Papers
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Muhammad Osama, Dave Zachariah, Thomas B. Schön
Orthogonal Random Forest for Causal Inference
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
Approximation and non-parametric estimation of ResNet-type convolutional neural networks
Kenta Oono, Taiji Suzuki
Scalable Learning in Reproducing Kernel Krein Spaces
Dino Oglic, Thomas Gärtner
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Augustus Odena, Catherine Olsson, David Andersen et al.
Model Function Based Conditional Gradient Method with Armijo-like Line Search
Peter Ochs, Yura Malitsky
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Michael Oberst, David Sontag
Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer, Eli Bingham, Martin Jankowiak et al.
Learning to Infer Program Sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum et al.
Remember and Forget for Experience Replay
Guido Novati, Petros Koumoutsakos
Training Neural Networks with Local Error Signals
Arild Nøkland, Lars Hiller Eidnes
Lossless or Quantized Boosting with Integer Arithmetic
Richard Nock, Robert Williamson
Rotation Invariant Householder Parameterization for Bayesian PCA
Rajbir Nirwan, Nils Bertschinger
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
Than Huy Nguyen, Umut Simsekli, Gael Richard
Anomaly Detection With Multiple-Hypotheses Predictions
Duc Tam Nguyen, Zhongyu Lou, Michael Klar et al.
On Connected Sublevel Sets in Deep Learning
Quynh Nguyen
Learning to bid in revenue-maximizing auctions
Thomas Nedelec, Noureddine El Karoui, Vianney Perchet
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq et al.
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani
A Framework for Bayesian Optimization in Embedded Subspaces
Amin Nayebi, Alexander Munteanu, Matthias Poloczek
Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj et al.
Learning Context-dependent Label Permutations for Multi-label Classification
Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencia et al.
Hybrid Models with Deep and Invertible Features
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh et al.
Dropout as a Structured Shrinkage Prior
Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli