Papers
Determinantal Reinforcement Learning
Takayuki Osogami, Rudy Raymond
Biologically Motivated Algorithms for Propagating Local Target Representations
Alexander G. Ororbia, Ankur Mali
Multigrid Backprojection Super–Resolution and Deep Filter Visualization
Pablo Navarrete Michelini, Hanwen Liu, Dan Zhu
Efficient Counterfactual Learning from Bandit Feedback
Yusuke Narita, Shota Yasui, Kohei Yata
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
Hyoungwook Nam, Segwang Kim, Kyomin Jung
Subspace Selection via DR-Submodular Maximization on Lattices
So Nakashima, Takanori Maehara
ClusterGAN: Latent Space Clustering in Generative Adversarial Networks
Sudipto Mukherjee, Himanshu Asnani, Eugene Lin et al.
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey et al.
Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting
Kohei Miyaguchi, Hiroshi Kajino
A Probabilistic Derivation of LASSO and L12-Norm Feature Selections
Di Ming, Chris Ding, Feiping Nie
A Two-Stream Mutual Attention Network for Semi-Supervised Biomedical Segmentation with Noisy Labels
Shaobo Min, Xuejin Chen, Zheng-Jun Zha et al.
Cost-Sensitive Learning to Rank
Ryan McBride, Ke Wang, Zhouyang Ren et al.
Towards Better Interpretability in Deep Q-Networks
Raghuram Mandyam Annasamy, Katia Sycara
DyS: A Framework for Mixture Models in Quantification
André Maletzke, Denis dos Reis, Everton Cherman et al.
A Distillation Approach to Data Efficient Individual Treatment Effect Estimation
Maggie Makar, Adith Swaminathan, Emre Kıcıman
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody
Complex Unitary Recurrent Neural Networks Using Scaled Cayley Transform
Kehelwala D. G. Maduranga, Kyle E. Helfrich, Qiang Ye
LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition
Yuchao Ma, Hassan Ghasemzadeh
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning
Shuai Ma, Jia Yuan Yu
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
Simon Luo, Mahito Sugiyama
Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees
Lei Luo, Jie Xu, Cheng Deng et al.
Orthogonality-Promoting Dictionary Learning via Bayesian Inference
Lei Luo, Jie Xu, Cheng Deng et al.
Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS)
Chen Luo, Anshumali Shrivastava