Papers
Structured agents for physical construction
Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch et al.
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Kshitij Bansal, Sarah Loos, Markus Rabe et al.
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
Muhammed Fatih Balın, Abubakar Abid, James Zou
Open-ended learning in symmetric zero-sum games
David Balduzzi, Marta Garnelo, Yoram Bachrach et al.
Provable Guarantees for Gradient-Based Meta-Learning
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji, Hamed Hassani, Rama Chellappa et al.
Scalable Fair Clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak et al.
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior
Fadhel Ayed, Juho Lee, Francois Caron
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
Sergul Aydore, Bertrand Thirion, Gael Varoquaux
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Jordan Awan, Ana Kenney, Matthew Reimherr et al.
Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations
Kubilay Atasu, Thomas Mittelholzer
Bayesian Optimization of Composite Functions
Raul Astudillo, Peter Frazier
Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas et al.
Distributed Weighted Matching via Randomized Composable Coresets
Sepehr Assadi, Mohammadhossein Bateni, Vahab Mirrokni
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Sanjeev Arora, Simon Du, Wei Hu et al.
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert et al.
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
Luigi Antelmi, Nicholas Ayache, Philippe Robert et al.
Sorting Out Lipschitz Function Approximation
Cem Anil, James Lucas, Roger Grosse
Scaling Up Ordinal Embedding: A Landmark Approach
Jesse Anderton, Javed Aslam
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
Marco Ancona, Cengiz Oztireli, Markus Gross
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
Kareem Amin, Alex Kulesza, Andres Munoz et al.
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Ahsan Alvi, Binxin Ru, Jan-Peter Calliess et al.
A Convergence Theory for Deep Learning via Over-Parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song
Infinite Mixture Prototypes for Few-shot Learning
Kelsey Allen, Evan Shelhamer, Hanul Shin et al.
Analogies Explained: Towards Understanding Word Embeddings
Carl Allen, Timothy Hospedales