Papers
Detecting Overfitting via Adversarial Examples
Roman Werpachowski, András György, Csaba Szepesvari
Differentiable Cloth Simulation for Inverse Problems
Junbang Liang, Ming Lin, Vladlen Koltun
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis
Yuki Yoshida, Masato Okada
Energy-Inspired Models: Learning with Sampler-Induced Distributions
John Lawson, George Tucker, Bo Dai et al.
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar et al.
Better Transfer Learning with Inferred Successor Maps
Tamas Madarasz, Tim Behrens
Hamiltonian Neural Networks
Samuel Greydanus, Misko Dzamba, Jason Yosinski
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
Adithya M Devraj, Jianshu Chen
Neural Lyapunov Control
Ya-Chien Chang, Nima Roohi, Sicun Gao
Weighted Linear Bandits for Non-Stationary Environments
Yoan Russac, Claire Vernade, Olivier Cappé
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh et al.
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Sharan Vaswani, Aaron Mishkin, Issam Laradji et al.
Modeling Tabular data using Conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante et al.
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
Lili Su, Pengkun Yang
The Option Keyboard: Combining Skills in Reinforcement Learning
Andre Barreto, Diana Borsa, Shaobo Hou et al.
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Maher Nouiehed, Maziar Sanjabi, Tianjian Huang et al.
Escaping from saddle points on Riemannian manifolds
Yue Sun, Nicolas Flammarion, Maryam Fazel
A Latent Variational Framework for Stochastic Optimization
Philippe Casgrain
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Hadi Salman, Greg Yang, Huan Zhang et al.
Sample Complexity of Learning Mixture of Sparse Linear Regressions
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor et al.
Inverting Deep Generative models, One layer at a time
Qi Lei, Ajil Jalal, Inderjit S Dhillon et al.
Diffusion Improves Graph Learning
Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann
What the Vec? Towards Probabilistically Grounded Embeddings
Carl Allen, Ivana Balazevic, Timothy Hospedales
Nonlinear scaling of resource allocation in sensory bottlenecks
Laura Rose Edmondson, Alejandro Jimenez Rodriguez, Hannes P. Saal
DetNAS: Backbone Search for Object Detection
Yukang Chen, Tong Yang, Xiangyu Zhang et al.