1,483 research outputs found
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies
Scenarios requiring humans to choose from multiple seemingly optimal actions
are commonplace, however standard imitation learning often fails to capture
this behavior. Instead, an over-reliance on replicating expert actions induces
inflexible and unstable policies, leading to poor generalizability in an
application. To address the problem, this paper presents the first imitation
learning framework that incorporates Bayesian variational inference for
learning flexible non-parametric multi-action policies, while simultaneously
robustifying the policies against sources of error, by introducing and
optimizing disturbances to create a richer demonstration dataset. This
combinatorial approach forces the policy to adapt to challenging situations,
enabling stable multi-action policies to be learned efficiently. The
effectiveness of our proposed method is evaluated through simulations and
real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm.
Results show that, through improved flexibility and robustness, the learning
performance and control safety are better than comparison methods.Comment: 7 pages, Accepted by the 2021 International Conference on Robotics
and Automation (ICRA 2021
Effects of Teaching Methods on Swimming Skill Acquisition in Children with Developmental Coordination Disorder
This study compared the delivery of “verbal and visual” with “verbal, visual and tactile” swimming instruction to small groups of DCD pupils for front crawl and backstroke performance across 10 lessons x 30 minutes/week. The interaction and main effects for group and time on front crawl performances were not significant, indicating no differences were found between the two teaching methods. Also, the front crawl performances of the DCD swimmers did not change over the intervention period. The interaction and main effect for group on backstroke performances over the 10 weekly lessons were not significant. However, a significant main effect of time was found with backstroke changes found between Weeks 1 and 10 and Weeks 6 and 10. Sub-component analyses for horizontal body position, head position and breathing, and use of the lower limbs, revealed significant time effect improvements, but only between Weeks 1 and 10. Hence, both DCD groups improved their backstroke performances at the same rate across the 10 week intervention, despite being exposed to different instructional method
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation
Humans demonstrate a variety of interesting behavioral characteristics when
performing tasks, such as selecting between seemingly equivalent optimal
actions, performing recovery actions when deviating from the optimal
trajectory, or moderating actions in response to sensed risks. However,
imitation learning, which attempts to teach robots to perform these same tasks
from observations of human demonstrations, often fails to capture such
behavior. Specifically, commonly used learning algorithms embody inherent
contradictions between the learning assumptions (e.g., single optimal action)
and actual human behavior (e.g., multiple optimal actions), thereby limiting
robot generalizability, applicability, and demonstration feasibility. To
address this, this paper proposes designing imitation learning algorithms with
a focus on utilizing human behavioral characteristics, thereby embodying
principles for capturing and exploiting actual demonstrator behavioral
characteristics. This paper presents the first imitation learning framework,
Bayesian Disturbance Injection (BDI), that typifies human behavioral
characteristics by incorporating model flexibility, robustification, and risk
sensitivity. Bayesian inference is used to learn flexible non-parametric
multi-action policies, while simultaneously robustifying policies by injecting
risk-sensitive disturbances to induce human recovery action and ensuring
demonstration feasibility. Our method is evaluated through risk-sensitive
simulations and real-robot experiments (e.g., table-sweep task, shaft-reach
task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate
the improved characterisation of behavior. Results show significant improvement
in task performance, through improved flexibility, robustness as well as
demonstration feasibility.Comment: 69 pages, 9 figures, accepted by Elsevier Neural Networks - Journa
One-shot Empirical Privacy Estimation for Federated Learning
Privacy estimation techniques for differentially private (DP) algorithms are
useful for comparing against analytical bounds, or to empirically measure
privacy loss in settings where known analytical bounds are not tight. However,
existing privacy auditing techniques usually make strong assumptions on the
adversary (e.g., knowledge of intermediate model iterates or the training data
distribution), are tailored to specific tasks and model architectures, and
require retraining the model many times (typically on the order of thousands).
These shortcomings make deploying such techniques at scale difficult in
practice, especially in federated settings where model training can take days
or weeks. In this work, we present a novel "one-shot" approach that can
systematically address these challenges, allowing efficient auditing or
estimation of the privacy loss of a model during the same, single training run
used to fit model parameters, and without requiring any a priori knowledge
about the model architecture or task. We show that our method provides provably
correct estimates for privacy loss under the Gaussian mechanism, and we
demonstrate its performance on a well-established FL benchmark dataset under
several adversarial models
Unleashing the Power of Randomization in Auditing Differentially Private ML
We present a rigorous methodology for auditing differentially private machine
learning algorithms by adding multiple carefully designed examples called
canaries. We take a first principles approach based on three key components.
