Papers
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
Andrew Jesson, Sören Mindermann, Yarin Gal et al.
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
Eran Malach, Pritish Kamath, Emmanuel Abbe et al.
Quantile Bandits for Best Arms Identification
Mengyan Zhang, Cheng Soon Ong
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
Akira Nakagawa, Keizo Kato, Taiji Suzuki
Quantization Algorithms for Random Fourier Features
Xiaoyun Li, Ping Li
Quantum algorithms for reinforcement learning with a generative model
Daochen Wang, Aarthi Sundaram, Robin Kothari et al.
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Tao Lin, Sai Praneeth Karimireddy, Sebastian Stich et al.
Query Complexity of Adversarial Attacks
Grzegorz Gluch, Rüdiger Urbanke
Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint
Shuang Cui, Kai Han, Tianshuai Zhu et al.
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering
Shyam Narayanan, Sandeep Silwal, Piotr Indyk et al.
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Shariq Iqbal, Christian A Schroeder De Witt, Bei Peng et al.
Randomized Exploration in Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen et al.
Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei et al.
RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg, Sivaraman Balakrishnan, Zico Kolter et al.
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
Haitian Sun, Patrick Verga, Bhuwan Dhingra et al.
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
Elad Sarafian, Shai Keynan, Sarit Kraus
Recovering AES Keys with a Deep Cold Boot Attack
Itamar Zimerman, Eliya Nachmani, Lior Wolf
Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
Xinlei Yi, Xiuxian Li, Tao Yang et al.
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
Regularized Online Allocation Problems: Fairness and Beyond
Santiago Balseiro, Haihao Lu, Vahab Mirrokni
Regularized Submodular Maximization at Scale
Ehsan Kazemi, Shervin Minaee, Moran Feldman et al.
Regularizing towards Causal Invariance: Linear Models with Proxies
Michael Oberst, Nikolaj Thams, Jonas Peters et al.
Reinforcement Learning for Cost-Aware Markov Decision Processes
Wesley Suttle, Kaiqing Zhang, Zhuoran Yang et al.
Reinforcement Learning of Implicit and Explicit Control Flow Instructions
Ethan Brooks, Janarthanan Rajendran, Richard L Lewis et al.
Reinforcement Learning Under Moral Uncertainty
Adrien Ecoffet, Joel Lehman