New algorithm detects changes quickly without knowing parameters, near optimally.
problem Quickest change detection with unknown parameters.
method Leverages theoretical asymptotic properties to derive a scalable approximate algorithm with near optimal performance.
result Detects changes in constant complexity with near optimal performance.
The paper offers simple, near-optimal algorithms for multi-group learning.
problem Learning predictors within subgroups of a population, addressing fairness and hidden stratification.
method Studies the structure of solutions and provides simple, near-optimal algorithms.
result Simple and near-optimal algorithms for multi-group learning.
Time-limited metaheuristics find near-optimal solutions for constrained portfolio optimisation.
problem Finding near-optimal solutions for constrained portfolio optimisation within limited computation time.
method Time-limited metaheuristics (simulated annealing, tabu search, genetic algorithm) tested on historical market data.
result Simulated annealing found near-optimal solutions in 5 seconds across most datasets.
New algorithm solves complex optimization problems efficiently.
problem Minimizing convex upper-level functions over optimal lower-level solutions.
method Reformulates bilevel problems into functionally constrained problems, achieving near-optimal rates.
result Achieves near-optimal rates for both smooth and nonsmooth problems.
New method finds near-optimal solutions for non-convex optimization problems.
problem Finding near-optimal solutions for non-convex optimization problems.
method Riemannian stochastic recursive momentum method
result Achieves a near-optimal complexity of i l d e O ( ε − 3 ) ilde{\mathcal{O}}(ε^{-3}) i l d e O ( ε − 3 ) . Study on learning sparse fixed-structure Gaussian Bayesian networks with near-optimal sample complexity.
problem Learning a fixed-structure Gaussian Bayesian network up to a bounded error in total variation distance.
method Analysis of node-wise least squares regression and introduction of BatchAvgLeastSquares and CauchyEst algorithms.
result BatchAvgLeastSquares and CauchyEstTree have near-optimal sample complexity.
Deep RL learns 2-opt heuristics to improve TSP solutions.
problem Improving TSP solutions beyond initial heuristics.
method Deep reinforcement learning to learn 2-opt operations.
result Learned policies improve solutions faster than previous methods.
New DP algorithms achieve near-optimal regret bounds for online learning problems.
problem Online learning problems with zero-loss solutions and differential privacy constraints.
method Developed new Differentially Private algorithms with near-optimal regret bounds.
result Achieved near-optimal regret bounds for various online prediction and convex optimization problems.
Proposes method to discover diverse near-optimal policies in reinforcement learning.
problem Finding different solutions to the same problem in reinforcement learning.
method Formalizes problem as CMDP, uses Successor Features, proposes new diversity rewards.
result Proposed method discovers diverse near-optimal policies that are robust and distinct.
This work uses MMD to find diverse policies in reinforcement learning.
problem Finding multiple near-optimal policies for a task.
method Formalizes policy difference as trajectory distribution discrepancy, uses MMD for optimization.
result Derives gradient-based optimization for diverse policy identification.
Algorithm finds near-optimal VaR portfolios using MILP, improving risk management.
problem Computing optimal VaR portfolios is hard due to non-convexity and combinatorial nature.
method Formulates VaR portfolio problem as MILP, uses alternate formulations for guarantees.
result Near-optimal VaR portfolios with near-optimality guarantees.
Motivated principally by the low-rank matrix completion problem, we present an extension of the Frank-Wolfe method that is designed to induce near-optimal solutions on low-dimensional faces of the feasible region. This is accomplished by a new approach to generating ``in-face" directions at each iteration, as well as t…
Introduces model class selection to find sets of near-optimal models.
problem Finding sets of near-optimal models within multiple model collections.
method Generalizes model set selection framework to model class selection, using data splitting approaches.
result Shows that simpler, interpretable models can perform similarly to complex machine learning models.
Fast robust subspace tracking in sparse data-dependent noise with near-optimal delay.
problem Robustly tracking time-varying subspaces in the presence of sparse outliers.
method Introduces a fast mini-batch robust ST solution under mild assumptions.
result Provably correct subspace tracking with near-optimal delay and same time complexity as simple PCA.
This work expands state-action aggregation methods for non-Markovian environments.
problem Real-world problems with large state and action spaces are not tractable with existing methods.
method Expands Extreme State Aggregation (ESA) framework to non-Markovian homomorphisms and relaxes policy uniformity.
result Near-optimal performance is guaranteed even for non-Markovian homomorphisms.
Paper develops a fast method to find near-optimal power solutions.
problem Solving AC OPF on fast timescales for large networks.
method Leverages machine learning to map system loading to optimal generation values.
result Near-optimal and feasible solutions found on milliseconds timescales.
