9 accepted papers
The accuracy of path following is crucial for collision-free navigation of hyper-redundant robots, especially in narrow environments. However, the existing path following methods only consider the deviations of joints and the end effector, while ignoring the deviations of the robot body. In this let
Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones. While well studied in the realizable case, the statistical properties of weak-to-strong learning remain less understood in the agnostic setting, where there are no assumptions on the distributio…
Recently, neural implicit representations have been applied in the mapping process of simultaneous localization and mapping (SLAM), accompanied by less storage overhead and continuous representation. Nevertheless, related methods use a single neural network to represent the whole scene, resulting in
We study the problem of learning mixtures of linear classifiers under Gaussian covariates. Given sample access to a mixture of $r$ distributions on $\mathbb{R}^n$ of the form $(\mathbf{x},y_{\ell})$, $\ell \in [r]$, where $\mathbf{x}\sim\mathcal{N}(0,\mathbf{I}_n)$ and $y_\ell=\mathrm{sign}(\langle…
We study the complexity of Non-Gaussian Component Analysis (NGCA) in the Statistical Query (SQ) model. Prior work developed a methodology to prove SQ lower bounds for NGCA that have been applicable to a wide range of contexts. In particular, it was known that for any univariate distribution $A$ sati…
We present new insights and a novel paradigm for learning implicit neural representations (INR) of shapes. In particular, we shed light on the popular eikonal loss used for imposing a signed distance function constraint in INR. We show analytically that as the representation power of the network inc…
Back exoskeletons can reduce low-back pain and injury for workers engaged in manual handling operations. However, conventional back exoskeletons are incompatible with human trunk kinematics, arousing uncomfortable human-exoskeleton interaction and limiting natural human movement. This article propos
We study the problem of PAC learning a single neuron in the presence of Massart noise. Specifically, for a known activation function $f: \mathbb{R}\to \mathbb{R}$, the learner is given access to labeled examples $(\mathbf{x}, y) \in \mathbb{R}^d \times \mathbb{R}$, where the marginal distribution of…
We empirically demonstrate numerical instabilities in training standard deep networks with SGD. Specifically, we show numerical error (on the order of the smallest floating point bit) induced from floating point arithmetic in training deep nets can be amplified significantly and result in significan…