Summary of On Pi Controllers For Updating Lagrange Multipliers in Constrained Optimization, by Motahareh Sohrabi et al.
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimizationby Motahareh Sohrabi, Juan Ramirez, Tianyue…
Summary of On Regularization Via Early Stopping For Least Squares Regression, by Rishi Sonthalia and Jackie Lok and Elizaveta Rebrova
On Regularization via Early Stopping for Least Squares Regressionby Rishi Sonthalia, Jackie Lok, Elizaveta RebrovaFirst…
Summary of Simulating, Fast and Slow: Learning Policies For Black-box Optimization, by Fabio Valerio Massoli et al.
Simulating, Fast and Slow: Learning Policies for Black-Box Optimizationby Fabio Valerio Massoli, Tim Bakker, Thomas…
Summary of Theoretical Guarantees For Variational Inference with Fixed-variance Mixture Of Gaussians, by Tom Huix et al.
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussiansby Tom Huix, Anna Korba, Alain…
Summary of Neuro-symbolic Temporal Point Processes, by Yang Yang et al.
Neuro-Symbolic Temporal Point Processesby Yang Yang, Chao Yang, Boyang Li, Yinghao Fu, Shuang LiFirst submitted…
Summary of Enhancing In-context Learning Performance with Just Svd-based Weight Pruning: a Theoretical Perspective, by Xinhao Yao et al.
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspectiveby Xinhao Yao, Xiaolin…
Summary of Deepracer on Physical Track: Parameters Exploration and Performance Evaluation, by Sinan Koparan et al.
DeepRacer on Physical Track: Parameters Exploration and Performance Evaluationby Sinan Koparan, Bahman JavadiFirst submitted to…
Summary of Error Dynamics Of Mini-batch Gradient Descent with Random Reshuffling For Least Squares Regression, by Jackie Lok et al.
Error dynamics of mini-batch gradient descent with random reshuffling for least squares regressionby Jackie Lok,…
Summary of How Does Gradient Descent Learn Features — a Local Analysis For Regularized Two-layer Neural Networks, by Mo Zhou et al.
How Does Gradient Descent Learn Features – A Local Analysis for Regularized Two-Layer Neural Networksby…
Summary of Neural Network Learns Low-dimensional Polynomials with Sgd Near the Information-theoretic Limit, by Jason D. Lee and Kazusato Oko and Taiji Suzuki and Denny Wu
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limitby Jason D. Lee, Kazusato…