Paper proposes CNN-LSTM for WiFi indoor localization.
problem Classical WiFi localization methods using only spatial data.
method Combines CNN and LSTM for CSI feature extraction.
result Achieves 2.5~m average localization error with 80% under 4~m.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
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48 results for “Single Access Point”
New method learns cell trajectories from multiple snapshots.
problem Inferring cell trajectories from limited, single-time-point data.
method Multi-marginal Schrödinger Bridges with iterative reference refinement.
result Effective in capturing long-term dependencies and learning from multiple time points.
To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. We employ the proposed framework …
A new method for federated learning with only positive labels.
problem Learning with only positive labels leads to poor classifier performance.
method Federated Averaging with Spreadout (FedAwS) framework.
result FedAwS can almost match the performance of conventional learning with negative labels.
Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (…
Refereed Learningstat.ML
Protocol assesses better model among two opaque models with minimal interaction.
problem Learning from two provers, one honest, the other potentially deceptive.
method Formulated and implemented a protocol for assessing better models among two opaque models.
result Protocol achieves near-optimal performance with minimal interaction and query access.
The paper develops efficient algorithms for sampling from random spanning trees and determinantal point processes.
problem Sampling from strongly Rayleigh distributions efficiently.
method Optimal sublinear sampling algorithms for random spanning trees and determinantal point processes.
result Achieves optimal sublinear sampling for strongly Rayleigh distributions.
Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience …
Study on collaborative vs. non-collaborative online and bandit convex optimization.
problem Minimizing average regret in distributed online and bandit convex optimization.
method Analyzes the impact of collaboration in adaptive and zeroth-order feedback settings.
result Collaboration is beneficial in high-dimensional federated online optimization with limited feedback.
Visual Spatial Learning: Single-Field Spatial Interpolation Using Convolutional Neural Networksstat.ML
CNNs predict spatial fields from sparse data.
problem Predicting complete spatial fields from limited observations.
method Convolutional Neural Networks (CNNs) trained on a single partially observed field.
result CNNs can flexibly capture local spatial patterns without explicit covariance modeling.
C-Learning estimates reachability over time to solve multi-goal tasks.
problem Multi-goal reaching challenges in reinforcement learning.
method Cumulative accessibility functions and recurrence relations.
result Optimal cumulative accessibility functions are monotonic in horizon.
Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the envir…
New method for selecting data points in deep learning models.
problem Selecting data points for overparameterized deep learning models.
method Proposes a new experimental design strategy for overparameterized regression and interpolation.
result Demonstrates the effectiveness of the new method in single shot deep active learning.
Theorem converse to Jordan's curve theorem says that {\it if a compact set has two complementary domains in , from each of which it is at every point accessible, it is a simple closed curve}. We show that the requirement of this theorem that {\it all} points of were accessible from {\it both} complementa…
New algorithms reduce complexity for solving nonconvex optimization problems with stochastic objectives and constraints.
problem Solving nonconvex optimization problems with stochastic objectives and constraints.
method Single-loop quadratic penalty and augmented Lagrangian algorithms with variance reduction techniques.
result Achieved best-known complexity guarantees for solving nonconvex optimization problems with stochastic objectives and constraints.
Single model learns physics from diverse data.
problem Lack of universal physics models for diverse applications.
method General Physics Transformer (GPhyT) trained on diverse physics data.
result Single model achieves superior performance across multiple physics domains.
Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diag…
Let be a polynomial of degree with a Cremer point and no repelling or parabolic periodic bi-accessible points. We show that there are two types of such Julia sets . The \emph{red dwarf} are nowhere connected im kleinen and such that the intersection of all impressions of external angles is a cont…
Study of tree automorphisms via arc-stabilizers.
problem Understanding automorphisms of -trees.
method Analyzing actions over arc-stabilizers.
result Point-stabilizers are finitely generated.
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguitycs.LG
Study identifies latent variables and causal relationships from multiple environments.
problem Identify latent variables and causal relationships from multiple environments.
method Proposes algorithm LiNGCReL for identifying causal graph up to surrounded-node ambiguity.
result Identifies latent variables up to surrounded-node ambiguity (SNA) in linear causal models.
Query access significantly speeds up learning Multi-Index Models under Gaussian distribution.
problem Agnostically learning Multi-Index Models (MIMs) under Gaussian distribution.
method Query access for MIMs with complexity under standard regularity assumptions.
result Query access gives significant runtime improvements over random examples for agnostically learning MIMs.
SnapMMD forecasts cell differentiation outcomes from snapshot data.
problem Forecasting cell differentiation outcomes from limited snapshot data.
method SnapMMD learns dynamics by directly fitting the joint distribution of state measurements and observation time with MMD loss, allowing for unknown and state-dependent volatilities.
result SnapMMD delivers accurate forecasts and an R2-style statistic for diagnosing fit.
Estimates joint causal effects using single-variable interventions on nonlinear models.
problem Estimating joint causal effects from single-variable interventions.
method Identifiability result and practical estimator for decomposing causal effects.
result Joint effects can be inferred without joint interventional data for nonlinear additive models.
Study shows priority access in ELA auctions is less valuable due to volatility risks.
problem Impact of volatility on time-based transaction ordering policies.
method Single-round model with risk-averse bidders, tested using ETH price data.
result Priority access is discounted due to volatility and risk aversion.
We consider the task of opportunistic channel access in a primary system composed of independent Gilbert-Elliot channels where the secondary (or opportunistic) user does not dispose of a priori information regarding the statistical characteristics of the system. It is shown that this problem may be cast into the framew…
Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, and few organizations and select highly trained professionals have the w…
We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points. Instead, we can only ask an oracle whether the distance between two points and is smaller than the distance between the points an…
Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a…
New method speeds up distributed linear regression.
problem Efficiently solve distributed linear regression problems.
method Iteratively Pre-conditioned Stochastic Gradient Descent (IPSG)
result Converges linearly in expectation to the solution.
In this paper we solve the following problem in the affirmative: Let be a continuum in the plane $\complex$ and suppose that $h:Z\times [0,1]\to\complex$ is an isotopy starting at the identity. Can be extended to an isotopy of the plane? We will provide a new characterization of an accessible point in a planar …
In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solutio…
The Oracle of DLphics.LG
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of labelled training data, our methodology is capable of predicting the labels of the test data almost always even if the training data is entir…
A new method reduces the complexity of decentralized optimization.
problem Decentralized stochastic non-convex optimization over a network.
method GT-HSGD, a hybrid variance-reduced method.
result Achieves an oracle complexity of O(n^(-1)ε^(-3)) for small ε.
CTM improves diffusion model sampling quality with efficient ODE traversal.
problem Lack of natural trade-off between sample quality and speed in consistency models.
method CTM trains a neural network to output scores and traverse ODE trajectories efficiently.
result CTM achieves state-of-the-art FIDs and improves sample quality with increased computational budget.
We show that for an arbitrarily given closed Riemannian manifold admitting a point with a single cut point, every closed Riemannian manifold admitting a point with a single cut point is diffeomorphic to if the radial curvatures of at are sufficiently close in the sense of -n…
A method to generate multi-label data from single positive annotations.
problem Generating multi-label datasets is costly and impractical.
method Single-to-multi-label (S2M) sampling using Markov chain Monte Carlo.
result S2M sampling enables high-quality multi-label data with minimal annotation cost.
Estimates effects of multiple interventions with hidden confounders using single-variable interventions.
problem Estimating effects of multiple interventions in the presence of hidden confounders.
method Identifiability under nonlinear structural causal model with additive Gaussian noise; pooling and joint likelihood maximization.
result Proven identifiability and superior performance compared to baseline.
The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose. However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a …
Improved Frank-Wolfe algorithm for constrained convex optimization with nearest extreme point oracle.
problem Constrained smooth convex minimization with limited linear optimization oracle access.
method Frank-Wolfe algorithm with nearest extreme point oracle.
result Improved complexity bounds for specific feasible sets, including linear convergence for polytopes.
PEARL uses AI to replicate private equity performance with liquid assets.
problem Lack of access to private equity due to high costs and complexity.
method Combines AI with liquid assets, incorporating asymmetry for better performance.
result Model outperforms liquid proxies and aligns with private equity benchmarks.
Method constructs uniformly valid prediction sets across multiple distributions.
problem Uniformly valid prediction sets across multiple distributions.
method Max-p aggregation scheme and optimization programs.
result Optimal and efficient prediction sets for multiple distributions.
Fairness audits fail under missing protected labels, especially at zero access.
problem Understanding the reliability of fairness audits with incomplete protected-label data.
method Introduced a seed-calibrated stress test to separate missingness effects from seed-to-seed movement.
result Missing protected labels do not significantly alter fairness mitigation methods, but they can lead to harmful intersectional outcomes.
In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F…
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimationcs.LG
Many machine learning approaches are characterized by information constraints on how they interact with the training data. These include memory and sequential access constraints (e.g. fast first-order methods to solve stochastic optimization problems); communication constraints (e.g. distributed learning); partial acce…
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entir…
Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples. However, while most prior works analyze the effects of perturbing every pixel of every frame assuming white-box policy access, in this paper we take a more restrictive view towards adversary generation - with t…
Lecture notes on mean curvature flow for beginners.
problem Existence and uniqueness of mean curvature flow solutions.
method Conditional existence and weak-strong uniqueness theory.
result Accessible introduction for graduate students.
Neural networks offer high-accuracy solutions to a range of problems, but are costly to run in production systems because of computational and memory requirements during a forward pass. Given a trained network, we propose a techique called Deep Learning Approximation to build a faster network in a tiny fraction of the …