Papers
Nearly-tight Bounds for Deep Kernel Learning
Yifan Zhang, Min-Ling Zhang
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
Kaiwen Wang, Nathan Kallus, Wen Sun
Near-Optimal $Φ$-Regret Learning in Extensive-Form Games
Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
Hilal Asi, Vitaly Feldman, Tomer Koren et al.
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
Donghao Li, Ruiquan Huang, Cong Shen et al.
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals
Ilias Diakonikolas, Daniel Kane, Lisheng Ren
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
Yecheng Xue, Xiaoyu Chen, Tongyang Li et al.
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
Jiatao Gu, Alex Trevithick, Kai-En Lin et al.
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Yonggan Fu, Ye Yuan, Souvik Kundu et al.
Nested Elimination: A Simple Algorithm for Best-Item Identification From Choice-Based Feedback
Junwen Yang, Yifan Feng
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel et al.
Network Effects in Performative Prediction Games
Xiaolu Wang, Chung-Yiu Yau, Hoi To Wai
Neural Algorithmic Reasoning with Causal Regularisation
Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz et al.
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Hien Dang, Tho Tran Huu, Stanley Osher et al.
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Abdul Fatir Ansari, Alvin Heng, Andre Lim et al.
Neural Diffusion Processes
Vincent Dutordoir, Alan Saul, Zoubin Ghahramani et al.
Neural FIM for learning Fisher information metrics from point cloud data
Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong et al.
Neural Inverse Operators for Solving PDE Inverse Problems
Roberto Molinaro, Yunan Yang, Björn Engquist et al.
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
Cheol Jun Cho, Edward Chang, Gopala Anumanchipalli
Neural Markov Jump Processes
Patrick Seifner, Ramses J Sanchez
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods
Brian Irwin, Eldad Haber, Raviv Gal et al.
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu et al.
Neural networks trained with SGD learn distributions of increasing complexity
Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt
Neural Prediction Errors enable Analogical Visual Reasoning in Human Standard Intelligence Tests
Lingxiao Yang, Hongzhi You, Zonglei Zhen et al.
Neural signature kernels as infinite-width-depth-limits of controlled ResNets
Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi