We expose (without proofs) a unified computational approach to integrable structures (including recursion, Hamiltonian, and symplectic operators) based on geometrical theory of partial differential equations. We adopt a coordinate based approach and aim to provide a tutorial to the computations.
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Quantum computing speeds up linear regression training.
problem Reducing training time for machine learning models.
method Formulated regression problem as QUBO, used D-Wave 2000Q for adiabatic optimization.
result Quantum approach achieves up to 2.8x speedup on larger datasets.
Hybrid approach reduces computation time and decoding complexity.
problem Straggling servers in distributed computing.
method Coded partial gradient computation (CPGC) that balances gradient accuracy and completion time.
result Reduces both computation time and decoding complexity.
Randomized exploration methods are more statistically efficient than optimistic methods in reinforcement learning.
problem Comparing and contrasting optimistic and randomized exploration methods in reinforcement learning.
method Analytic examples to compare optimistic and randomized approaches.
result Randomized approaches are more statistically efficient than optimistic approaches.
Develops robust methods for computer vision representation learning.
problem Noise and outliers frustrate unsupervised learning of latent representations.
method New robust PCA and spectral clustering methods.
result Superior performance on real-world test sets.
New solar tracking system uses computer vision and low-cost hardware.
problem Current solar tracking systems are expensive and have operational issues.
method Low-cost hardware, computer vision, and deep learning.
result The new system shows great potential and can improve solar tracking performance.
New approach models computer network activity as mixtures of sources.
problem Malicious activity detection in computer networks using standard algorithms is ineffective.
method Source separation approach to model short-term dynamics of computer network activity.
result Qualitative and quantitative experiments validate the approach.
Approaches for big data regression using sufficient statistics.
problem Challenges in computing closed-form solutions for large datasets.
method Multiple learning approaches using sufficient statistics arrays.
result Achieves closed-form solutions for multiple models at half the training time of traditional methods.
Faster neural network predictions for aerodynamics simulations.
problem Challenges in simulating complex systems like jets and spacecraft due to computational resources and time.
method A novel model-free approach using a cluster network architecture to reformulate and expand pre-computed datasets.
result Nearly as accurate as state-of-the-art model-based approximations, an order of magnitude faster, and easier to apply.
We present a braid-theoretic approach to combinatorially computing knot Floer homology. To a knot or link K, which is braided about the standard disk open book decomposition for (S^3,ξ_std), we associate a corresponding multi-pointed nice Heegaard diagram. We then describe an explicit algorithm for computing the associ…
A new approach for efficient data compression in split DNN computing.
problem Optimizing data compression for DNN models split between mobile devices and edge servers.
method Systematic design and training of bottleneck units that can be inserted at the split point.
result Achieves excellent rate-distortion performance with minimal compute and storage overhead.
Learning codes for non-linear computations improves resilience in machine learning.
problem Resilience of machine learning models in the face of unavailability.
method Learning neural network architectures to design codes for non-linear computations.
result Learned codes can reconstruct up to 98% of unavailable predictions from neural networks.
A DRL approach optimizes computation offloading in MEC systems for mobile users.
problem Optimizing computation offloading in MEC systems with mobile users and stochastic task arrivals.
method Deep Deterministic Policy Gradient (DDPG) for decentralized dynamic computation offloading.
result The DDPG-based strategy outperforms conventional strategies in terms of computation cost and power-delay tradeoff.
GP3 framework efficiently analyzes Gaussian processes on GPUs.
problem Certifiable safety in machine learning applications.
method GP3 framework using interval analysis and multi-resolution sampling on GPUs.
result Efficient analysis of Gaussian processes with certifiable safety.
ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learningcs.LG
ATA optimizes task allocation in distributed machine learning.
problem Greedy task allocation leads to inefficiencies in distributed machine learning.
method Adaptive Task Allocation (ATA) adapts to unknown computation time distributions.
result ATA identifies optimal task allocation without prior knowledge of computation times.
A new MCMC method combines low and high-fidelity models to reduce computation.
problem Inefficient computation of expensive target densities in scientific applications.
method Pseudo-marginal MCMC approach using a telescoping series of low-fidelity models.
result Asymptotically exact multi-fidelity MCMC algorithms for reduced computational cost.