First, we introduce Lifted Differential Privacy (LiDP) that expands the
definition of differential privacy to handle randomized datasets. This gives us
the freedom to design randomized canaries. Second, we audit LiDP by trying to
distinguish between the model trained with canaries versus canaries
in the dataset, leaving one canary out. By drawing the canaries i.i.d., LiDP
can leverage the symmetry in the design and reuse each privately trained model
to run multiple statistical tests, one for each canary. Third, we introduce
novel confidence intervals that take advantage of the multiple test statistics
by adapting to the empirical higher-order correlations. Together, this new
recipe demonstrates significant improvements in sample complexity, both
theoretically and empirically, using synthetic and real data. Further, recent
advances in designing stronger canaries can be readily incorporated into the
new framework
The Student Movement Volume 107 Issue 23: So Long, Farewell: Students Step Into the Future
HUMANS
Advice to Younger Selves, Interviewed by: Anna Pak
Finals Stress Management, Gloria Oh
Farewell to Pastor Dwight Nelson, Student Movement Staff
What does AANHPI Heritage Month Mean?, Grace No
ARTS & ENTERTAINMENT
Currently: Reflecting on the Original Script, Solana Campbell
In Memory of K.V. Rathnam, Ysabelle Fernando
The Deal Premiers at Sonscreen Film Festival, Solana Campbell
Through Their Eyes: AANHPI Expression, Amelia Stefanescu
NEWS
A Night at AU: SASA Cultural Night, Solana Campbell
Interview with Professor Daniel Weber: Envision Magazine, Interviewed By: Brendan Oh
Interview with Gloria Oh: Transforming an Idea into Reality, Interviewed By: Brendan Oh
IDEAS
All at Once: How AANHPI Media Representation Took Over 2023, Bella Hamann
Raise A Glass to Freedom?, Terika Williams
The Gem Off the Back of a Lorry, Gabi Francisco
PULSE
Goodbye and Welcome: Letters to the Incoming and Outgoing Presidents, The Andrews University Student Association Senate
Honduras Mission, Interviewed By: Abraham Bravo
Last Words for the School Year, Elizabeth Dovich
In Summer: Professor Olaf Presents You the Ultimate Way to Spend Summer, Gloria Oh
LAST WORD
Take it from Me Part II, The Student Movement Staffhttps://digitalcommons.andrews.edu/sm-107/1022/thumbnail.jp
Learning to Generate Image Embeddings with User-level Differential Privacy
Small on-device models have been successfully trained with user-level
differential privacy (DP) for next word prediction and image classification
tasks in the past. However, existing methods can fail when directly applied to
learn embedding models using supervised training data with a large class space.
To achieve user-level DP for large image-to-embedding feature extractors, we
propose DP-FedEmb, a variant of federated learning algorithms with per-user
sensitivity control and noise addition, to train from user-partitioned data
centralized in the datacenter. DP-FedEmb combines virtual clients, partial
aggregation, private local fine-tuning, and public pretraining to achieve
strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding
models for faces, landmarks and natural species, and demonstrate its superior
utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD
and iNaturalist. We further illustrate it is possible to achieve strong
user-level DP guarantees of while controlling the utility drop
within 5%, when millions of users can participate in training
The Student Movement Volume 107 Issue 12: Revisiting The Dream : Students Celebrate MLK Day
HUMANS
Andrews Gaming Club, Interviewed by: Grace No
Meet Gio Lee, Interviewed by: Nora Martin
New Year, Happier Me, Gloria Oh
ARTS & ENTERTAINMENT
Art @ AU: Harrigan\u27s Gallery, Ysabelle Fernando
Currently: The Way of Water, Solana Campbell
Ode to 2022, Amelia Stefanescu
Where Do I Find God - Part I, Anonymous
NEWS
Honoring Martin Luther King Jr.\u27s Legacy, Brendan Oh
Is America Safe?: First Grader Shoots Teacher, Julia Randall
A House Divided: Current Issues Within the School of Architecture and Interior Design, Student Movement Editorial Staff
IDEAS
Harry & Meghan: Unpacking Royal Pains, Gabriela Francisco
A New Space for Creativity and Reaching Across Disciplinary Boundaries: The Inspiration Center, Peter Lyons, Anthony Bosman, Martin Hanna, Ryan Hayes, and Karin Thompson
PULSE
Our Food: Can They Cook It?, Melissa Moore
Should We Have Bible Classes in the Core Curriculum?, Wambui Karanja
What Comes First is a Question, Part II, Desmond H. Murray
LAST WORD
College in the Rearview Mirror, Scott Moncrieffhttps://digitalcommons.andrews.edu/sm-107/1011/thumbnail.jp
The Student Movement Volume 107 Issue 20: Andrews Students Spring Back from Break: Six More Weeks to Go
HUMANS
Meeting Dr. McCree, Interviewed by: Grace No
Interview with Dr. Luxton: Saying Goodbye, Interviewed by: Grace No
Spring Break Renovations & Relaxation, Caryn Cruz
ARTS & ENTERTAINMENT
Currently: The Last of Us, Solana Campbell
Spring Break Spotlight, Amelia Stefanescu
What Happened to Wang in the Costco Bathroom?, Nora Martin
NEWS
Experiences Living in Lamson Hall, Abigail Kim
Time is Ticking for TikTok, Brendan Oh
WEAAU x CFE Service Sabbath, Terika Williams
IDEAS
Inequality Drags on in Tennessee, Alexander J. Hess
On Value: True Crime and the Search for Meaning, Nora Martin
When Winds Change: The Legacy of President Luxton, Bella Hamann
PULSE
A Trip to the Museo , Chris Ngugi
AUSA Senates Holds Bon Appétit Forum, Neesa Richards
Speaking Up With Women Press Release, Nicholas C. Gunn
LAST WORD
A Week of Rest and Relaxation ... Almost, Grace Nohttps://digitalcommons.andrews.edu/sm-107/1019/thumbnail.jp
- …