89,137 research outputs found
Manipulation-Proof Machine Learning
An increasing number of decisions are guided by machine learning algorithms.
In many settings, from consumer credit to criminal justice, those decisions are
made by applying an estimator to data on an individual's observed behavior. But
when consequential decisions are encoded in rules, individuals may
strategically alter their behavior to achieve desired outcomes. This paper
develops a new class of estimator that is stable under manipulation, even when
the decision rule is fully transparent. We explicitly model the costs of
manipulating different behaviors, and identify decision rules that are stable
in equilibrium. Through a large field experiment in Kenya, we show that
decision rules estimated with our strategy-robust method outperform those based
on standard supervised learning approaches
Basil Leaf Automation
Recent population and wage increases have forced farmers to grow more food without a proportionate increase in work force. Automation is a key factor in reducing cost and increasing efficiency. In this paper, we explore our automation solution that utilizes position manipulation and vision processing to identify, pick up, and drop a leaf into a can. Two stepper motors and a linear actuator drove the three-dimensional actuation. Leaf and can recognition were accomplished through edge detection and machine learning algorithms. Testing proved subsystem-level functionality and proof of concept of a delicate autonomous pick-and-place robot
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
The Disparate Effects of Strategic Manipulation
When consequential decisions are informed by algorithmic input, individuals
may feel compelled to alter their behavior in order to gain a system's
approval. Models of agent responsiveness, termed "strategic manipulation,"
analyze the interaction between a learner and agents in a world where all
agents are equally able to manipulate their features in an attempt to "trick" a
published classifier. In cases of real world classification, however, an
agent's ability to adapt to an algorithm is not simply a function of her
personal interest in receiving a positive classification, but is bound up in a
complex web of social factors that affect her ability to pursue certain action
responses. In this paper, we adapt models of strategic manipulation to capture
dynamics that may arise in a setting of social inequality wherein candidate
groups face different costs to manipulation. We find that whenever one group's
costs are higher than the other's, the learner's equilibrium strategy exhibits
an inequality-reinforcing phenomenon wherein the learner erroneously admits
some members of the advantaged group, while erroneously excluding some members
of the disadvantaged group. We also consider the effects of interventions in
which a learner subsidizes members of the disadvantaged group, lowering their
costs in order to improve her own classification performance. Here we encounter
a paradoxical result: there exist cases in which providing a subsidy improves
only the learner's utility while actually making both candidate groups
worse-off--even the group receiving the subsidy. Our results reveal the
potentially adverse social ramifications of deploying tools that attempt to
evaluate an individual's "quality" when agents' capacities to adaptively
respond differ.Comment: 29 pages, 4 figure
Composable Deep Reinforcement Learning for Robotic Manipulation
Model-free deep reinforcement learning has been shown to exhibit good
performance in domains ranging from video games to simulated robotic
manipulation and locomotion. However, model-free methods are known to perform
poorly when the interaction time with the environment is limited, as is the
case for most real-world robotic tasks. In this paper, we study how maximum
entropy policies trained using soft Q-learning can be applied to real-world
robotic manipulation. The application of this method to real-world manipulation
is facilitated by two important features of soft Q-learning. First, soft
Q-learning can learn multimodal exploration strategies by learning policies
represented by expressive energy-based models. Second, we show that policies
learned with soft Q-learning can be composed to create new policies, and that
the optimality of the resulting policy can be bounded in terms of the
divergence between the composed policies. This compositionality provides an
especially valuable tool for real-world manipulation, where constructing new
policies by composing existing skills can provide a large gain in efficiency
over training from scratch. Our experimental evaluation demonstrates that soft
Q-learning is substantially more sample efficient than prior model-free deep
reinforcement learning methods, and that compositionality can be performed for
both simulated and real-world tasks.Comment: Videos: https://sites.google.com/view/composing-real-world-policies
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