The classical approach to inverse problems is based on the optimization of a misfit function. Despite its computational appeal, such an approach suffers from many shortcomings, e.g., non-uniqueness of solutions, modeling prior knowledge, etc. The Bayesian formalism to inverse problems avoids most of the difficulties en…
Enhances neural network dynamics to boost computational capacity.
problem Improving computational capacity of neural networks.
method Introducing Phase Transition Adaptation to drive system dynamics towards edge of stability.
result Consistently achieves enhancement in computational capacity over multiple datasets.
A new method computes Greeks for multi-asset options using tensor trains and Fourier transforms.
problem Efficient computation of Greeks for multi-asset options with high accuracy and low sample complexity.
method Tensor train (TT) representations of Fourier-based pricing functions, combined with numerical differentiation or analytical approaches.
result Significant speed-ups of up to over Monte Carlo simulations while maintaining comparable accuracy.
Develops a framework for decision-making abstractions under computational limitations.
problem Decision-making by agents with limited computational resources.
method Information-theoretic signal compression and optimization problem formulation.
result Generates a hierarchy of abstractions for a non-trivial environment.
New method computes transverse knot invariant using braids.
problem Compute transverse invariant of knots and links.
method Define new combinatorial complex to compute knot Floer homology.
result Identify BRAID invariant of transverse knots and links.
Optimizes train schedules and maintenance using CP and QA.
problem Optimizing train schedules and maintenance considering constraints.
method Used Constraint Programming and Quantum Annealing to model and solve the problem.
result Both CP and QA approaches produce comparable results on real quantum computers.
Improves statistical and computational efficiency in sparse regression.
problem Balancing statistical accuracy and computational efficiency in data analysis.
method Proposes a unified approach to sparse and group-sparse regression.
result Shows improved performance in both statistical and computational aspects.
Quantum machine learning: Adiabatic quantum SVM outperforms classical methods.
problem Training support vector machines efficiently on large datasets.
method Adiabatic quantum computing for SVM training.
result Quantum approach outperforms classical methods in accuracy and scalability.
Generalised Degrees of Freedom (GDF), as defined by Ye (1998 JASA 93:120-131), represent the sensitivity of model fits to perturbations of the data. As such they can be computed for any statistical model, making it possible, in principle, to derive the number of parameters in machine-learning approaches. Defined origin…
Computational method identifies 30 major complaints in GEICO reviews.
problem Analyzing large numbers of online complaints is challenging.
method Topic modeling approach to reveal latent semantic of complaints.
result 30 major complaints in four categories identified from GEICO reviews.
We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling leve…
Model-data-driven constitutive responses: application to a multiscale computational frameworkstat.ML
A hybrid method combines model-based and data-driven approaches for multiscale constitutive responses.
problem High computational costs and inaccuracies in nonlinear multiscale methods.
method Hybrid methodology combining model-based constitutive laws, data-driven corrections, and computational multiscale approaches.
result Model-data-driven approach improves macroscale simulations with similar accuracy and computational cost.
A machine learning approach to compute Black-Scholes prices with uncertain volatility.
problem Approximating financial markets with continuous-time models like Black-Scholes when data is discrete.
method Generalized Polynomial Chaos (gPC) method combined with a machine learning technique called Bi-Fidelity.
result Efficient numerical method to quantify uncertainty in derivative pricing.
Paper proposes a new approach to improve EEG-based BCIs.
problem Improving learning performance for new subjects with minimal data.
method Aligns EEG trials in Euclidean space to make them more similar.
result Outperforms state-of-the-art approaches in offline and online experiments.
Quantum reservoir computing improves volatility forecasting.
problem Forecasting realized volatility in finance.
method Quantum reservoir computing with Ising Hamiltonian and feature selection.
result Quantum reservoir computing outperforms benchmarks in volatility forecasting.
Paper studies estimating network properties with missing data using SRL and GNN.
problem Estimating aggregate properties in networks with missing data attributes.
method Comparative study of SRL and GNN approaches for inferring missing attributes and estimating aggregate properties.
result SRL-based approaches tend to outperform GNN-based approaches in estimating aggregate properties and predictive accuracy.
