Papers
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita et al.
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson, Suriya Gunasekar, Jason Lee et al.
Learning Optimal Fair Policies
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Relational Pooling for Graph Representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao et al.
A Dynamical Systems Perspective on Nesterov Acceleration
Michael Muehlebach, Michael Jordan
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Seungyong Moon, Gaon An, Hyun Oh Song
Flat Metric Minimization with Applications in Generative Modeling
Thomas Möllenhoff, Daniel Cremers
Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
Co-manifold learning with missing data
Gal Mishne, Eric Chi, Ronald Coifman
Formal Privacy for Functional Data with Gaussian Perturbations
Ardalan Mirshani, Matthew Reimherr, Aleksandra Slavković
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
Andrew Miller, Ziad Obermeyer, John Cunningham et al.
On Dropout and Nuclear Norm Regularization
Poorya Mianjy, Raman Arora
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Meyer, Jean Honorio
Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon et al.
Reinforcement Learning in Configurable Continuous Environments
Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization
Michael Metel, Akiko Takeda
Spectral Clustering of Signed Graphs via Matrix Power Means
Pedro Mercado, Francesco Tudisco, Matthias Hein
Geometric Losses for Distributional Learning
Arthur Mensch, Mathieu Blondel, Gabriel Peyré
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis, Alex Renda, Dr.Saman Amarasinghe et al.
The Wasserstein Transform
Facundo Memoli, Zane Smith, Zhengchao Wan
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
Eldad Meller, Alexander Finkelstein, Uri Almog et al.
Imputing Missing Events in Continuous-Time Event Streams
Hongyuan Mei, Guanghui Qin, Jason Eisner
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin Duke, Piyush Rai
Toward Controlling Discrimination in Online Ad Auctions
Elisa Celis, Anay Mehrotra, Nisheeth Vishnoi