12 citations · 28 across the 4 of their papers we have counts for
7 papers
Supervised Learning on Relational Databases with Graph Neural Networks
Milan Cvitkovic
The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.]. But training machine learnin…
SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning
Keng Wah Loon, Laura Graesser, Milan Cvitkovic
We introduce SLM Lab, a software framework for reproducible reinforcement learning (RL) research. SLM Lab implements a number of popular RL algorithms, provides synchronous and asy…
Sampling-Free Learning of Bayesian Quantized Neural Networks
Jiahao Su, Milan Cvitkovic, Furong Huang
Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this paper, we propose Bayesia…
Minimal Achievable Sufficient Statistic Learning
Milan Cvitkovic, Günther Koliander
We introduce Minimal Achievable Sufficient Statistic (MASS) Learning, a training method for machine learning models that attempts to produce minimal sufficient statistics with resp…
A General Method for Amortizing Variational Filtering
Joseph Marino, Milan Cvitkovic, Yisong Yue
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information fro…
Some Requests for Machine Learning Research from the East African Tech Scene
Milan Cvitkovic
Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit…