A new algorithm Zeus for multi-objective clustering with lexicographic ordering and slack.
problem Improving clustering quality with lexicographic objectives and slack.
method Introduced a slack mechanism and proposed Zeus algorithm to solve multi-objective clustering problems.
result Empirical validation of Zeus on real-world data demonstrates its effectiveness.
An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global …
EDSVM uses elite observations to guide SVM classification.
problem Classical SVMs lack ways to encode trusted models or preferences.
method EDSVM augments SVMs by guiding slack variables for elite observations.
result EDSVM models closely track reference SVMs while achieving competitive performance.
Fairness in ML models can lead to counterintuitive predictions.
problem Enforcing fairness in machine learning models leads to unexpected outcomes.
method Introducing slack-consistency as a desirable property for fairness procedures.
result Standard fairness methods violate the property of monotonicity with respect to slack.
AR model forecasts partially observed dynamical time series by estimating evolution function and imputing missing variables.
problem Forecasting dynamical time series with missing variables.
method Autoregressive with slack time series (ARS) model.
result ARS model forecasts future time series with time-invariant and linear assumptions.
The paper proves geometric and spectral alignment for deep neural networks.
problem Understanding the singular spectra of deep neural network layers.
method Proves deterministic quotient-geometric estimates for singular spectra of Frobenius-normalized layer factors.
result Exact power-law spectra form a trace-normalized Cartan orbit under Frobenius normalization.
Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes. This hyperplane is determined by support vectors. In existing SVM formulations, the objective function uses L2 norm or L1 norm on slack variables. The number of support vectors is a me…
Optimization with inequality constraints using embedded gradient vector field method
problem Optimization with inequality constraints
method Geometric framework using quadratic slack variables
result Derives Lagrange multiplier functions and second-order optimality conditions
DiffSlack learns neural networks with nonlinear constraints via learnable slack variables.
problem Enforcing nonlinear inequality constraints in neural networks.
method DiffSlack reformulates inequalities as equalities with learnable slack variables, predicting them as part of the network output.
result DiffSlack achieves higher planning success rates and stronger geometric constraint satisfaction compared to existing methods.
Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.
problem Queue peak laws in stochastic networks with geometric thresholds.
method Self-normalization mechanism
result Logarithmic scaling of queue peaks after geometric thresholds.
New analysis reveals gaps in selective classifiers, guiding improvements.
problem Improving selective classifiers to match perfect-ordering oracle performance.
method Formalized selective classification gap, decomposed into five sources of looseness.
result Monotone post-hoc calibration has limited impact on closing the gap.
We consider the classical optimal dividends problem under the Cramér-Lundberg model with exponential claim sizes subject to a constraint on the time of ruin. We introduce the dual problem and show that the complementary slackness conditions are satisfied, thus there is no duality gap. Therefore the optimal value functi…
A method to learn transition matrices without anchor points improves classifier performance.
problem Learning transition matrices in label-noise learning without anchor points.
method Transition-revision ( T T T -Revision) method to learn transition matrices. result The proposed method leads to better classifiers without anchor points.
Researchers analyze betting odds and free coupons to find exploitable gains.
problem Determining if customers can exploit free coupons for guaranteed gains.
method Using desirability theory and the Choquet integral, they evaluate odds and free coupons.
result Customers can exploit free coupons for guaranteed gains under certain conditions.
The exact nonnegative matrix factorization (exact NMF) problem is the following: given an m m m -by- n n n nonnegative matrix X X X and a factorization rank r r r , find, if possible, an m m m -by- r r r nonnegative matrix W W W and an r r r -by- n n n nonnegative matrix H H H such that X = W H X = WH X = W H . In this paper, we propose two heuristics for exac…
Proposes a method for evaluating multiple dimensions of organizational effectiveness using DEA.
problem Evaluating multiple dimensions of organizational effectiveness in large data sets.
method Introduces two regularized DEA models (SBM and GP-SBM) to estimate both dimension-specific and aggregate efficiency scores.
result Demonstrates improved efficiency and validity compared to conventional methods.
Improved robustness of machine learning models with controlled Lipschitz constants.
problem Vulnerability of state-of-the-art models to adversarial attacks.
method Proposes a CLL loss that calibrates the margin and Lipschitz constant penalties, improving robustness certificates.
result Consistently outperforms other losses on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets.
