33 accepted papers
Intrinsic image decomposition (IID) of outdoor scenes is crucial for relighting, editing, and understanding large-scale environments, but progress has been limited by the lack of real-world datasets with reliable albedo and shading supervision. We introduce Olbedo, a large-scale aerial dataset for o
Anomaly-based detection is effective against evolving insider threats but still suffers from low precision. Current data processing can result in information loss, and models often struggle to distinguish between benign anomalies and actual threats. Both issues hinder precise detection. To address t…
Retrieval-Augmented Generation (RAG) compensates for the static knowledge limitations of Large Language Models (LLMs) by integrating external knowledge, producing responses with enhanced factual correctness and query-specific contextualization. However, it also introduces new attack surfaces such as
While Retrieval-Augmented Generation systems enhance Large Language Models by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We presentParetoRAG, an unsupervised framework that optimize
Capsule endoscopes, predominantly serving diagnostic functions, provide lucid internal imagery but are devoid of surgical or therapeutic capabilities. Consequently, despite lesion detection, physicians frequently resort to traditional endoscopic or open surgical procedures for treatment, resulting i…
We study a class of private learning problems in which the data is a join of private and public features. This is often the case in private personalization tasks such as recommendation or ad prediction, in which features related to individuals are sensitive, while features related to items (the movi…
Despite the success of StyleGAN in image synthesis, the images it synthesizes are not always perfect and the well-known truncation trick has become a standard post-processing technique for StyleGAN to synthesize high-quality images. Although effective, it has long been noted that the truncation tric…
Machine learning models exhibit two seemingly contradictory phenomena: training data memorization and various forms of forgetting. In memorization, models overfit specific training examples and become susceptible to privacy attacks. In forgetting, examples which appeared early in training are forgot…
We study the problem of multi-task learning under user-level differential privacy, in which n users contribute data to m tasks, each involving a subset of users. One important aspect of the problem, that can significantly impact quality, is the distribution skew among tasks. Tasks that have much few…
Surgical robots with variable stiffness can provide more stable configurations during minimally-invasive surgery.This letter presents a new design of modular lockable mechanism which can be used to change the stiffness of tendon-driven surgical robots. Locking and unlocking are simply actuated by pu
Flexible robots have been widely used in minimally-invasive surgery because of their dexterity and accuracy. However, the torsion of end-effector under bending state usually produces unnecessary concomitant motion of the robot arm. This letter proposes a novel design of <i>in-situ</i> torsionally-st
Flexible robots with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> torsion can be used in laryngeal endoscopic surgery which can maintain the position and approach vector of the end-effector during the operation. However, the inherent e
This paper proposes a new network for reconstructing multi-object point cloud. Different from previous networks which reconstruct multi-object point cloud as a whole, our network iteratively reconstructs each individual object point cloud from a frame of multi-object point cloud. To achieve this goa…
Magnetically-actuated flexible endoscopes (MAFE) have been well used in minimally-invasive surgery because they can be steered by a magnetic field thus more flexible than traditional endoscopes. Model-free and uncalibrated visual-feedback control makes it possible to manipulate MAFE with a magnetic…
In this paper, we revisit the problem of using in-distribution public data to improve the privacy/utility trade-offs for differentially private (DP) model training. (Here, public data refers to auxiliary data sets that have no privacy concerns.) We design a natural variant of DP mirror descent, wher…
Most existing bipedal robots can only move with either their wheels or feet. Even if some of them are capable of transforming between these two motions, they need to change their configuration dramatically. In order to truly combine the advantages of wheeled and footed robots, in this letter, an <it
Magnetic soft robots have attracted widespread attention for their untethered, remotely operated, and compliant deformation characteristics. Earlier work has demonstrated magnetic origami robots' diverse locomotion capabilities. This letter will focus on the motion generation and open-loop control o
We study personalization of supervised learning with user-level differential privacy. Consider a setting with many users, each of whom has a training data set drawn from their own distribution $P_i$. Assuming some shared structure among the problems $P_i$, can users collectively learn the shared str…
Accurate position feedback in a wide range is critical for medical microrobotics and robot-assisted examinations, such as colonoscopy, bronchoscopy and capsule endoscopy examination. Among the many modalities of positioning feedback, magnetic tracking is a preferable method due to the unique advanta…
We revisit the well-studied problem of differentially private empirical risk minimization (ERM). We show that for unconstrained convex generalized linear models (GLMs), one can obtain an excess empirical risk of $\tilde O\left(\sqrt{\rank}/\epsilon n\right)$, where $\rank$ is the rank of the feature…
Magnetically-actuated robotic catheter (MARC) has shown great potential in minimally-invasive surgery because they can be steered remotely and wirelessly. However, when driven by external permanent magnets (EPM), the MARC is subject to nonlinearly-coupled gravitational, magnetic, and elastic forces,
Small-scale robots have great potential in minimally invasive surgery (MIS). In this paper, we propose an untethered magnetic gripper with small scale and build a double-magnet model for it. The gripper is 4.3mm long and its maximum width is 4mm. It contains a spindle and two magnets, which can achi…
We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), requires \emph{privacy amplification by sampling or shuffling} to obtain the best privacy/accuracy/computation trade-offs…
We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Squares (ALS) method that achieves: i) (nearly) optimal sample complexity for matrix completion (in terms of number of ite…
Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer differential privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running training with a different optimizer, but using…
We present a novel framework for mesh reconstruction from unstructured point clouds by taking advantage of the learned visibility of the 3D points in the virtual views and traditional graph-cut based mesh generation. Specifically, we first propose a three-step network that explicitly employs depth c…
We revisit the problem of counting the number of distinct elements $\dist$ in a data stream $D$, over a domain $[u]$. We propose an $(\epsilon,\delta)$-differentially private algorithm that approximates $\dist$ within a factor of $(1\pm\gamma)$, and with additive error of $O(\sqrt{\ln(1/\delta)}/\ep…
Alignment of two point clouds is an essential problem in medical robotics and computer-assisted surgery. In this paper, we first formally formulate the generalized point cloud registration problem in a probabilistic manner. Specifically, not only positional but also the orientational information are…
The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or…
Flexible manipulator enables curvilinear accessibility through small incisions or natural orifices for minimally invasive surgery and diagnosis, which makes it a good choice for minimally invasive surgery. In order to control the robot precisely and safely, the real-time position and shape informati…
With the newly proposed privacy definition of Rényi Differential Privacy (RDP) in (Mironov, 2017), we re-examine the inherent privacy of releasing a single sample from a posterior distribution. We exploit the impact of the prior distribution in mitigating the influence of individual data points. In…
Flexible robotic manipulators have been widely used in minimally invasive surgery (MIS) and many other applications requiring closer inspection and operation. Although a variety of manipulators enabled by different mechanism have been developed, few of them can preserve softness, thinness, and decen
We consider learning from data of variable quality that may be obtained from different heterogeneous sources. Addressing learning from heterogeneous data in its full generality is a challenging problem. In this paper, we adopt instead a model in which data is observed through heterogeneous noise, wh…