Papers
EvoPrompting: Language Models for Code-Level Neural Architecture Search
Angelica Chen, David Dohan, David So
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
Fabian Zaiser, Andrzej Murawski, Chih-Hao Luke Ong
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Waïss Azizian, Franck Iutzeler, Jérôme Malick
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
Berivan Isik, Wei-Ning Chen, Ayfer Ozgur et al.
Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model
Maximilien Dreveton, Felipe Fernandes, Daniel Figueiredo
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
Sudhanshu Chanpuriya, Ryan Rossi, Anup B. Rao et al.
Exact Verification of ReLU Neural Control Barrier Functions
Hongchao Zhang, Junlin Wu, Yevgeniy Vorobeychik et al.
Expanding Small-Scale Datasets with Guided Imagination
Yifan Zhang, Daquan Zhou, Bryan Hooi et al.
Experimental Designs for Heteroskedastic Variance
Justin Weltz, Tanner Fiez, Alexander Volfovsky et al.
Experiment Planning with Function Approximation
Aldo Pacchiano, Jonathan Lee, Emma Brunskill
Expert load matters: operating networks at high accuracy and low manual effort
Sara Sangalli, Ertunc Erdil, Ender Konukoglu
Explainable and Efficient Randomized Voting Rules
Soroush Ebadian, Aris Filos-Ratsikas, Mohamad Latifian et al.
Explainable Brain Age Prediction using coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan et al.
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
Ao Sun, Pingchuan Ma, Yuanyuan Yuan et al.
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
David Watson, Joshua O'Hara, Niek Tax et al.
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
Galen Pogoncheff, Jacob Granley, Michael Beyeler
Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
Aaron Havens, Alexandre Araujo, Siddharth Garg et al.
Exploiting Contextual Objects and Relations for 3D Visual Grounding
Li Yang, chunfeng yuan, Ziqi Zhang et al.
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits
Arun Verma, Zhongxiang Dai, YAO SHU et al.
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
Iosif Sakos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos et al.
Explore In-Context Learning for 3D Point Cloud Understanding
Zhongbin Fang, Xiangtai Li, Xia Li et al.
Explore to Generalize in Zero-Shot RL
Ev Zisselman, Itai Lavie, Daniel Soudry et al.
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
Chudi Zhong, Zhi Chen, Jiachang Liu et al.
Exploring Diverse In-Context Configurations for Image Captioning
Xu Yang, Yongliang Wu, Mingzhuo Yang et al.