74,254 research outputs found
Unsupervised Domain Adaptation with Copula Models
We study the task of unsupervised domain adaptation, where no labeled data
from the target domain is provided during training time. To deal with the
potential discrepancy between the source and target distributions, both in
features and labels, we exploit a copula-based regression framework. The
benefits of this approach are two-fold: (a) it allows us to model a broader
range of conditional predictive densities beyond the common exponential family,
(b) we show how to leverage Sklar's theorem, the essence of the copula
formulation relating the joint density to the copula dependency functions, to
find effective feature mappings that mitigate the domain mismatch. By
transforming the data to a copula domain, we show on a number of benchmark
datasets (including human emotion estimation), and using different regression
models for prediction, that we can achieve a more robust and accurate
estimation of target labels, compared to recently proposed feature
transformation (adaptation) methods.Comment: IEEE International Workshop On Machine Learning for Signal Processing
201
Visual Causal Feature Learning
We provide a rigorous definition of the visual cause of a behavior that is
broadly applicable to the visually driven behavior in humans, animals, neurons,
robots and other perceiving systems. Our framework generalizes standard
accounts of causal learning to settings in which the causal variables need to
be constructed from micro-variables. We prove the Causal Coarsening Theorem,
which allows us to gain causal knowledge from observational data with minimal
experimental effort. The theorem provides a connection to standard inference
techniques in machine learning that identify features of an image that
correlate with, but may not cause, the target behavior. Finally, we propose an
active learning scheme to learn a manipulator function that performs optimal
manipulations on the image to automatically identify the visual cause of a
target behavior. We illustrate our inference and learning algorithms in
experiments based on both synthetic and real data.Comment: Accepted at UAI 201
Discovering Blind Spots in Reinforcement Learning
Agents trained in simulation may make errors in the real world due to
mismatches between training and execution environments. These mistakes can be
dangerous and difficult to discover because the agent cannot predict them a
priori. We propose using oracle feedback to learn a predictive model of these
blind spots to reduce costly errors in real-world applications. We focus on
blind spots in reinforcement learning (RL) that occur due to incomplete state
representation: The agent does not have the appropriate features to represent
the true state of the world and thus cannot distinguish among numerous states.
We formalize the problem of discovering blind spots in RL as a noisy supervised
learning problem with class imbalance. We learn models to predict blind spots
in unseen regions of the state space by combining techniques for label
aggregation, calibration, and supervised learning. The models take into
consideration noise emerging from different forms of oracle feedback, including
demonstrations and corrections. We evaluate our approach on two domains and
show that it achieves higher predictive performance than baseline methods, and
that the learned model can be used to selectively query an oracle at execution
time to prevent errors. We also empirically analyze the biases of various
feedback types and how they influence the discovery of blind spots.Comment: To appear at AAMAS 201
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