12 papers
Is Generation Required for Data-Efficient Perception?
Jack Brady, Bernhard Schölkopf, Thomas Kipf +2
It has been hypothesized that achieving the data efficiency of human visual perception requires a generative approach in which internal representations result from inverting a deco…
Use What You Know: Causal Foundation Models with Partial Graphs
Arik Reuter, Anish Dhir, Cristiana Diaconu +6
Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified a…
Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning
Hsiao-Ru Pan, Bernhard Schölkopf
Direct Advantage Estimation (DAE) has been shown to improve the sample efficiency of deep reinforcement learning algorithms. However, its reliance on full environment observability…
On the Variance of Temporal Difference Learning and its Reduction Using Control Variates
Hsiao-Ru Pan, Bernhard Schölkopf
We analyze the variance of temporal difference (TD) learning using the phased setting with tabular representation, and show that one of the mechanisms behind its ability to reduce…
The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions
Alejandro H. Artiles, Martin Weiss, Levin Brinkmann +6
Scientific discovery is constrained not only by what is true, but by what is cognitively available to the researchers currently exploring a field. Many directions are coherent in l…
PENEX: AdaBoost-Inspired Neural Network Regularization
Klaus-Rudolf Kladny, Bernhard Schölkopf, Michael Muehlebach
AdaBoost sequentially fits so-called weak learners to minimize an exponential loss, which penalizes misclassified data points more severely than other loss functions like cross-ent…