Paper explores tradeoffs in classification using tensor subspaces.
problem Supervised classification with sample, computation, and storage complexities.
method Use of tensor subspaces, particularly hierarchical Kronecker structured subspaces.
result Hierarchical Kronecker structured subspaces improve classification tradeoffs.
Study shows a tradeoff between sample complexity and computational efficiency for learning halfspaces with random noise.
problem PAC learning γ-margin halfspaces with Random Classification Noise.
method Established an information-computation tradeoff and provided a simple efficient algorithm with sample complexity O(1/(γ^2 ε^2)). Also, proved lower bounds for SQ algorithms and low-degree polynomial tests.
result Inherent gap between sample complexity and computational efficiency for learning halfspaces with random noise.
Study the tradeoff between signal distortion and human perception over finite channels.
problem Characterize the distortion-perception tradeoff for finite channels with arbitrary metrics.
method Solve linear programming problems to compute the distortion-perception function and optimal reconstructions.
result DP function is piecewise linear in the perception index.
The stochastic block model guides community detection limits and algorithms.
problem Understanding community detection limits in stochastic block models.
method Information-theoretic and computational tradeoffs, algorithms derivation.
result Phase transitions and mutual information tradeoffs for community detection.
A method to reduce computation by dynamically sacrificing accuracy in deep neural networks.
problem Balancing computational effort and classification accuracy in deep neural networks.
method A cascade of deep neural networks with dynamically set confidence thresholds based on softmax outputs.
result Reduces 15%-50% in MAC operations with a 1% accuracy degradation.
The paper studies adversarial training for linear regression models.
problem Understanding the tradeoffs between robust and standard accuracy in adversarial training.
method Characterizes the fundamental tradeoff and specific adversarial training approach for linear regression with Gaussian features.
result Precise characterization of the standard and robust accuracy tradeoff in high-dimensional settings.
Faced with massive data, is it possible to trade off (statistical) risk, and (computational) space and time? This challenge lies at the heart of large-scale machine learning. Using k-means clustering as a prototypical unsupervised learning problem, we show how we can strategically summarize the data (control space) in …
Stochastic momentum methods trade compute efficiency for serial runtime.
problem Stochastic momentum methods trade compute efficiency for serial runtime.
method Stochastic HB and ASGD for consistent linear regression with Gaussian covariates.
result HB preserves SGD-level CE over a larger batch-size window, allowing larger batches to reduce serial runtime until HB reaches its deterministic accelerated scale.
Paper solves NGCA for discrete distributions using LLL method.
problem Learning hidden non-Gaussian components in discrete distributions.
method Utilizes LLL lattice basis reduction method.
result Sample and computationally efficient algorithm for NGCA in discrete distributions.
Paper optimizes distributed learning by reducing gradient computation time.
problem Efficiently compute gradients in distributed learning tasks.
method Recursive polynomial constructions for coding across data subsets and vector components.
result Optimal tradeoff between computation load, straggler tolerance, and communication cost achieved.
This work explores feature learning tradeoffs in neural networks.
problem Resource tradeoffs in neural feature learning.
method Theoretical and experimental investigation of offline sparse parity learning.
result Width improves sample efficiency in sparse feature learning.
New method learns latent structures for deep NLP models without tradeoffs.
problem Joint learning of latent structures and downstream predictors with end-to-end differentiability.
method SparseMAP inference for joint learning of latent structures and downstream predictors.
result First method to enable unrestricted dynamic computation graph construction from global latent structure while maintaining differentiability.
The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful. We propose a framework that provides an explicit opportunity for practitioners to specify how much statistical ris…
New OLO algorithms use Stein's method for better performance tradeoffs.
problem Achieving optimal tradeoffs in adversarial online linear optimization.
method Operationalizing Stein's method for computationally efficient OLO algorithms.
result Additively sharp upper bounds on regret and total loss.
The bias-variance tradeoff doesn't always apply in neural networks, contradicting textbook claims.
problem The bias-variance tradeoff is not universally applicable in neural networks, contradicting textbook teachings.
method Extensive experiments and analysis on neural networks, revisiting Geman et al. (1992) experiments.
result Neural networks do not exhibit a bias-variance tradeoff when increasing network width, contradicting textbook claims.
New method quantifies redundant information using information bottleneck.
problem Quantifying redundant information among multiple sources.
method Formulated as an information bottleneck problem, termed redundancy bottleneck.
result Extracts information that best predicts the target without revealing source identity.