We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like minimum covaria…
Develops a fast Bayesian optimisation method that reduces computational overhead.
problem Computational inefficiency and restrictive kernel choices in information-theoretic Bayesian optimisation.
method FITBO method that avoids sampling the global minimizer and allows for more flexible kernel choices.
result Demonstrates that FITBO inherits performance from information-theoretic Bayesian optimisation but is faster.
Method for computing Khovanov homology of tangles.
problem Limited explicit computational studies of Khovanov homology for tangles.
method Arc reduction approach to compute Khovanov homology.
result Derived and computed Poincaré polynomials for simple and complex tangles.
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Modelstat.ME
Novel approach to compute hazard ratios from observational studies using SCMs and backdoor adjustment.
problem Identifying causal relationships from observational data using hazard ratios.
method Backdoor adjustment through structural causal models (SCMs) and do-calculus.
result Novel approach for computing hazard ratios from observational studies.
A neural network speeds up computation of Wasserstein barycenters by 60x.
problem Computing Wasserstein barycenters is computationally demanding.
method Trained a deep convolutional neural network to compute Wasserstein barycenters.
result Computational times reduced from milliseconds to seconds.
Paper introduces a new deep-learning method for quantum mechanics.
problem Simulating time-evolving Schrödinger equations efficiently.
method Generative diffusion models and stochastic mechanics.
result Significantly lower computational complexity compared to existing methods.
New method speeds up knot computations in 3D.
problem Computational complexity in knot theory.
method 3D representation of knots for faster computation.
result Savings in computational complexity for knot invariants.
PARSEC uses a probabilistic approach to reduce memory usage in neural architecture search.
problem Efficiently search over large and complex neural architectures with reduced memory usage.
method Probabilistic sampling to learn a distribution over high-performing architectures, enabling transfer learning.
result Our approach outperforms state-of-the-art methods with significantly less computational cost.
Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flowsstat.ML
Efficiently computes optimal transport maps and Wasserstein barycenters using conditional normalizing flows.
problem Computing optimal transport maps and Wasserstein barycenters in high-dimensional spaces.
method Uses conditional normalizing flows to approximate distributions and solve the primal problem.
result Shows computational feasibility for hundreds of input distributions and yields accurate results.
New methods improve accuracy in predicting complex systems.
problem Improving accuracy in predicting complex physical systems from simulators.
method Proposes two new methods of design approaches that sequentially select input settings.
result Demonstrates effectiveness of the proposed methods through numerical examples.
Reinforcement learning optimizes neural network execution costs.
problem Minimizing execution cost of neural network computation graphs.
method Offline training of a reinforcement learning optimizer for generalization to unseen graphs.
result Significant improvements in execution time and memory usage compared to baselines.
Novel approach for large genus intersection number asymptotics.
problem Computing intersection numbers in large genus.
method Resurgent analysis of n-point functions with quantum curve.
result Extension of Aggarwal's results and new -spin and Theta-class intersection numbers.
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Baye…
Chinchilla Approach 2 biases neural scaling law estimates, leading to unnecessary compute costs.
problem Systematic biases in Chinchilla Approach 2's parabolic fits of neural scaling laws.
method Analyzes three sources of error: IsoFLOP sampling grid width, uncentered sampling, and loss surface asymmetry.
result Chinchilla Approach 3 largely eliminates these biases, offering a more convenient or scalable alternative.
We simplify deep learning convergence analysis using basic math.
problem Ensuring reliable convergence of optimization algorithms in deep networks.
method Elementary arguments and computations to estimate smoothness constants.
result Systematic computation of smoothness constants for first-order algorithms.
Paper introduces a new method for Gaussian Processes that improves prediction and hyper-parameter optimization.
problem Efficiently predicting unknown functions and optimizing hyper-parameters in Gaussian Processes.
method Sequential randomized low-rank matrix factorization for incremental predictions and hyper-parameter optimization.
result The proposed method outperforms existing approaches in terms of accuracy and computational efficiency.