Graph neural networks optimize radio resource management policies for wireless networks.
problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.
Efficiently learns optimal regularizers to improve model performance.
problem Improving model generalization and performance on test data.
method Views regularizers as upper bounds on generalization gap and uses linear programming to find optimal hyperparameters.
result The approach can jump to optimal hyperparameters with limited data, improving model performance.
Tail-Safe hedging uses reinforcement learning with a safety layer to manage financial risks.
problem Managing financial risks in derivatives trading with robustness and explainability.
method Combines distributional reinforcement learning with a CBF-QP safety layer to enforce financial constraints.
result Improves risk management without degrading central performance and avoids hard constraint violations.
Study bandwidth-limited training and inference of language models.
problem Training and inference of language models on scattered data with limited bandwidth.
method Analyzed two protocols: Federated Probe-Logit Distillation (FPLD) for training and Federated Conformal RAG (FC-RAG) for inference.
result Explicit high-probability KL-consistency rate and distribution-free marginal-coverage bound for Federated Conformal RAG.
We introduce a longevity feature to the classical optimal dividend problem by adding a constraint on the time of ruin of the firm. We extend the results in \cite{HJ15}, now in context of one-sided Lévy risk models. We consider de Finetti's problem in both scenarios with and without fix transaction costs, e.g. taxes. We…
Storytelling algorithms aim to 'connect the dots' between disparate documents by linking starting and ending documents through a series of intermediate documents. Existing storytelling algorithms are based on notions of coherence and connectivity, and thus the primary way by which users can steer the story construction…
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of…
Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer …
Study optimal policies under budget and coverage constraints.
problem Optimal policy learning with budget and coverage constraints.
method Combination of knapsack structure, affine threshold rule, linear programming relaxation, Greedy-Lagrangian (GLC), and rank-and-cut (RC) algorithms.
result GLC closely approximates the optimal solution and achieves near-optimal performance in finite samples; RC is approximately optimal under certain conditions.
Nonnegative Matrix Factorization (NMF) has been a popular representation method for pattern classification problem. It tries to decompose a nonnegative matrix of data samples as the product of a nonnegative basic matrix and a nonnegative coefficient matrix, and the coefficient matrix is used as the new representation. …
New RGraSP framework for efficient non-convex optimization.
problem Large-scale non-convex sparsity-constrained optimization problems.
method Relaxed gradient support pursuit with semi-stochastic gradient hard thresholding.
result Our algorithms converge faster with lower per-iteration cost.
Learning with non-modular losses is an important problem when sets of predictions are made simultaneously. The main tools for constructing convex surrogate loss functions for set prediction are margin rescaling and slack rescaling. In this work, we show that these strategies lead to tight convex surrogates iff the unde…
Study robust hypothesis testing under Hellinger distance, proving lower bounds and providing tests.
problem Testing close variants of specified distributions robustly to Hellinger distance.
method Lower bound on slack factor, testing with Hellinger balls, symmetric chi-squared distance analysis.
result Lower bound on slack factor quantifies robustness under misspecification.
Study finds super-efficiency correlates more strongly with stock market valuation than ROA in Chinese banks.
problem Investigating the relationship between bank efficiency and stock market valuation.
method Employed a non-radial, non-oriented slack-based super-efficiency Data Envelopment Analysis (Super-SBM-UND-VRS) model, treating NPLs as undesired output.
result Super-efficiency is more strongly correlated with stock market valuation than ROA, as measured by Tobin's Q.
CREDO combines credal and conformal methods to create interpretable prediction intervals.
problem Overconfident prediction intervals in regions of model extrapolation.
method CREDO uses a credal envelope to widen intervals in weak evidence regions and then applies conformal calibration.
result CREDO prediction intervals are interpretable and maintain target coverage.
Unified framework for deriving generalization bounds in supervised learning.
problem Generalization error bounds in supervised learning.
method Data Processing Inequality PAC-Bayesian framework.
result Unified bounds on binary Kullback-Leibler generalization gap for various divergences.
