Papers
572 papers found
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Vikas Garg, Adam Tauman Kalai, Katrina Ligett et al.
Approximate Function Evaluation via Multi-Armed Bandits
Tavor Z. Baharav, Gary Cheng, Mert Pilanci et al.
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig et al.
Conditional Gradients for the Approximately Vanishing Ideal
Elias S. Wirth, Sebastian Pokutta
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search
Matti Karppa, Martin Aumüller, Rasmus Pagh
A prior-based approximate latent Riemannian metric
Georgios Arvanitidis, Bogdan M. Georgiev, Bernhard Schölkopf
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
Hsu Kao, Vijay Subramanian
Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances
Ruida Zhou, Chao Tian
Mixed Linear Regression via Approximate Message Passing
Nelvin Tan, Ramji Venkataramanan
Approximate Regions of Attraction in Learning with Decision-Dependent Distributions
Roy Dong, Heling Zhang, Lillian Ratliff
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
Lorenzo Mauri, Giacomo Zanella
Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers
Arnab Auddy, Haolin Zou, Kamiar Rahnamarad et al.
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
Haoyang Zheng, Wei Deng, Christian Moya et al.
Approximate Control for Continuous-Time POMDPs
Yannick Eich, Bastian Alt, Heinz Koeppl
Model-based Policy Optimization under Approximate Bayesian Inference
Chaoqi Wang, Yuxin Chen, Kevin Murphy
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift
Thomas L. Lee, Amos Storkey
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours
Henry W. J. Reeve, Gavin Brown
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Raghavendra Addanki, Andrew McGregor, Cameron Musco
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo et al.
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
Shlomi Weitzman, Sivan Sabato
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding
Linhai Zhang, Deyu Zhou
Agnostic KWIK learning and efficient approximate reinforcement learning
István Szita, Csaba Szepesvári
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions
Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan et al.
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar