Papers
4,122 papers found
Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL
Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi
Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data
Jie Peng, Weiyu Li, Stefan Vlaski et al.
Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents
Marco Pleines, Matthias Pallasch, Frank Zimmer et al.
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Tianyang Hu, Ruiqi Liu, Zuofeng Shang et al.
Minimax Optimal Two-Sample Testing under Local Differential Privacy
Jongmin Mun, Seungwoo Kwak, Ilmun Kim
Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
Mario Bravo, Juan P. Flores-Mella, Cristóbal Guzmán
Mixtures of Gaussian Process Experts with SMC^2
Teemu Härkönen, Sara Wade, Kody Law et al.
Model-free Change-Point Detection Using AUC of a Classifier
Rohit Kanrar, Feiyu Jiang, Zhanrui Cai
Modelling Populations of Interaction Networks via Distance Metrics
George Bolt, Simón Lunagómez, Christopher Nemeth
Multiple Instance Verification
Xin Xu, Eibe Frank, Geoffrey Holmes
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles
Lesi Chen, Yaohua Ma, Jingzhao Zhang
Neural Operators Can Play Dynamic Stackelberg Games
Guillermo A. Alvarez, Ibrahim Ekren, Anastasis Kratsios et al.
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
Kuangyu Ding, Jingyang Li, Kim-Chuan Toh
Nonlinear functional regression by functional deep neural network with kernel embedding
Zhongjie Shi, Jun Fan, Linhao Song et al.
Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds
Paul Rosa, Judith Rousseau
On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations
Antoine Godichon-Baggioni, Nicklas Werge
On Consistent Bayesian Inference from Synthetic Data
Ossi Räisä, Joonas Jälkö, Antti Honkela
On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel et al.
On Inference for the Support Vector Machine
Jakub Rybak, Heather Battey, Wen-Xin Zhou
Online Quantile Regression
Yinan Shen, Dong Xia, Wen-Xin Zhou
On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls
Anish Agarwal, Devavrat Shah, Dennis Shen
On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes
Zhiheng Chen, Guanhua Fang, Wen Yu
On Probabilistic Embeddings in Optimal Dimension Reduction
Ryan Murray, Adam Pickarski
On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory
Andrea Perin, Stephane Deny