Papers
11,015 papers found
Robust and Controllable Object-Centric Learning through Energy-based Models
Ruixiang ZHANG, Tong Che, Boris Ivanovic et al.
Robust Explanation Constraints for Neural Networks
Matthew Robert Wicker, Juyeon Heo, Luca Costabello et al.
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra et al.
Robust Graph Dictionary Learning
Weijie Liu, Jiahao Xie, Chao Zhang et al.
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
Linbo Liu, Youngsuk Park, Trong Nghia Hoang et al.
Robustness to corruption in pre-trained Bayesian neural networks
Xi Wang, Laurence Aitchison
Robust Scheduling with GFlowNets
David W Zhang, Corrado Rainone, Markus Peschl et al.
ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Han Lu, Zenan Li, Runzhong Wang et al.
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data
Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma et al.
ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning
Olga Golovneva, Moya Peng Chen, Spencer Poff et al.
Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction
Shitong Luo, Yufeng Su, Zuofan Wu et al.
RPM: Generalizable Multi-Agent Policies for Multi-Agent Reinforcement Learning
Wei Qiu, Xiao Ma, Bo An et al.
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL
Ruiquan Huang, Jing Yang, Yingbin Liang
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
Yannick Hogewind, Thiago D. Simão, Tal Kachman et al.
SAM as an Optimal Relaxation of Bayes
Thomas Möllenhoff, Mohammad Emtiyaz Khan
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
Xiang Ji, Minshuo Chen, Mengdi Wang et al.
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier
Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin et al.
Sampling-based inference for large linear models, with application to linearised Laplace
Javier Antoran, Shreyas Padhy, Riccardo Barbano et al.
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
Nicholas Gao, Stephan Günnemann
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li et al.
Sampling with Mollified Interaction Energy Descent
Lingxiao Li, qiang liu, Anna Korba et al.
Scaffolding a Student to Instill Knowledge
Anil Kag, Durmus Alp Emre Acar, Aditya Gangrade et al.
Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions
Jeremy Ocampo, Matthew Alexander Price, Jason McEwen
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
Renyu Zhang, Aly A Khan, Robert L. Grossman et al.
Scalable Subset Sampling with Neural Conditional Poisson Networks
Adeel Pervez, Phillip Lippe, Efstratios Gavves