Papers
4,025 papers found
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
Chris J. Cundy, Rishi Desai, Stefano Ermon
Privacy-Preserving Decentralized Actor-Critic for Cooperative Multi-Agent Reinforcement Learning
Maheed H. Ahmed, Mahsa Ghasemi
Private Learning with Public Features
Walid Krichene, Nicolas E Mayoraz, Steffen Rendle et al.
Probabilistic Calibration by Design for Neural Network Regression
Victor Dheur, Souhaib Ben Taieb
Probabilistic Integral Circuits
Gennaro Gala, Cassio de Campos, Robert Peharz et al.
Probabilistic Modeling for Sequences of Sets in Continuous-Time
Yuxin Chang, Alex J Boyd, Padhraic Smyth
Provable local learning rule by expert aggregation for a Hawkes network
Sophie Jaffard, Samuel Vaiter, Alexandre Muzy et al.
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
Nikita Tsoy, Anna Mihalkova, Teodora N Todorova et al.
Provable Policy Gradient Methods for Average-Reward Markov Potential Games
Min Cheng, Ruida Zhou, P. R. Kumar et al.
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
Damien Ferbach, Baptiste Goujaud, Gauthier Gidel et al.
Proximal Causal Inference for Synthetic Control with Surrogates
Jizhou Liu, Eric Tchetgen Tchetgen, Carlos Varjão
Proxy Methods for Domain Adaptation
Katherine Tsai, Stephen R Pfohl, Olawale Salaudeen et al.
P-tensors: a General Framework for Higher Order Message Passing in Subgraph Neural Networks
Andrew R. Hands, Tianyi Sun, Risi Kondor
Pure Exploration in Bandits with Linear Constraints
Emil Carlsson, Debabrota Basu, Fredrik Johansson et al.
Quantifying intrinsic causal contributions via structure preserving interventions
Dominik Janzing, Patrick Blöbaum, Atalanti A Mastakouri et al.
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models
Frederiek Wesel, Kim Batselier
Queuing dynamics of asynchronous Federated Learning
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov et al.
Random Oscillators Network for Time Series Processing
Andrea Ceni, Andrea Cossu, Maximilian W Stölzle et al.
Recovery Guarantees for Distributed-OMP
Chen Amiraz, Robert Krauthgamer, Boaz Nadler
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures
Hao Liang, Zhiquan Luo
Reparameterized Variational Rejection Sampling
Martin Jankowiak, Du Phan
Resilient Constrained Reinforcement Learning
Dongsheng Ding, Zhengyan Huan, Alejandro Ribeiro
Restricted Isometry Property of Rank-One Measurements with Random Unit-Modulus Vectors
Wei Zhang, Zhenni Wang
Revisiting the Noise Model of Stochastic Gradient Descent
Barak Battash, Lior Wolf, Ofir Lindenbaum