Statistical Learning
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data.
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrรถdinger Equation
More specifically, we propose "Wasserstein Quantum Monte Carlo"
A Appendix
A.1 Remarks on executed benchmarks We executed all benchmarks faithfully and to the best of our knowledge. In particular, with regards to the multi-modal transformer scaling behavior, as there are in fact no such studies for AR models yet to compare to. The integration of Chefer was straightforward. We did not further investigate or fine-tune evaluations to any method. In Figure 1 we ran Chefer with a full backward pass.
Robust Matrix Sensing in the Semi-Random Model
Low-rank matrix recovery is a fundamental problem in machine learning with numerous applications.
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks Y eshu Li
The DAG structure of a Bayesian network is typically unknown in practice [Natori et al., 2017,
Explore to Generalize in Zero-Shot RL
Recent developments in reinforcement learning (RL) led to algorithms that surpass human experts in a broad range of tasks [Mnih et al., 2015, Vinyals et al., 2019, Schrittwieser et al., 2020, Wurman et al.,
