Papers
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
Josiah Hanna, Scott Niekum, Peter Stone
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
Chunjiang Zhu, Sabine Storandt, Kam-Yiu Lam et al.
Improved Parallel Algorithms for Density-Based Network Clustering
Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrović
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
Kaiyi Ji, Zhe Wang, Yi Zhou et al.
Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang, Kun Xu, Chao Du et al.
Improving Model Selection by Employing the Test Data
Max Westphal, Werner Brannath
Improving Neural Language Modeling via Adversarial Training
Dilin Wang, Chengyue Gong, Qiang Liu
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel et al.
Imputing Missing Events in Continuous-Time Event Streams
Hongyuan Mei, Guanghui Qin, Jason Eisner
Incorporating Grouping Information into Bayesian Decision Tree Ensembles
Junliang Du, Antonio Linero
Incremental Randomized Sketching for Online Kernel Learning
Xiao Zhang, Shizhong Liao
Inference and Sampling of $K_33$-free Ising Models
Valerii Likhosherstov, Yury Maximov, Misha Chertkov
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Muhammad Osama, Dave Zachariah, Thomas B. Schön
Infinite Mixture Prototypes for Few-shot Learning
Kelsey Allen, Evan Shelhamer, Hanul Shin et al.
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen, Nan Jiang
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Mitchell Stern, William Chan, Jamie Kiros et al.
Interpreting Adversarially Trained Convolutional Neural Networks
Tianyuan Zhang, Zhanxing Zhu
Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen et al.
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy et al.
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis, Alex Renda, Dr.Saman Amarasinghe et al.
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
Shengjie Wang, Tianyi Zhou, Jeff Bilmes
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Zaiyi Chen, Yi Xu, Haoyuan Hu et al.
Kernel-Based Reinforcement Learning in Robust Markov Decision Processes
Shiau Hong Lim, Arnaud Autef