This work creates a CS for non-negative heavy-tailed data with bounded mean.
problem Constructing a confidence sequence for non-negative heavy-tailed data with bounded mean.
method Non-parametric, non-asymptotic lower confidence sequence construction.
result The constructed CS is efficient and can be converted into a closed-interval CS.
A new framework uses directed information to efficiently select context chunks.
problem Efficiently selecting relevant context chunks for query understanding.
method Directed Information γ γ γ -covering framework, formulated as a γ γ γ -cover problem, with a greedy algorithm for context selection. result The γ γ γ -covering algorithm provides clear advantages in hard-decision regimes like context compression and single-slot prompt selection. Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However, classical convex surrogates can only tightly bound modular loss functions, sub-modular functions or supermodular functions separately while …
Algorithm stabilizes queues in asymmetric systems with unknown service rates.
problem Stabilizing queues in multi-class multi-server systems with unknown service rates.
method Proposes UCB and Thompson Sampling algorithms to stabilize queues while learning service rates.
result Achieves system stability with an average queue length bound of \(O(\min\{N,K\}/ε)\) for large time horizon \(T\).
New algorithm tackles dynamic assortment optimization with knapsack constraints.
problem Optimizing retailer's assortment decisions under resource constraints with multi-nomial choice modeling.
method Epoch-based re-solving algorithm that transforms MNL's fractional structure into a linear program with slack variables.
result Regret scales logarithmically with time horizon and resource capacities.
New method optimizes portfolios by dynamically integrating ESG constraints.
problem Static ESG scores mismatch sequential portfolio decisions.
method MACF-X, a family of adapters that learns ESG costs from multimodal evidence.
result Reduces tail ESG budget pressure while maintaining financial performance.
Paper proposes RL for efficient dispatching in dynamic manufacturing environments.
problem Efficient dispatching in dynamic, stochastic manufacturing environments.
method Reinforcement learning (RL) with policy transfer for dynamic shop floor settings.
result Proposed RL approach outperforms other methods in terms of total discounted reward and average lateness, tardiness.
Theory for soft-margin classifiers on object manifolds.
problem Classifying object manifolds with variability.
method Mean-field theory of soft-margin classifiers applied to object manifolds.
result Prediction of classification errors and their dependence on regularization.
A new method detects outliers in dirty data using a leave-out strategy.
problem Outliers in training data skew support vector machine performance.
method Leave-out strategy: temporarily omit one candidate at a time for outlier detection.
result The leave-out strategy improves outlier detection compared to existing methods.
Improved algorithm reduces regret in NRM with unknown demand.
problem Optimizing prices for products with limited resources under unknown demand.
method Primal-dual optimization with demand balancing.
result Improved regret bound of O ( N 3.25 T ) O(N^{3.25}\sqrt{T}) O ( N 3.25 T ) . OLLA framework efficiently samples from constrained distributions with nonconvex constraints.
problem Sampling from constrained distributions with nonconvex constraints is challenging.
method Overdamped Langevin with Landing (OLLA) framework that handles both equality and inequality constraints.
result OLLA converges exponentially fast to the constrained target density in W 2 W_2 W 2 distance. A new CVaR test reduces group performance disparity detection complexity.
problem Detecting performance disparities across multiple sensitive groups in ML models.
method Conditional Value-at-Risk (CVaR) testing to reduce sample complexity.
result Sample complexity reduced exponentially to be at most the square root of the number of groups.
Improves VAE latent space by balancing capacity and disentanglement.
problem Hard to achieve both high capacity and disentanglement in VAE latent space.
method Formulates soft-constrained optimization problem to address both properties.
result EC and weight-filter method improve latent space quality and performance.
Bayesian neural networks learn efficiently at infinite width, matching polynomial-width performance.
problem Understanding the inductive bias of infinite-width neural networks.
method Analyzing the reduced entropy and using subsampling techniques.
result The Bayesian mean-field learner generalizes exactly on polynomially-bounded targets.
A quantum framework optimizes collateral allocation for derivatives.
problem Legal constraints and operational rules in collateral allocation for derivatives.
method Certified higher-order quantum framework that normalizes margin requirements and builds a bounded neighborhood of actions.
result Quantum framework improves certified sample quality compared to classical methods.