Papers
4,025 papers found
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Mathieu Carriere, Frederic Chazal, Yuichi Ike et al.
POPCORN: Partially Observed Prediction Constrained Reinforcement Learning
Joseph Futoma, Michael Hughes, Finale Doshi-Velez
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information
Esther Rolf, Michael I. Jordan, Benjamin Recht
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder et al.
Precision-Recall Curves Using Information Divergence Frontiers
Josip Djolonga, Mario Lucic, Marco Cuturi et al.
Prediction Focused Topic Models via Feature Selection
Jason Ren, Russell Kunes, Finale Doshi-Velez
Prior-aware Composition Inference for Spectral Topic Models
Moontae Lee, David Bindel, David Mimno
Private k-Means Clustering with Stability Assumptions
Moshe Shechner, Or Sheffet, Uri Stemmer
Private Protocols for U-Statistics in the Local Model and Beyond
James Bell, Aurélien Bellet, Adria Gascon et al.
Prophets, Secretaries, and Maximizing the Probability of Choosing the Best
Hossein Esfandiari, MohammadTaghi Hajiaghayi, Brendan Lucier et al.
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang et al.
Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space
Quentin Mérigot, Alex Delalande, Frederic Chazal
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free
Mingrui Zhang, Lin Chen, Aryan Mokhtari et al.
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
Sebastian Farquhar, Michael A. Osborne, Yarin Gal
Randomized Exploration in Generalized Linear Bandits
Branislav Kveton, Manzil Zaheer, Csaba Szepesvari et al.
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization
Prathamesh Mayekar, Himanshu Tyagi
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
Takashi Nicholas Maeda, Shohei Shimizu
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
François-Pierre Paty, Alexandre d’Aspremont, Marco Cuturi
Regularization via Structural Label Smoothing
Weizhi Li, Gautam Dasarathy, Visar Berisha
Regularized Autoencoders via Relaxed Injective Probability Flow
Abhishek Kumar, Ben Poole, Kevin Murphy
RelatIF: Identifying Explanatory Training Samples via Relative Influence
Elnaz Barshan, Marc-Etienne Brunet, Gintare Karolina Dziugaite
Rep the Set: Neural Networks for Learning Set Representations
Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios et al.
Revisiting Stochastic Extragradient
Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin et al.
Revisiting the Landscape of Matrix Factorization
Hossein Valavi, Sulin Liu, Peter Ramadge