Statistical Learning
Interaction-Force Transport Gradient Flows
This paper presents a new gradient flow dissipation geometry over non-negative and probability measures.
Batch Active Learning at Scale
Acquiring this data can easily become a bottleneck in cost, time, and computational resources.
Sampling from Log-Concave Distributions with Infinity-Distance Guarantees Oren Mangoubi Worcester Polytechnic Institute Nisheeth K. Vishnoi Y ale University
This approach also allows us to obtain an improvement on the dimension d in the running time for the problem of sampling from a log-concave distribution on polytopes K with infinity distance Á, by plugging in TV -distance running time bounds for the Dikin Walk Markov chain.
Stochastic optimization under time drift: iterate averaging, step decay, and high-probability guarantees
Two examples are worth highlighting.
A code
This section is meant to give an overview of our opensource code. Together with this git repo, we include a'tutorial colab' - a Jupyter notebooks that can be run in the browser without requiring any local installation at We view this open-source effort as a major contribution of our paper. We present the testbed pseudocode in this section. Recall from Section 3.1 that we We now describe the other parameters we use in the Testbed. In this section, we describe the benchmark agents in Section 3.3 and the choice of various Step 3: compute likelihoods for n = 1, 2, . . .
The Neural Testbed: Evaluating Joint Predictions
Predictive distributions quantify uncertainties ignored by point estimates.
Interaction-Grounded Learning with Action-Inclusive Feedback
Prior analyzed approaches fail when the feedback vector contains the action, which significantly limits IGL's success in many potential scenarios such as Brain-computer interface (BCI) or Human-computer interface (HCI) applications.