Papers
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth, Michael J Hutchinson, Yee Whye Teh
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof Schütt, Oliver Unke, Michael Gastegger
Equivariant Networks for Pixelized Spheres
Mehran Shakerinava, Siamak Ravanbakhsh
Estimating $α$-Rank from A Few Entries with Low Rank Matrix Completion
Yali Du, Xue Yan, Xu Chen et al.
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimation and Quantization of Expected Persistence Diagrams
Vincent Divol, Theo Lacombe
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner et al.
Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo
James Brofos, Roy R Lederman
Event Outlier Detection in Continuous Time
Siqi Liu, Milos Hauskrecht
Evolving Attention with Residual Convolutions
Yujing Wang, Yaming Yang, Jiangang Bai et al.
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Zitong Yang, Yu Bai, Song Mei
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning
Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi
Examining and Combating Spurious Features under Distribution Shift
Chunting Zhou, Xuezhe Ma, Paul Michel et al.
Explainable Automated Graph Representation Learning with Hyperparameter Importance
Xin Wang, Shuyi Fan, Kun Kuang et al.
Explaining Time Series Predictions with Dynamic Masks
Jonathan Crabbé, Mihaela Van Der Schaar
Explanations for Monotonic Classifiers.
Joao Marques-Silva, Thomas Gerspacher, Martin C Cooper et al.
Exploiting Shared Representations for Personalized Federated Learning
Liam Collins, Hamed Hassani, Aryan Mokhtari et al.
Exploiting structured data for learning contagious diseases under incomplete testing
Maggie Makar, Lauren West, David Hooper et al.
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
Luisa M Zintgraf, Leo Feng, Cong Lu et al.
Explore Visual Concept Formation for Image Classification
Shengzhou Xiong, Yihua Tan, Guoyou Wang
Exponentially Many Local Minima in Quantum Neural Networks
Xuchen You, Xiaodi Wu
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
Arkopal Dutt, Andrey Lokhov, Marc D Vuffray et al.
Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks
Xin Zhao, Zeru Zhang, Zijie Zhang et al.
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction
Aditi Jha, Michael J. Morais, Jonathan W Pillow