Papers
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon et al.
Attentional Pooling for Action Recognition
Rohit Girdhar, Deva Ramanan
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar et al.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg, Su-In Lee
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys et al.
A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Ashkan Panahi, Babak Hassibi
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo et al.
Balancing information exposure in social networks
Kiran Garimella, Aristides Gionis, Nikos Parotsidis et al.
Bandits Dueling on Partially Ordered Sets
Julien Audiffren, Liva Ralaivola
Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, Max Welling
Bayesian Dyadic Trees and Histograms for Regression
Stéphanie van der Pas, Veronika Ročková
Bayesian GAN
Yunus Saatci, Andrew G Wilson
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Ahmed M. Alaa, Mihaela van der Schaar
Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew G Wilson et al.
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
Jiasen Lu, Anitha Kannan, Jianwei Yang et al.
Best Response Regression
Omer Ben-Porat, Moshe Tennenholtz
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Rebecca Morrison, Ricardo Baptista, Youssef Marzouk
Beyond Parity: Fairness Objectives for Collaborative Filtering
Sirui Yao, Bert Huang
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization
Omar El Housni, Vineet Goyal
Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi, Claudio Gentile, Gabor Lugosi et al.
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction
Zhan Shi, Xinhua Zhang, Yaoliang Yu
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu et al.
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian, Ce Zhang, Huan Zhang et al.