Papers
Optimal Auctions through Deep Learning
Paul Duetting, Zhe Feng, Harikrishna Narasimhan et al.
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
Yilun Du, Karthic Narasimhan
Incorporating Grouping Information into Bayesian Decision Tree Ensembles
Junliang Du, Antonio Linero
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon Du, Jason Lee, Haochuan Li et al.
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang et al.
Generalized No Free Lunch Theorem for Adversarial Robustness
Elvis Dohmatob
Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr, Michael Volpp, Marc Toussaint et al.
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning
Thinh Doan, Siva Maguluri, Justin Romberg
Noisy Dual Principal Component Pursuit
Tianyu Ding, Zhihui Zhu, Tianjiao Ding et al.
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
Xiaohan Ding, Guiguang Ding, Yuchen Guo et al.
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane et al.
Learning to Convolve: A Generalized Weight-Tying Approach
Nichita Diaconu, Daniel Worrall
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology
Onur Dereli, Ceyda Oğuz, Mehmet Gönen
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi et al.
Stochastic Deep Networks
Gwendoline De Bie, Gabriel Peyré, Marco Cuturi
Teaching a black-box learner
Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis et al.
TarMAC: Targeted Multi-Agent Communication
Abhishek Das, Théophile Gervet, Joshua Romoff et al.
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander Ratner et al.
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
Tri Dao, Albert Gu, Matthew Eichhorn et al.
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei et al.
Bayesian Optimization Meets Bayesian Optimal Stopping
Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low et al.
The Value Function Polytope in Reinforcement Learning
Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux et al.
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Milan Cvitkovic, Badal Singh, Animashree Anandkumar
Minimal Achievable Sufficient Statistic Learning
Milan Cvitkovic, Günther Koliander