New method optimizes ambiguity sets for robust MDPs, improving policy robustness.
problem Sensitivity of optimal policies to model misspecification in MDPs.
method Proposes a new approach to optimize the shape of ambiguity sets, departing from norm-bounded uniform and symmetric sets.
result Empirical results show practical promise of weighted near-optimal ambiguity sets.
A deep Q-learning method improves downlink power allocation in multi-cell networks.
problem Optimal resource allocation in dense, heterogeneous wireless networks is computationally challenging.
method Proposes a centralized DRL-based downlink power allocation scheme using deep Q-learning.
result The proposed DRL-based power allocation scheme outperforms conventional methods in multi-cell scenarios.
FMOPF generates diverse near-optimal power flow solutions.
problem Generating diverse near-optimal power flow solutions for risk quantification.
method Decouples compression from generation through latent flow matching and explicitly models load-state coupling.
result FMOPF provides the most effective Newton-Raphson warm starts and lowest tail risk.
SUSTAIN algorithm tackles stochastic bilevel optimization with near-optimal complexity.
problem Stochastic bilevel optimization problems with specific convexity and smoothness properties.
method SUSTAIN algorithm using single-timescale double-momentum stochastic approximation.
result SUSTAIN achieves near-optimal complexity for finding ε-stationary solutions.
New algorithm reduces FL sample and communication costs.
problem Optimizing FL for minimal samples and rounds.
method Stochastic two-sided momentum algorithm.
result Achieves near-optimal sample and communication complexities.
Near-optimal regret in distributed bandit learning with efficient communication protocols.
problem Minimizing total regret in collaborative bandit learning with limited communication.
method Proposed communication protocols for distributed multi-armed and linear bandits with near-optimal regret and efficient communication costs.
result Achieved near-optimal regret with communication costs independent of time horizon and number of arms.
Paper optimizes hyperspherical prototypes for better class separation.
problem Previous HPL approaches either lack principled optimisation or are limited to one latent dimension.
method Develops a principled optimisation procedure and uses linear block codes to create well-separated prototypes in various dimensions.
result Optimal prototype placement is characterized with achievable and converse bounds, showing near-optimality.
Develops methods for near-optimal personalized treatment recommendations.
problem Assigning optimal treatments to patients based on individual characteristics.
method Outcome weighted learning framework to estimate near-optimal alternative individualized treatment recommendations (A-ITR).
result Consistency of proposed methods and upper bound for risk between optimal and estimated recommendations.
Bayesian method estimates dynamics from near-optimal trajectories.
problem Estimating dynamics from near-optimal expert trajectories in reinforcement learning.
method Constraint-based Bayesian approach integrating expert near-optimality.
result Significant improvements in decision-making and transfer success.
Paper tackles non-stationary kernelized bandits with near-optimal algorithm.
problem Minimizing regret in a time-varying reward function.
method Near-optimal algorithm with a novel restarting phased elimination with random permutation (R-PERP).
result Regret upper bound matches the lower bound, making the algorithm near-optimal.
MIP-GNN uses graph neural networks to predict variable biases for MIP solvers.
problem Improving combinatorial optimization through data-driven insights.
method Encoding MILP interactions as graphs, training a graph neural network to predict variable biases, and guiding the MIP solver with these predictions.
result Significant improvements in solving binary MILPs compared to default settings of state-of-the-art solvers.
A new method uses machine learning to optimize user pairing and association in multicell NOMA networks.
problem Optimizing user pairing and association in multicell non-orthogonal multiple access (NOMA) systems.
method Formulated as a combinatorial optimization problem, solved using a Pointer Network (PtrNet) trained with deep reinforcement learning.
result Achieves near-optimal performance in terms of aggregate data rate, outperforming random heuristics by up to 30%.
We propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on pairs of data points, and is a generalization of the kernel matching pursuit method. It can operate both in a supervised and a semi-supervi…
Hedging in the presence of transaction costs leads to complex optimization problems. These problems typically lack closed-form solutions, and their implementation relies on numerical methods that provide hedging strategies for specific parameter values. In this paper we use a genetic programming algorithm to derive exp…
We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning. We investigate the near-optimality of greedy algorithms for this problem with both modular and non-modular cost functions. In both case…
Efficient streaming algorithms for robust statistics with near-optimal memory.
problem High-dimensional robust statistics tasks in streaming model.
method First efficient streaming algorithms with near-optimal memory requirements.
result Near-optimal error guarantees and space complexity nearly-linear in the dimension for robust mean estimation.
Solves action selection for large spaces in RL, achieving near-optimal performance.
problem Selecting a small, representative subset of actions from a large, shared action space.
method Extends meta-bandit approach to MDPs, using a relaxed sub-Gaussian process model.
result Achieves performance comparable to full action space, with theoretical guarantees.
A new neural network learns optimal designs in one shot.
problem Designing optimal structures in iterative processes.
method Theory-driven machine learning combining student and teacher models.
result Near-optimal designs achieved with less data.
Develops a method for near-optimal asset allocation with trading constraints.
problem Optimizing investment strategies in financial markets with trading constraints.
method Dual-control method using convex duality to generate bounds on optimal value function.
result Derives near-optimal asset allocation explicitly and demonstrates its accuracy in a real financial market.
New framework solves low-rank optimization problems to certifiable optimality.
problem Low-rank optimization problems with certifiable solutions.
method Mixed-Projection Conic Optimization framework using symmetric projection matrices and outer-approximation algorithms.
result Solves low-rank problems to certifiable optimality, outperforming existing methods.
Quantum algorithm speeds up MIP solving by a near-quadratic factor.
problem Solving Mixed Integer Programs (MIPs) efficiently.
method Incremental-Quantum-Branch-and-Bound algorithm combining quantum speedup with classical search heuristics.
result Universal near-quadratic speedup over classical Branch-and-Bound algorithms.
Deep learning methods find near-optimal solutions without explicit regularization.
problem Theoretical challenges in understanding deep learning's success.
method Analysis of gradient methods, overparametrization, and implicit regularization.
result Gradient methods can find near-optimal solutions and exhibit excellent predictive accuracy without explicit regularization.
New algorithm learns optimal policies without explicit rewards.
problem Learning optimal policies without explicit rewards.
method Developed a computationally tractable algorithm for reward-free navigation using linear value iteration.
result PAC guarantees on learning near optimal value functions and policies.
Paper tackles clustering with ordinal comparisons, achieving near-optimal results.
problem Clustering with ordinal comparisons when similarity measures are not available.
method Two-step procedure: estimate similarity matrix from comparisons, then apply SDP clustering.
result Near-optimal recovery of planted clustering using near-optimal number of comparisons.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introd…
Adam is the most practical optimizer, especially in low-budget scenarios.
problem Evaluating optimizers' performance without considering hyperparameter tuning costs.
method Evaluated a variety of optimizers on standard datasets and architectures, accounting for hyperparameter tuning costs.
result Adam is the most practical solution, especially in low-budget scenarios.
FedConPE improves conversational recommender systems efficiency and privacy.
problem Efficiently eliciting user preferences in interactive systems with heterogeneous clients.
method Phase elimination-based federated conversational bandit algorithm with adaptive key term construction.
result Minimizes uncertainty across all dimensions in feature space and offers improved efficiency and privacy.
Unified DNN-based precoder for MIMO networks with multiple objectives.
problem Optimizing data transmission, energy harvesting, and security in MIMO networks.
method Rotation-based precoding and DNN for multi-objective optimization.
result DNN-based precoder reduces computational complexity and achieves near-optimal performance.
Flat solutions don't guarantee generalization for logistic loss in neural networks.
problem Proving flat solutions imply generalization for logistic loss in neural networks.
method Analyzing overparameterized two-layer ReLU networks with univariate input under logistic loss.
result Flat solutions enjoy near-optimal generalization bounds within uncertain sets but can still overfit at infinity.
Efficiently find near-optimal medical treatments with less trial and error.
problem Finding effective medical treatments through trial and error.
method Formalizes the problem, uses a causal inference framework, and proposes model-based dynamic programming and greedy algorithms.
result Our methods compare favorably to model-free reinforcement learning, offering a more transparent trade-off between search time and treatment efficacy.
Algorithm extsc{Pedel} learns near-optimal policies efficiently on specific problems.
problem Learning near-optimal policies in linear MDPs with minimal samples.
method Online experiment design to focus exploration on relevant directions.
result Achieves instance-dependent complexity, outperforming minimax-optimal algorithms.
Improved algorithm for adaptive dueling bandits with near-optimal regret bound.
problem Non-stationary dueling bandits with unknown number of preference changes.
method Elimination-based rescheduling algorithm for adaptive dynamic regret.
result Near-optimal i l d e O ( S e x t t t C W T ) ilde{O}(\sqrt{S^{ exttt{CW}} T}) i l d e O ( S e x ttt C W T ) dynamic regret bound.