Papers
Hardness-Aware Deep Metric Learning
Wenzhao Zheng, Zhaodong Chen, Jiwen Lu et al.
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
Siyuan Qiao, Zhe Lin, Jianming Zhang et al.
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions
Yusuke Tsuzuku, Issei Sato
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem
Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf
Kervolutional Neural Networks
Chen Wang, Jianfei Yang, Lihua Xie et al.
Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning
Spyros Gidaris, Nikos Komodakis
Edge-Labeling Graph Neural Network for Few-Shot Learning
Jongmin Kim, Taesup Kim, Sungwoong Kim et al.
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Hongyang Li, David Eigen, Samuel Dodge et al.
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory
Blake Woodworth, Nathan Srebro
Open Problem: Monotonicity of Learning
Tom Viering, Alexander Mey, Marco Loog
Open Problem: Risk of Ruin in Multiarmed Bandits
Filipo S. Perotto, Mathieu Bourgais, Bruno C. Silva et al.
Open Problem: Do Good Algorithms Necessarily Query Bad Points?
Rong Ge, Prateek Jain, Sham M. Kakade et al.
Open Problem: How fast can a multiclass test set be overfit?
Vitaly Feldman, Roy Frostig, Moritz Hardt
Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?
Amit Daniely, Vitaly Feldman
Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate
Lijun Zhang, Zhi-Hua Zhou
Estimating the Mixing Time of Ergodic Markov Chains
Geoffrey Wolfer, Aryeh Kontorovich
Estimation of smooth densities in Wasserstein distance
Jonathan Weed, Quentin Berthet
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds
Santosh Vempala, John Wilmes
Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen, Maxim Raginsky
The Relative Complexity of Maximum Likelihood Estimation, MAP Estimation, and Sampling
Christopher Tosh, Sanjoy Dasgupta
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions
Adrien Taylor, Francis Bach
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy et al.
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression
Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar et al.
Maximum Entropy Distributions: Bit Complexity and Stability
Damian Straszak, Nisheeth K. Vishnoi