activity
20202022
most citedOptimal Order Simple Regret for Gaussian Process Bandits

5 citations · 13 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG20223 cited

Flexible Multiple-Objective Reinforcement Learning for Chip Placement

Fu-Chieh Chang, Yu-Wei Tseng, Ya-Wen Yu +11

Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the…

cs.LG2021

Uniform Generalization Bounds for Overparameterized Neural Networks

Sattar Vakili, Michael Bromberg, Jezabel Garcia +2

An interesting observation in artificial neural networks is their favorable generalization error despite typically being extremely overparameterized. It is well known that the clas…

stat.ML20215 cited

Optimal Order Simple Regret for Gaussian Process Bandits

Sattar Vakili, Nacime Bouziani, Sepehr Jalali +2

Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function . The problem can be cast as a Gaussian Process (GP) band…

cs.CL20211 cited

Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging

Philipp Ennen, Yen-Ting Lin, Ali Girayhan Ozbay +5

In a dialogue system pipeline, a natural language generation (NLG) unit converts the dialogue direction and content to a corresponding natural language realization. A recent trend…

cs.LG20214 cited

Meta-Learning with MAML on Trees

Jezabel R. Garcia, Federica Freddi, Feng-Ting Liao +5

In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tas…

cs.CV2020

Cyclic orthogonal convolutions for long-range integration of features

Federica Freddi, Jezabel R Garcia, Michael Bromberg +4

In Convolutional Neural Networks (CNNs) information flows across a small neighbourhood of each pixel of an image, preventing long-range integration of features before reaching deep…