Tensor networks constrain kernel machines to Gaussian processes.
problem Speeding up kernel machines with reduced model complexity.
method Proving CPD and TT-constrained models recover Gaussian processes with i.i.d. priors.
result TT-constrained models exhibit more Gaussian process behavior than CPD for the same parameters.
New method solves portfolio optimization with cardinality constraints efficiently.
problem Real-world portfolio constraints like transaction costs and client preferences.
method Continuous relaxation method for NP-hard problems, extending Markowitz and CVaR models.
result Efficient algorithms find near-optimal portfolios for cardinality-constrained problems.
A deep learning framework improves constrained clustering with new types of side information.
problem Limitations of existing constrained clustering formulations.
method Developed a deep learning framework to extend constrained clustering.
result Framework handles various types of constraints, including continuous values and high-level domain knowledge.
Self-distillation improves constrained language generation by aligning models with target distributions.
problem Sparse and uninformative reward signals in constrained generation settings.
method Iteratively refining the base model through self-distillation, incorporating learned twist functions and proposals.
result Substantial gains in generation quality through improved model alignment with target distributions.
This article reviews and explains HMC-based methods for sampling constrained continuous distributions.
problem Sampling from continuous distributions with constraints.
method HMC and related methods for constrained sampling.
result HMC and related methods are more efficient for constrained sampling.
New model generates data on constrained sets without losing tractability.
problem Generating data on constrained sets without losing tractability.
method Mirror Diffusion Models (MDM) learn diffusion processes in a dual space constructed from a mirror map.
result MDM generates data on convex constrained sets without losing tractability.
Proposes a Gaussian process model for constrained dynamics learning.
problem Challenges in identifying constrained dynamics of mechanical systems.
method Combines analytical mechanics with Gaussian process regression.
result Improves data efficiency and constraint integrity in predictions.
Develops a theory to make learning solutions fair and safe.
problem Ensuring learning solutions are unbiased and safe in critical applications.
method Generates a generalization theory based on PAC learning framework, introduces constrained learning algorithm.
result Proves that constrained learning is as learnable as unconstrained learning, provides practical algorithm.
Extends GENO framework for GPU optimization of constrained ML problems.
problem Constrained optimization in classical machine learning.
method Extends GENO framework to GPU optimization, specifying problems in a modeling language.
result Solvers on GPU outperform state-of-the-art approaches by several orders of magnitude.
Proposes a new method for constrained generative modeling using Langevin dynamics.
problem Challenges in satisfying underlying constraints with score-based generative models.
method Uses kinetic Langevin dynamics with specular reflection to model constraints.
result Demonstrates efficient numerical samplers with optimal convergence rates.
We find the optimal error for a constrained regression model under a linear model.
problem Minimizing error while adhering to demographic parity constraints.
method Proposed a minimax optimal error analysis for a demographic parity-constrained regression problem within a linear model.
result The minimax optimal error is characterized by $Θ( rac{dM}{n})$.
This work compares regularization and constrained inference for label constraints in machine learning.
problem Improving model performance with label constraints in machine learning.
method Comparison of regularization and constrained inference strategies.
result Constrained inference reduces population risk by correcting model violations, while regularization narrows the generalization gap but introduces bias.
Optimizes PDE-constrained LDDMM for efficient non-rigid registration.
problem Inexact Newton-Krylov optimization in PDE-constrained LDDMM leads to poor geodesic paths.
method Band-limited vector field parameterization to optimize computational complexity.
result Optimized method shows competitive performance with reduced memory load and computational time.
Algorithm optimizes constrained reinforcement learning with dual variables.
problem Minimizing convex functional subject to convex constraint in large state spaces.
method VPDPO algorithm using Lagrangian and Fenchel duality.
result Achieves sublinear regret and constraint violation, globally optimal policy.
Unified framework for aligning and composing diffusion models to satisfy multiple constraints.
problem Improving quality and compliance of generated samples from diffusion models.
method Constrained optimization framework that unifies alignment and composition of diffusion models.
result Proposed framework effectively satisfies multiple constraints in image generation.
A multilevel optimization method for constrained problems.
problem Regularized constrained linear inverse problems with box constraints.
method Geometric multilevel optimization with varying discretization levels.
result Preserves feasibility of updates while speeding up computations.
Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under mean square error (…
Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained d…
Three physics-constrained regression exercises for image velocimetry and turbulence modeling.
problem Image velocimetry and turbulence modeling challenges.
method Physics-constrained regression exercises implemented as toy problems.
result Python codes provided for all exercises.
We present a novel approach for constrained Bayesian inference. Unlike current methods, our approach does not require convexity of the constraint set. We reduce the constrained variational inference to a parametric optimization over the feasible set of densities and propose a general recipe for such problems. We apply …
Bayesian optimization tackles constrained high-dimensional problems with penalties and trust regions.
problem Constrained optimization in high-dimensional black-box settings with expensive evaluations and complex feasibility regions.
method Penalty formulation, surrogate model, trust region strategy, Expected Improvement acquisition function.
result The proposed Trust Region method identifies high-quality feasible solutions with fewer evaluations and maintains stable performance.
This study improves numeric data generation using constrained WGAN structures.
problem Overfitting and ill-conditioning in numeric data generation with GANs.
method Designs and evaluates constrained network structures (isomorphic, mirror, self-symmetric) in WGANs for numeric data generation.
result Constrained structures significantly improve numeric data generation in 17/20 experiments.
Proposes a framework for learning constrained motor skills.
problem Learning constrained motor skills in robotic systems.
method Exploits probabilistic properties of multiple demonstrations in a linearly constrained optimization problem.
result Proposes a non-parametric solution for constrained motor skills.
Efficient method for constrained optimization under partial observations with provable convergence.
problem Optimizing under partial and constrained data.
method Improved acquisition functions and Gaussian process embedding for partially observable constraints.
result Empirically validated method outperforms traditional approaches.
Proposes a new framework for constrained classification and ranking.
problem Class imbalance and constrained optimization in binary classifiers.
method Explicitly models the threshold for satisfying constraints, using a surrogate loss function.
result Competitive performance relative to existing baselines on various benchmarks.
ECC compresses DNNs for energy-constrained devices like UAVs and smartphones.
problem Energy-constrained deep neural networks in vision applications.
method ECC uses a bilinear regression model to estimate DNN energy consumption and optimizes compression to meet energy constraints.
result ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art techniques.
Unified framework for constrained diffusion models on nonconvex sets with efficient landing mechanism.
problem Efficiently modeling generative models under nonconvex constraints.
method Unified framework with overdamped and underdamped dynamics, landing mechanism.
result Significantly reduces computational cost while maintaining sample quality.
CPS solves inverse problems using forward passes and constrained particle seeking.
problem Solving inverse problems with limited forward observation information.
method Gradient-free approach that reformulates inverse problem as constrained optimization.
result CPS achieves results comparable to gradient-based methods while outperforming alternatives.
A new method for optimizing non-decomposable metrics with constraints.
problem Optimizing complex machine learning objectives with thresholded constraints.
method Formulate rate-constrained optimization using the Implicit Function theorem and solve with gradient-based methods.
result Demonstrated effectiveness over existing methods on benchmark datasets.
cCBO optimizes interventions in causal graphs under constraints.
problem Finding optimal interventions in causal graphs with constraints.
method Exploits graph structure, uses Gaussian processes, and sequentially selects interventions.
result Successful trade-off between fast convergence and feasibility of interventions.
Paper uses RL to solve constrained combinatorial optimization problems.
problem Constrained combinatorial optimization problems.
method Extending NCO theory to CMDPs, using RL with penalty signals.
result Proposes a superior method for solving constrained problems.
Proposes ConstraintMatch for semi-supervised clustering with unconstrained data.
problem Leveraging unconstrained data alongside constraints for clustering models.
method Semi-supervised context with pseudo-constraining and pseudo-labeling mechanisms.
result Demonstrates effectiveness of ConstraintMatch over baselines.
Optimal strategy found for constrained multi-armed bandit problems.
problem Constrained multi-armed bandit problems.
method Extended ε_t-greedy strategy with asymptotic optimality.
result Asymptotic optimality achieved with a simple strategy.
