Papers
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz, Steven Lang, Antonio Vergari et al.
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
Reza Oftadeh, Jiayi Shen, Zhangyang Wang et al.
Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou, Hang Le, Miguel Del Rio et al.
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai, Yoni Halpern, Yoon Kim et al.
Encoding Musical Style with Transformer Autoencoders
Kristy Choi, Curtis Hawthorne, Ian Simon et al.
Energy-Based Processes for Exchangeable Data
Mengjiao Yang, Bo Dai, Hanjun Dai et al.
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang, Joel Lehman, Aditya Rawal et al.
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Entropy Minimization In Emergent Languages
Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt et al.
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler, Leon Klein, Frank Noe
Equivariant Neural Rendering
Emilien Dupont, Miguel Bautista Martin, Alex Colburn et al.
Error-Bounded Correction of Noisy Labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami et al.
Error Estimation for Sketched SVD via the Bootstrap
Miles Lopes, N. Benjamin Erichson, Michael Mahoney
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Jongseok Lee, Matthias Humt, Jianxiang Feng et al.
Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients
Ashley Edwards, Himanshu Sahni, Rosanne Liu et al.
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Jessie X.T. Chen, Miles Lopes
Estimating the Number and Effect Sizes of Non-null Hypotheses
Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar, Fredrik Johansson, John Guttag et al.
Evaluating Lossy Compression Rates of Deep Generative Models
Sicong Huang, Alireza Makhzani, Yanshuai Cao et al.
Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar, Rebecca Roelofs, Horia Mania et al.
Evaluating the Performance of Reinforcement Learning Algorithms
Scott Jordan, Yash Chandak, Daniel Cohen et al.
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Somdeb Majumdar, Shauharda Khadka, Santiago Miret et al.
Evolutionary Topology Search for Tensor Network Decomposition
Chao Li, Zhun Sun