Partial fusion combines neural networks to balance accuracy and efficiency.
problem Balancing accuracy and computational cost in neural networks.
method Extending weight aggregation methods based on neuron-level similarity, using partial optimal transport to match similar neurons.
result Achieves a flexible tradeoff between computational cost and performance.
Survey on using low-degree polynomials to assess statistical tasks complexity.
problem Understanding the complexity of statistical tasks using polynomial functions.
method Applying low-degree polynomials to measure the complexity of statistical tasks, including detection, recovery, and estimation.
result Low-degree polynomials provide a framework to predict and explain statistical-computational tradeoffs.
Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental …
The paper clarifies fairness vs. accuracy in criminal justice risk assessments.
problem Lack of conceptual precision in discussions of fairness in criminal justice risk assessments.
method Integrated examination of fairness and accuracy using criminology, computer science, and statistics.
result There are at least six kinds of fairness, some incompatible with accuracy.
Study disproves conjecture about low-degree polynomials in hypothesis testing.
problem Conjecture about limitations of polynomial-time algorithms in hypothesis testing.
method Used counterexamples to refute the conjecture and modified the conjecture to rule out the counterexample.
result Disproved conjecture about limitations of low-degree polynomials in hypothesis testing.
Proposes TgNN-LD to improve neural network effectiveness and efficiency.
problem Limits in maintaining tradeoff between data and domain knowledge.
method Converts loss function to constrained form with PDEs, ECs, and EK as constraints, incorporating Lagrangian variables for equitable tradeoff.
result Improves prediction accuracy and conserves resources.
Optimizes glmnet configuration for better accuracy and efficiency.
problem Inappropriate glmnet configuration leads to inaccurate solutions and increased computation time.
method Data-driven framework using neural networks to predict accuracy and computation time from dataset characteristics and configuration.
result Automatic selection of optimal configuration maximizing accuracy under a time constraint.
Structured linear substitutions improve both efficiency and accuracy in neural networks.
problem Improving neural network efficiency and accuracy tradeoff.
method Replacing linear components in pointwise convolutions with structured linear decompositions.
result Structured layers provide Pareto-optimal benefits in efficiency/accuracy.
New DP mechanisms improve ML privacy-utility-computational tradeoffs.
problem Improving privacy in machine learning with multiple passes over data.
method Formalized DP for adaptive streams, extended matrix factorization techniques, Fourier-transform-based mechanism.
result Substantial improvements in privacy-utility-computational tradeoffs over previous methods.
Reduces average-case complexity of sparse PCA from weak PC conjectures.
problem Characterizing the average-case complexity of sparse PCA.
method Reduction from planted clique conjecture to spiked covariance model.
result First full characterization of computational barrier in spiked covariance model, providing tight lower bounds at all sparsities.
The paper analyzes parameter estimation from nonlinear observations.
problem Recovering a structured but unknown parameter from nonlinear observations.
method Develops a framework for characterizing time-data tradeoffs for various parameter estimation algorithms.
result Projected gradient descent schemes converge at a linear rate with near minimal number of samples.
A new tradeoff between regularization and sharpness improves model performance in overparameterized settings.
problem Improving model performance in overparameterized settings with minimum-norm interpolators.
method Proposes a regularization-sharpness tradeoff for overparameterized linear regression with an ℓ^p penalty.
result Empirical validation shows the tradeoff terms can distinguish performant linear interpolators.
Privacy improves robustness in statistical estimation.
problem Sparse mean estimation under privacy constraints.
method Sum-of-Squares method and exponential-time mechanisms.
result Private algorithms matching optimal tradeoffs are not known, but achieved via Sum-of-Squares.
Analyzes self-attention in recurrent networks, proving it mitigates vanishing gradients.
problem Vanishing gradients in recurrent networks when capturing long-term dependencies.
method Formal analysis of self-attention's effect on gradient propagation, proposing a relevancy screening mechanism.
result Self-attention mitigates vanishing gradients in recurrent networks, providing guarantees.
The paper optimizes portfolio selection with a new regret-based method.
problem Dynamic portfolio optimization with unknown tradeoff parameters.
method Regret-based selection criterion for sparse portfolios.
result Optimal sparse portfolios constructed with unknown tradeoff parameter.
We perform the first study of the tradeoff space of access methods and replication to support statistical analytics using first-order methods executed in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical analytics systems differ from conventional SQL-analytics in the amount and types of memory …
New data structure identifies close match from multiple distributions.
problem Identify the closest distribution to a given sample.
method Developed a sublinear-time data structure for identifying the closest distribution.
result First data structure that identifies the closest distribution in sublinear time.
