Papers
Nyström Kernel Stein Discrepancy
Florian Kalinke, Zoltán Szabó, Bharath Sriperumbudur
Offline Multi-task Transfer RL with Representational Penalization
Avinandan Bose, Simon Shaolei Du, Maryam Fazel
Offline RL via Feature-Occupancy Gradient Ascent
Gergely Neu, Nneka Okolo
On adaptivity and minimax optimality of two-sided nearest neighbors
Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi
Online Assortment and Price Optimization Under Contextual Choice Models
Yigit Efe Erginbas, Thomas Courtade, Kannan Ramchandran
Online Student-$t$ Processes with an Overall-local Scale Structure for Modelling Non-stationary Data
Taole Sha, Michael Minyi Zhang
Online-to-PAC generalization bounds under graph-mixing dependencies
Baptiste Abélès, Gergely Neu, Eugenio Clerico
On Local Posterior Structure in Deep Ensembles
Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt et al.
On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
Ziyu Wang, Christopher C. Holmes
On the Asymptotic Mean Square Error Optimality of Diffusion Models
Benedikt Fesl, Benedikt Böck, Florian Strasser et al.
On the Computational Tractability of the (Many) Shapley Values
Reda Marzouk, Shahaf Bassan, Guy Katz et al.
On the Consistent Recovery of Joint Distributions from Conditionals
Mahbod Majid, Rattana Pukdee, Vishwajeet Agrawal et al.
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
Satish Kumar Keshri, Nazreen Shah, Ranjitha Prasad
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
Tim Rensmeyer, Oliver Niggemann
On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark
Jaiden Fairoze, Guillermo Ortiz-Jimenez, Mel Vecerik et al.
On the Geometry and Optimization of Polynomial Convolutional Networks
Vahid Shahverdi, Giovanni Luca Marchetti, Kathlén Kohn
On the Identifiability of Causal Abstractions
Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh
On the Inherent Privacy of Zeroth-Order Projected Gradient Descent
Devansh Gupta, Meisam Razaviyayn, Vatsal Sharan
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
Dun Zeng, Zenglin Xu, SHIYU LIU et al.
On the Power of Multitask Representation Learning with Gradient Descent
Qiaobo Li, Zixiang Chen, Yihe Deng et al.
On the Relationship Between Robustness and Expressivity of Graph Neural Networks
Lorenz Kummer, Wilfried N. Gansterer, Nils Morten Kriege
On the Sample Complexity of Next-Token Prediction
Oğuz Kaan Yüksel, Nicolas Flammarion
On Tractability of Learning Bayesian Networks with Ancestral Constraints
Juha Harviainen, Pekka Parviainen