Row Sampling Algorithm for Quantile Regression
Yi Li, Ruosong Wang, Lin Yang et al.
Near-optimal Regret Bounds for Stochastic Shortest Path
Aviv Rosenberg, Alon Cohen, Yishay Mansour et al.
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
Chiranjib Bhattacharyya, Ravindran Kannan
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Allan Grønlund, Lior Kamma, Kasper Green Larsen
Negative Sampling in Semi-Supervised learning
John Chen, Vatsal Shah, Anastasios Kyrillidis