Papers
Thunder: a Fast Coordinate Selection Solver for Sparse Learning
Shaogang Ren, Weijie Zhao, Ping Li
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
Chu Zhou, Hang Zhao, Jin Han et al.
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Ben Letham, Roberto Calandra, Akshara Rai et al.
Deep Archimedean Copulas
Chun Kai Ling, Fei Fang, J. Zico Kolter
Explainable Voting
Dominik Peters, Ariel D Procaccia, Alexandros Psomas et al.
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
Kaihua Tang, Jianqiang Huang, Hanwang Zhang
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono, Yao Zhang, Mihaela van der Schaar
Auto Learning Attention
Benteng Ma, Jing Zhang, Yong Xia et al.
HiPPO: Recurrent Memory with Optimal Polynomial Projections
Albert Gu, Tri Dao, Stefano Ermon et al.
A causal view of compositional zero-shot recognition
Yuval Atzmon, Felix Kreuk, Uri Shalit et al.
On the Similarity between the Laplace and Neural Tangent Kernels
Amnon Geifman, Abhay Yadav, Yoni Kasten et al.
A Bayesian Nonparametrics View into Deep Representations
Michał Jamroż, Marcin Kurdziel, Mateusz Opala
Assessing SATNet's Ability to Solve the Symbol Grounding Problem
Oscar Chang, Lampros Flokas, Hod Lipson et al.
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli et al.
Continuous Submodular Maximization: Beyond DR-Submodularity
Moran Feldman, Amin Karbasi
A Unifying View of Optimism in Episodic Reinforcement Learning
Gergely Neu, Ciara Pike-Burke
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas et al.
Recurrent Quantum Neural Networks
Johannes Bausch
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Ambar Pal, Rene Vidal
Learning to search efficiently for causally near-optimal treatments
Samuel Håkansson, Viktor Lindblom, Omer Gottesman et al.
The Implications of Local Correlation on Learning Some Deep Functions
Eran Malach, Shai Shalev-Shwartz
FleXOR: Trainable Fractional Quantization
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim et al.
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
Joey Huchette, Haihao Lu, Hossein Esfandiari et al.