Papers
4,122 papers found
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
Ethan X. Fang, Yajun Mei, Yuyang Shi et al.
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
Ben Hambly, Renyuan Xu, Huining Yang
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz
Posterior Consistency for Bayesian Relevance Vector Machines
Xiao Fang, Malay Ghosh
Posterior Contraction for Deep Gaussian Process Priors
Gianluca Finocchio, Johannes Schmidt-Hieber
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
Gavin Zhang, Salar Fattahi, Richard Y. Zhang
Prediction Equilibrium for Dynamic Network Flows
Lukas Graf, Tobias Harks, Kostas Kollias et al.
Principled Out-of-Distribution Detection via Multiple Testing
Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy et al.
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann et al.
Privacy-Aware Rejection Sampling
Jordan Awan, Vinayak Rao
ProtoryNet - Interpretable Text Classification Via Prototype Trajectories
Dat Hong, Tong Wang, Stephen Baek
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI
Samuel Hess, Gregory Ditzler
Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints
Qinbo Bai, Vaneet Aggarwal, Ather Gattami
Python package for causal discovery based on LiNGAM
Takashi Ikeuchi, Mayumi Ide, Yan Zeng et al.
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity
Ali Kara, Naci Saldi, Serdar Yüksel
q-Learning in Continuous Time
Yanwei Jia, Xun Yu Zhou
Quantifying Network Similarity using Graph Cumulants
Gecia Bravo-Hermsdorff, Lee M. Gunderson, Pierre-André Maugis et al.
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk et al.
Quasi-Equivalence between Width and Depth of Neural Networks
Fenglei Fan, Rongjie Lai, Ge Wang
Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning
John Harlim, Shixiao Willing Jiang, John Wilson Peoples
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett
Random Forests for Change Point Detection
Malte Londschien, Peter Bühlmann, Solt Kovács
Randomized Spectral Co-Clustering for Large-Scale Directed Networks
Xiao Guo, Yixuan Qiu, Hai Zhang et al.
RankSEG: A Consistent Ranking-based Framework for Segmentation
Ben Dai, Chunlin Li