Paper explores tradeoff between standard and robust accuracy for latent models.
problem Tradeoff between standard accuracy and robust accuracy in adversarial training.
method Revisits adversarial training for latent models, considering Gaussian mixture and generalized linear models.
result Low-dimensional manifold structure mitigates the tradeoff between standard and robust accuracy.
How should statistical procedures be designed so as to be scalable computationally to the massive datasets that are increasingly the norm? When coupled with the requirement that an answer to an inferential question be delivered within a certain time budget, this question has significant repercussions for the field of s…
Proposes a new adversarial model to avoid accuracy vs. adversarial accuracy tradeoff.
problem Inherent tradeoff between accuracy and adversarial accuracy in existing adversarial robustness definitions.
method Introduces Voronoi-epsilon adversary that balances perturbation constraints.
result Voronoi-epsilon adversary avoids accuracy vs. adversarial accuracy tradeoff even with large ε. Researchers study fairness-accuracy tradeoffs in predictive models for multiple groups.
problem Understanding the tradeoff between fairness and accuracy in models serving multiple demographic groups.
method Characterizing the fairness-accuracy (FA) Pareto frontier, approximating it from limited data, and bounding the worst-case gap.
result Derivation of worst-case-optimal estimators and uniform finite-sample bounds for the entire FA frontier.
Modern neural networks show no bias-variance tradeoff with increased parameters.
problem The traditional bias-variance tradeoff does not hold in over-parameterized neural networks.
method Empirical measurements and theoretical analysis of bias and variance in modern neural networks.
result Bias and variance can decrease as the number of parameters grows in over-parameterized neural networks.
The paper explores robustness in linear regression models under adversarial attacks.
problem The impact of test-time adversarial attacks on linear regression models.
method Quantitative estimates and phase transitions analysis.
result Precise characterization of tradeoffs between adversarial robustness and accuracy.
Adversarial training can degrade standard accuracy even when optimal for robust accuracy.
problem Tradeoff between standard and robust accuracy in adversarial training.
method Analyzes adversarial training's impact on standard accuracy, even when optimal for robust accuracy.
result Even with optimal predictors, adversarial training can still degrade standard accuracy.
The paper analyzes the bias-variance tradeoff for Bregman divergences.
problem Understanding the bias-variance tradeoff for Bregman divergences.
method Analyzes the bias-variance tradeoff through operations in dual space.
result Derives several results including a generalized law of total variance and ensembling operations.
We study Bayesian optimal control of a general class of smoothly parameterized Markov decision problems. Since computing the optimal control is computationally expensive, we design an algorithm that trades off performance for computational efficiency. The algorithm is a lazy posterior sampling method that maintains a d…
Sum-of-Squares lower bound shows NGCA requires more samples than known algorithms.
problem Finding a non-Gaussian direction in a high-dimensional dataset.
method Sum-of-Squares (SoS) framework to prove lower bounds.
result First super-constant degree SoS lower bound for NGCA.
Optimizes privacy-preserving data release with adversarial neural networks.
problem Minimizing distortion while concealing sensitive information in data release.
method Adversarial neural networks for randomized mechanisms and variational approximation of mutual information privacy.
result Achieves near-optimal tradeoffs between data distortion and privacy in experiments.
Machine learning predicts atomization energies accurately from low-fidelity calculations.
problem Predicting accurate atomization energies of organic molecules efficiently.
method Machine learning models trained on low-fidelity B3LYP energies to predict high-fidelity G4MP2 energies.
result Predicted G4MP2 atomization energies within 0.012 eV for molecules with 10-14 heavy atoms.
First DP algorithm for Wasserstein barycenters on private data.
problem Computing Wasserstein barycenters on private datasets.
method Differentially private algorithms for Wasserstein barycenters.
result High-quality private barycenters with strong accuracy-privacy tradeoffs.
CDC-FM improves generative model quality-generalization tradeoff by regularizing with geometry-aware noise.
problem Tradeoff between high sample quality and memorization in deep generative models.
method Introduces Carré du champ flow matching (CDC-FM) that replaces homogeneous noise with anisotropic Gaussian noise capturing latent data manifold geometry.
result CDC-FM consistently offers better quality-generalization tradeoff across diverse datasets and architectures.
This paper explores tradeoffs between standard and adversarial risks in distributionally adversarial training.
problem Understanding the impact of adversarial training on standard risk and adversarial risk.
method Study of distributionally adversarial training with different learning settings and models.
result Derives Pareto-optimal tradeoff curves between standard and adversarial risks.