156 citations
- Amazon (United States)US12 papers
- Massachusetts Institute of TechnologyUS7 papers
- Carnegie Mellon UniversityUS6 papers
- Google (United States)US5 papers
- Johns Hopkins UniversityUS5 papers
- Meta (Israel)IL5 papers
- California Southern UniversityUS4 papers
- Microsoft Research (United Kingdom)GB4 papers
- Toyota Technological Institute at ChicagoUS4 papers
- University of California, Los AngelesUS4 papers
- University of Southern CaliforniaUS4 papers
- National Yang Ming Chiao Tung UniversityTW3 papers
18 papers · 2 filters
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization
Gauthier Guinet, Valerio Perrone, Cédric Archambeau
Bayesian optimization (BO) is a popular method to optimize expensive black-box functions. It efficiently tunes machine learning algorithms under the implicit assumption that hyperp…
Differentially Private Adversarial Robustness Through Randomized Perturbations
Nan Xu, Oluwaseyi Feyisetan, Abhinav Aggarwal +2
Deep Neural Networks, despite their great success in diverse domains, are provably sensitive to small perturbations on correctly classified examples and lead to erroneous predictio…
On Primes, Log-Loss Scores and (No) Privacy
Abhinav Aggarwal, Zekun Xu, Oluwaseyi Feyisetan +1
Membership Inference Attacks exploit the vulnerabilities of exposing models trained on customer data to queries by an adversary. In a recently proposed implementation of an auditin…
Predicting Training Time Without Training
Luca Zancato, Alessandro Achille, Avinash Ravichandran +2
We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function. To do so, we leverage…
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Francesco Tonolini, Pablo G. Moreno, Andreas Damianou +1
We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean valu…
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning
Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola
This paper prescribes a suite of techniques for off-policy Reinforcement Learning (RL) that simplify the training process and reduce the sample complexity. First, we show that simp…