Papers
Transformers from Diffusion: A Unified Framework for Neural Message Passing
Qitian Wu, David Wipf, Junchi Yan
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Yong Lin, Chen Liu, Chenlu Ye et al.
Actor-Critic learning for mean-field control in continuous time
Noufel FRIKHA, Maximilien GERMAIN, Mathieu LAURIERE et al.
Modelling Populations of Interaction Networks via Distance Metrics
George Bolt, Simón Lunagómez, Christopher Nemeth
BitNet: 1-bit Pre-training for Large Language Models
Hongyu Wang, Shuming Ma, Lingxiao Ma et al.
Physics-informed Kernel Learning
Nathan Doumèche, Francis Bach, Gérard Biau et al.
Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights
Paul Egels, Ismaël Castillo
Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL
Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi
Degree of Interference: A General Framework For Causal Inference Under Interference
Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi
Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo
Max Hird, Samuel Livingstone
Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions
Penghe Zhang, Naihua Xiu, Hou-Duo Qi
On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls
Anish Agarwal, Devavrat Shah, Dennis Shen
Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior
Ben Wu, Keru Wu, Jian Kang
DRM Revisited: A Complete Error Analysis
Yuling Jiao, Ruoxuan Li, Peiying Wu et al.
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
Han Shen, Zhuoran Yang, Tianyi Chen
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Fan Yang, Hongyang R. Zhang, Sen Wu et al.
Score-based Causal Representation Learning: Linear and General Transformations
Burak Varici, Emre Acartürk, Karthikeyan Shanmugam et al.
On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
Saptarshi Chakraborty, Peter L. Bartlett
Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
Keru Wu, Yuansi Chen, Wooseok Ha et al.
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles
Lesi Chen, Yaohua Ma, Jingzhao Zhang
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
Shao-Bo Lin, Xiaotong Liu, Di Wang et al.
On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel et al.
A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds
Lei Wang, Le Bao, Xin Liu
Learning conditional distributions on continuous spaces
Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal