DeepRec uses deep learning to recover signals from one-bit measurements.
problem Signal recovery from one-bit noisy measurements.
method Deep unfolding of inference optimization into deep neural network layers.
result DeepRec improves accuracy and computational efficiency.
A non-convex algorithm recovers low-rank matrices from one-bit labels efficiently.
problem Learning with one-bit labels in multi-label scenarios.
method Formulated as one-bit rank-one matrix sensing, developed an alternating power iteration algorithm.
result Achieves linear convergence and nearly optimal sampling complexity.
This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix $\Ab$ an…
Paper proposes a hybrid model-based and data-driven method for one-bit compressive variational autoencoding.
problem Designing efficient one-bit compressive sensing systems.
method Hybrid model-based and data-driven approach for one-bit compressive variational autoencoding.
result Significant improvement in one-bit compressive sensing compared to state-of-the-art methods.
Paper proposes a hybrid model-based and data-driven approach for one-bit compressive autoencoding.
problem Designing efficient one-bit compressive autoencoding models for complex systems.
method Hybrid model-based and data-driven methodology for one-bit sparse signal recovery.
result Significant improvement in one-bit compressive autoencoding compared to state-of-the-art algorithms.
This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measur…
Deep learning improves one-bit OFDM receiver performance.
problem One-bit quantization complicates accurate channel estimation and data detection in OFDM receivers.
method Developed deep neural networks for channel estimation and data detection, using a two-step training policy.
result Deep learning-based designs achieve lower BER than unquantized OFDM at moderate SNRs.
One-bit feedback suffices for a bandit problem's optimal strategy.
problem Optimal strategy for multi-armed bandit problem with limited feedback.
method Coding and decoding schemes for one-bit feedback to mimic full-reward feedback.
result Regret ratio approaches 1 with one-bit feedback.
Paper proposes a learning-based sparse Bayesian method for accurate off-grid DOA estimation.
problem One-bit off-grid direction of arrival (DOA) estimation in a single snapshot scenario.
method Formulated off-grid DOA estimation model, used Sparse Bayesian framework, proposed Learning-based Sparse Bayesian approach.
result Improved computational efficiency and accuracy in off-grid DOA estimation.
Paper proposes algorithms for robust 1-bit compressive sensing with nonconvex penalties.
problem Recovering sparse signals from one-bit measurements.
method Develops algorithms based on convex and nonconvex penalties, providing analytical solutions.
result Analytical solutions for several nonconvex penalties are found, making the recovery process faster and more efficient.
A deep learning autoencoder improves error correction for one-bit quantization.
problem Improving error correction for one-bit quantization in AWGN channels.
method Proposes a novel autoencoder-based coding scheme using turbo codes as implicit regularization.
result Empirically and theoretically shows nearly optimal performance of the proposed coding scheme.
Paper tackles one-bit compressed sensing using PAC learning theory.
problem One-bit compressed sensing problem.
method Formulated as PAC learning problem, uses VC-dimension and PAC learning theory.
result Consistent algorithm can recover k k k -sparse vectors with O ( k lg ( n / k ) ) O(k \lg (n/k)) O ( k lg ( n / k )) measurements. One-bit quantization improves inference speed for Random Features models.
problem Efficient inference on resource-constrained devices.
method Analysis of one-bit quantization in Random Features model.
result Asymptotically, quantizing weights except the last incurs no loss in generalization error.
One-bit proximal method speeds up nonconvex stochastic optimization.
problem Reducing communication in distributed SGD for large datasets.
method Stochastic proximal gradient method using one-bit per update.
result The method achieves convergence rates similar to uncompressed SGD.
AdaBoost improves binary classification in robust one-bit compressed sensing with adversarial errors.
problem Binary classification in robust one-bit compressed sensing with adversarial errors.
method AdaBoost and max- ℓ 1 \ell_1 ℓ 1 -margin-classifier approach, with convergence rates improved under certain feature conditions. result Improved convergence rates and explanation for harmless interpolating adversarial noise.
This paper improves support recovery in universal one-bit compressed sensing with fewer measurements.
problem Support recovery in universal one-bit compressed sensing.
method Developed algorithms to recover the support of sparse signals with a small number of false positives.
result Support recovery with i l d e O ( k 3 / 2 ) ilde{O}(k^{3/2}) i l d e O ( k 3/2 ) measurements, improving to i l d e O ( k ) ilde{O}(k) i l d e O ( k ) with known dynamic range. One-bit clustering method for two-component sub-Gaussian mixture models
problem Clustering in sub-Gaussian mixture models
method One-bit clustering using dithered quantization
result Decaying misclassification rate with exponential signal-to-noise ratio
One-bit quantization and sparsification improve multiclass classification with strong regularization.
problem Overfitting mislabeled data in multiclass classification.
method Linear regression with regularization and one-bit quantization/sparsification.
result Sparse and one-bit solutions perform almost as well as the optimal solution with f ( ⋅ ) = ∥ ⋅ ∥ 2 2 f(\cdot) = \|\cdot\|_2^2 f ( ⋅ ) = ∥ ⋅ ∥ 2 2 . Optimizes task offloading in fog networks with limited feedback.
problem Optimizing task offloading in fog networks with limited feedback.
method Multi-armed bandit framework and UCB-type algorithm.
result Maximizes long-term happiness metric in task offloading.
This paper improves support recovery in universal one-bit compressed sensing.
problem Support recovery in one-bit compressed sensing for sparse signals.
method Proposes approximate support recovery and superset recovery algorithms with polynomial-time complexity.
result Achieves improved support recovery with fewer measurements compared to existing methods.
The one-bit quantization is implemented by one single comparator that operates at low power and a high rate. Hence one-bit compressive sensing (1bit-CS) becomes attractive in signal processing. When measurements are corrupted by noise during signal acquisition and transmission, 1bit-CS is usually modeled as minimizing …
This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypot…
Estimating mean from one-bit samples of symmetric log-concave distributions.
problem Estimating the mean of a symmetric log-concave distribution with limited one-bit measurements.
method Analyzes mean squared error in three settings: centralized, adaptive, and distributed, with and without quantization.
result One round of adaptivity is sufficient to achieve optimal mean-square error in the adaptive setting.
Consider the recovery of an unknown signal x {x} x from quantized linear measurements. In the one-bit compressive sensing setting, one typically assumes that x {x} x is sparse, and that the measurements are of the form sign ( ⟨ a i , x ⟩ ) ∈ { ± 1 } \operatorname{sign}(\langle {a}_i, {x} \rangle) \in \{\pm1\} sign (⟨ a i , x ⟩) ∈ { ± 1 } . Since such measurements give no informati…
Paper shows interaction not necessary for optimal 1-bit mean estimation.
problem Optimal one-bit mean estimation with minimal interaction.
method Developed a fully non-adaptive protocol that avoids interaction.
result Achieved optimal sample complexity without interaction.
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a …
Improved privacy-preserving summation protocol with fewer messages.
problem Achieving efficient differential privacy in multi-party summation.
method Combining secure shuffling with Laplace mechanism in the shuffle model.
result Protocol with O ( 1 / ε ) O(1/ε) O ( 1/ ε ) error and O ( log ( n / δ ) ) O(\log(n/δ)) O ( log ( n / δ )) messages per party. New framework recovers sparse vectors via ReLU networks, achieving near-optimal statistical rate.
problem Robust one-bit compressed sensing with implicit sparsity constraints.
method Unconstrained empirical risk minimization on a ReLU generative network.
result Achieves a statistical rate of m = ~kn log(d/ε^2) for uniform recovery of any G(x0).
Paper tackles 1-bit compressed sensing, presenting efficient algorithm for sparse signal estimation.
problem Estimating sparse signals from binary measurements.
method Non-convex sparsity-constrained program with one-shot hard thresholding.
result Simple algorithm produces accurate signal approximation with high probability.
We consider the problem of completing a matrix with categorical-valued entries from partial observations. This is achieved by extending the formulation and theory of one-bit matrix completion. We recover a low-rank matrix X X X by maximizing the likelihood ratio with a constraint on the nuclear norm of X X X , and the obser…
Robustly estimates mean with quantized data and corruption.
problem Mean estimation under quantization and adversarial corruption.
method Constructs multivariate robust estimators in two settings.
result Optimal estimators up to logarithmic factors.
In this paper, we consider the matrix completion problem when the observations are one-bit measurements of some underlying matrix M, and in particular the observed samples consist only of ones and no zeros. This problem is motivated by modern applications such as recommender systems and social networks where only "like…
Paper proposes a CNN-based method for estimating intra frame bits and quality.
problem Efficient video delivery and bit allocation in video coding.
method Deep learning approach using CNNs trained on original frames and encoded distortions.
result Accurate estimation of intra frame bits and quality for better bit allocation.
Binary Iterative Hard Thresholding converges with optimal number of 1-bit measurements.
problem Recovering sparse signals from 1-bit compressed measurements.
method Binary Iterative Hard Thresholding (BIHT) algorithm.
result BIHT converges with only O(k/ε) measurements, optimal for recovery.
Study binary data classification with low costs.
problem Classifying data represented in binary form.
method Proposes a framework with low computation and resource costs.
result Illustrates and analyzes the utility of the proposed approach.
Optimal quantum change point detection without error.
problem Identifying a change point in a stream of identical quantum particles.
method Sequential local measurements with optimal performance bound.
result Optimal online detection strategy with one bit of memory.
This paper examines a general class of noisy matrix completion tasks where the goal is to estimate a matrix from observations obtained at a subset of its entries, each of which is subject to random noise or corruption. Our specific focus is on settings where the matrix to be estimated is well-approximated by a product …
Enhanced attacks quantify machine learning data leakage.
problem Quantifying how much machine learning models reveal about their training data.
method Hypothesis testing framework for membership inference attacks.
result New attacks achieve higher true positive rates with lower false positive rates.
This paper explains adversarial examples as feature redundancy abuse.
problem Understanding and mitigating adversarial examples in machine learning.
method Information-theoretic model to explain adversarial attacks.
result Feature redundancy is necessary for adversarial examples.
Two algorithms improve federated learning efficiency and resilience.
problem Scalability issues in federated learning due to communication, privacy, and Byzantine attacks.
method Proposes two algorithms, Ada-StoSign and β β β -StoSign, that compress gradients into bit vectors to reduce communication. result Ada-StoSign converges with a rate of O ( log T / T + 1 / M ) O(\log T/\sqrt{T} + 1/\sqrt{M}) O ( log T / T + 1/ M ) and outperforms existing methods. An efficient LDP protocol for QMLE with improved practicality and theoretical guarantees.
problem Difficult implementation of existing LDP QMLE for large-scale surveys.
method Developed an alternative LDP protocol without long waiting time, high communication cost, and derivative boundedness assumptions.
result Sufficient conditions for consistency and asymptotic normality of the protocol.
This paper examines fundamental error characteristics for a general class of matrix completion problems, where the matrix of interest is a product of two a priori unknown matrices, one of which is sparse, and the observations are noisy. Our main contributions come in the form of minimax lower bounds for the expected pe…
We extend the theory of matrix completion to the case where we make Poisson observations for a subset of entries of a low-rank matrix. We consider the (now) usual matrix recovery formulation through maximum likelihood with proper constraints on the matrix M M M , and establish theoretical upper and lower bounds on the rec…
Binary sequence correlation estimation fails but trinary data succeeds.
problem Estimating correlation in binary sequences generated by thresholding a hidden continuous sequence.
method Formal analysis and numerical experiments on likelihood maximization and discretization effects.
result Consistent estimation of correlation is possible with trinary data but not with binary data.
Improved SNNs with quantized activations outperform traditional networks.
problem Maintaining SotA accuracy in SNNs with limited bit precision.
method Interpolating between non-spiking and spiking regimes using signal processing tools.
result First hybrid SNN outperforms traditional RNNs in accuracy with reduced bit precision.
ARA combines aggregated RAPPOR and Tf-Idf estimation for centralized DP analysis.
problem Gap between local and central DP approaches in terms of data storage, analysis speed, and amount of data.
method Collects RAPPOR reports from multiple clients, pushes them to a Tf-Idf estimation model, and analyzes them for centralized DP.
result Successfully and efficiently analyzed major truth values from multiple clients.
Unified framework for nonconvex low-rank matrix estimation using gradient descent.
problem Estimating low-rank matrices in noisy and noiseless settings.
method Gradient descent algorithm applied to nonconvex optimization.
result Unified framework guarantees linear convergence to the unknown low-rank matrix with optimal statistical error.
Paper analyzes convergence of PAM method for low-rank factorization models.
problem Convergence analysis of PAM method with subspace correction for low-rank factorization models.
method Majorized proximal alternating minimization (PAM) method with subspace correction.
result Established full convergence of PAM method under KL property and column ℓ 2 , 0 \ell_{2,0} ℓ 2 , 0 -norm condition.