Study examines sample complexity for RL with safety constraints.
problem Ensuring safety in RL for systems with constraints.
method Analyzes two RL approaches: generative and online.
result Sample complexity increased by a logarithmic factor in constraints.
The paper develops methods for time-varying constrained online convex optimization.
problem Time-varying loss and constraint functions in online convex optimization.
method Model-based augmented Lagrangian methods (MALM) for time-varying and delayed feedback.
result Sublinear regret and constraint violation for both time-varying and delayed feedback scenarios.
Develops new reinforcement learning methods for complex constrained decision-making problems.
problem Complex constrained decision-making problems with a continuum of constraints.
method Proposes semi-infinitely constrained Markov decision processes (SICMDPs) and two reinforcement learning algorithms: SI-CRL and SI-CPO.
result Demonstrates the effectiveness of SI-CRL and SI-CPO in solving complex sequential decision-making tasks.
Shape-constrained symbolic regression improves model extrapolation with prior knowledge.
problem Improving model extrapolation with prior knowledge in symbolic regression.
method Shape-constrained symbolic regression using evolutionary algorithms with interval arithmetic.
result Models with shape constraints have improved extrapolation but lower accuracy on test sets.
New method for online inference of constrained optimization problems.
problem Online inference of constrained stochastic optimization problems.
method Random scaling of Sketched Stochastic Sequential Quadratic Programming (SSQP).
result Asymptotically valid confidence intervals and matrix-free computation.
New method optimizes processes under constraints using bivariate Gaussian models.
problem Optimizing processes with constraints using traditional methods.
method Developed a constrained expected improvement acquisition function using bivariate Gaussian process models.
result Demonstrated improved performance in a manufacturing cure process optimization.
Solves VaR-constrained portfolio optimization in markets with stochastic volatility.
problem Optimizing portfolio in markets with stochastic volatility under VaR constraints.
method Dynamic programming approach to Heston's stochastic volatility model.
result Optimal investment strategy linked to unconstrained problem via a vega-neutral derivative.
Chance-constrained ActInf allows for small violations of constraints to drive goal-directed behavior.
problem Goal-directed behavior constrained by prior beliefs.
method Introducing chance constraints to ActInf, allowing for small violations of constraints.
result Chance-constrained ActInf allows for a trade-off between robust control and chance constraint violation.
New algorithms for sampling in constrained domains without learning rates.
problem Sampling in constrained domains with fairness constraints and post-selection inference.
method Coin betting ideas from convex optimisation and a unifying framework for constrained sampling.
result Our algorithms achieve competitive performance without hyperparameter tuning.
A new method for constrained Bayesian optimization using Max-Value Entropy Search.
problem Optimizing expensive functions with unknown constraints.
method Constrained Max-value Entropy Search (cMES), a novel acquisition function.
result cMES outperforms prior work on constrained hyperparameter optimization problems.
Unified framework for network model assessment using maximum entropy.
problem Statistical inference for network models.
method Constrained entropy-maximization problem, Lagrange multipliers.
result Consistent goodness-of-fit and two-sample tests for network models.
The paper analyzes constrained optimal portfolios in high dimensions using novel statistical learning techniques.
problem Forming optimal portfolios with constraints in high-dimensional asset spaces.
method CROWN method integrating factor models with nodewise regression for estimation in large dimensions.
result Demonstrates estimation consistency and convergence rates for constrained portfolio weights, risk, and Sharpe Ratio.
Efficient method solves constrained Lasso problems.
problem Variable selection with prior information.
method Inexact augmented Lagrangian method exploiting second-order sparsity.
result Superior performance compared to first-order methods.
A new method optimizes slicing directions for SW distances to improve high-dimensional probability measure comparison.
problem Challenging identification of informative slicing directions for SW distances.
method Constrained learning approach to optimize slicing directions, using continuous relaxations and gradient-based primal-dual approach.
result Demonstrated efficacy in learning more informative slicing directions on various high-dimensional data.
Investors face constraints in Heston's model; optimal allocation differs from naive capped strategy.
problem Optimizing portfolio allocation with convex constraints in Heston's stochastic volatility model.
method Applied duality methods to derive a closed-form solution.
result The optimal constrained portfolio allocation differs from the naive capped portfolio, leading to different wealth outcomes.