Neural Information Processing Systems http://nips.cc/

Different from regularization approaches, our method achieves a well-balanced tradeoff between accuracy and speed. A numerical comparison on various distributions with existing algorithms exhibits the computational advantages of our approach.

-- Sparse programming is an important tool in robotics, for example in real-time sparse inverse kinematic control with a minimum number of active joints, or autonomous Cartesian goal selection. However, current approaches are limited to real-time control without consideration of the underlying non-linear problem. This prevents the application to non-linear problems like inverse kinematic planning while the robot simultaneously and autonomously chooses from a set of potential end-effector goal positions. Instead, kinematic reachability approximations are used while the robot's whole body motion is considered separately. This can lead to infeasible goals. Furthermore, the sparse constraints are not prioritized for intuitive problem formulation. Lastly, the computational effort of standard sparse solvers is cubically dependent on the number of constraints which prevents real-time control in the presence of a large number of possible goals. In this work, we develop a non-linear solver for sparse hierarchical non-linear programming. Sparse non-linear constraints for autonomous goal selection can be formulated on any priority level, which enables hierarchical decision making capabilities. Sparse programming is an important tool in robotics due to the inherent redundancy both in the robot's kinematics and motion planning. Robots typically possess more degrees of freedom than are necessary to fulfill a given kinematic task.

This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $\ell$p-norm constraints. We introduce a framework that dynamically adjusts fusion weights based on local data characteristics, addressing fundamental challenges in ensemble-based anomaly detection. Our method incorporates an interior-point optimization technique that significantly improves computational efficiency compared to traditional Frank-Wolfe approaches, achieving up to 19-fold speed improvements in complex scenarios. The framework is extensively evaluated on standard UCI benchmark datasets and specialized temporal sequence datasets, demonstrating superior performance across diverse anomaly types. Statistical validation through Skillings-Mack tests confirms our method's significant advantages over existing approaches, with consistent top rankings in both pure and non-pure learning scenarios. The framework's ability to adapt to local data patterns while maintaining computational efficiency makes it particularly valuable for real-time applications where rapid and accurate anomaly detection is crucial.

Recent research in communications-aware robotics has been propelled by advancements in 5G and emerging 6G technologies. This field now includes the integration of Multi-Rotor Aerial Vehicles (MRAVs) into cellular networks, with a specific focus on under-actuated MRAVs. These vehicles face challenges in independently controlling position and orientation due to their limited control inputs, which adversely affects communication metrics such as Signal-to-Noise Ratio. In response, a newer class of omnidirectional MRAVs has been developed, which can control both position and orientation simultaneously by tilting their propellers. However, exploiting this capability fully requires sophisticated motion planning techniques. This paper presents a novel application of omnidirectional MRAVs designed to enhance communication security and thwart eavesdropping. It proposes a strategy where one MRAV functions as an aerial Base Station, while another acts as a friendly jammer to secure communications. This study is the first to apply such a strategy to MRAVs in scenarios involving eavesdroppers.

An interior-point algorithm framework is proposed, analyzed, and tested for solving nonlinearly constrained continuous optimization problems. The main setting of interest is when the objective and constraint functions may be nonlinear and/or nonconvex, and when constraint values and derivatives are tractable to compute, but objective function values and derivatives can only be estimated. The algorithm is intended primarily for a setting that is similar for stochastic-gradient methods for unconstrained optimization, namely, the setting when stochastic-gradient estimates are available and employed in place of gradients of the objective, and when no objective function values (nor estimates of them) are employed. This is achieved by the interior-point framework having a single-loop structure rather than the nested-loop structure that is typical of contemporary interior-point methods. For completeness, convergence guarantees for the framework are provided both for deterministic and stochastic settings. Numerical experiments show that the algorithm yields good performance on a large set of test problems.

Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensus-optimization framework and demonstrate their superior performance over state of the art. We show empirically that in a large-scale voter-preference modeling problem our algorithms scale linearly in the number of dependencies and constraints.

While compressive sensing (CS) has been one of the most vibrant research fields in the past few years, most development only applies to linear models. This limits its application in many areas where CS could make a difference. This paper presents a novel extension of CS to the phase retrieval problem, where intensity measurements of a linear system are used to recover a complex sparse signal. We propose a novel solution using a lifting technique - CPRL, which relaxes the NP-hard problem to a nonsmooth semidefinite program. Our analysis shows that CPRL inherits many desirable properties from CS, such as guarantees for exact recovery. We further provide scalable numerical solvers to accelerate its implementation.

The interior-point methodology is one of the most effective approaches for solving continuous constrained optimization problems. In the context of (deterministic) derivative-based algorithmic strategies, interiorpoint methods offer convergence guarantees from remote starting points [11, 21, 27], and in both convex and nonconvex settings such algorithms can offer good worst-case iteration complexity properties [7, 21]. Furthermore, many of the most popular software packages for solving large-scale continuous optimization problems are based on interior-point methods [1, 11, 24, 25, 26, 27], and these have been used to great effect for many years. Despite the extensive literature on theoretical and practical benefits of interior-point methods in the context of (deterministic) derivative-based algorithms for solving (non)convex optimization problems, to the best of our knowledge there has not yet been one that has been shown rigorously to offer convergence guarantees when neither function nor derivative evaluations are available, and instead only stochastic gradient estimates are employed.

Networked data, in which every training example involves two objects and may share some common objects with others, is used in many machine learning tasks such as learning to rank and link prediction. A challenge of learning from networked examples is that target values are not known for some pairs of objects. In this case, neither the classical i.i.d. assumption nor techniques based on complete U-statistics can be used. Most existing theoretical results of this problem only deal with the classical empirical risk minimization (ERM) principle that always weights every example equally, but this strategy leads to unsatisfactory bounds. We consider general weighted ERM and show new universal risk bounds for this problem. These new bounds naturally define an optimization problem which leads to appropriate weights for networked examples. Though this optimization problem is not convex in general, we devise a new fully polynomial-time approximation scheme (FPTAS) to solve it.