Papers
Large Covariance Matrix Estimation With Nonnegative Correlations
Yixin Yan, QIAO YANG, Ziping Zhao
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
Masahiro Kato, Shinji Ito
Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient Descent
Guillaume Braun, Minh Ha Quang, Masaaki Imaizumi
Learning-Augmented Algorithms for Online Concave Packing and Convex Covering Problems
Elena Grigorescu, Young-San Lin, Maoyuan Song
Learning from biased positive-unlabeled data via threshold calibration
Paweł Teisseyre, Timo Martens, Jessa Bekker et al.
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
Berfin Simsek, Amire Bendjeddou, Daniel Hsu
Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks
Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche
Learning Graph Node Embeddings by Smooth Pair Sampling
Konstantin Kutzkov
Learning High-dimensional Gaussians from Censored Data
Arnab Bhattacharyya, Constantinos Costis Daskalakis, Themis Gouleakis et al.
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
Jiaru Zhang, Rui Ding, Qiang Fu et al.
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
Woojin Chae, Kihyuk Hong, Yufan Zhang et al.
Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis
Sihan Zeng, Sujay Bhatt, Alec Koppel et al.
Learning Laplacian Positional Encodings for Heterophilous Graphs
Michael Ito, Jiong Zhu, Dexiong Chen et al.
Learning Pareto manifolds in high dimensions: How can regularization help?
Tobias Wegel, Filip Kovačević, Alexandru Tifrea et al.
Learning signals defined on graphs with optimal transport and Gaussian process regression
Raphael Carpintero Perez, Sébastien Da Veiga, Josselin Garnier et al.
Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators
Naichang Ke, Ryogo Tanaka, Yoshinobu Kawahara
Learning the Distribution Map in Reverse Causal Performative Prediction
Daniele Bracale, Subha Maity, Yuekai Sun et al.
Learning the Pareto Front Using Bootstrapped Observation Samples
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes
Csaba Tóth, Masaki Adachi, Michael A Osborne et al.
Learning to Negotiate via Voluntary Commitment
Shuhui Zhu, Baoxiang Wang, Sriram Ganapathi Subramanian et al.
Learning Visual-Semantic Subspace Representations
Gabriel Moreira, Manuel Marques, Joao Costeira et al.
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
Arthur Pignet, Chiara Regniez, John Klein
Level Set Teleportation: An Optimization Perspective
Aaron Mishkin, Alberto Bietti, Robert M. Gower
Leveraging Frozen Batch Normalization for Co-Training in Source-Free Domain Adaptation
Xianwen Deng, Yijun Wang, Zhi Xue
Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications
Matthew Werenski, Brendan Mallery, Shuchin Aeron et al.