Optimization

Boost Bayesian Optimization’s efficiency by leveraging known invariances in objective functions for faster, more effective solutions.

This paper reveals how many buyer samples are needed to set near-optimal posted prices for a single item, resolving a fundamental problem in online markets and offering both theoretical and practical …

Neural networks enhance algorithm selection in branch-and-cut, significantly reducing tree sizes and improving efficiency for mixed-integer optimization, as proven by rigorous theoretical bounds and e…

SAMPa: Parallelizing gradient computations in Sharpness-Aware Minimization (SAM) achieves a 2x speedup and superior generalization.

This paper characterizes the fundamental tradeoff between trusting and distrusting learned type beliefs in games, establishing upper and lower bounds for optimal strategies in both normal-form and sto…

A novel safe & sparse Newton method (SSNS) for entropic-regularized optimal transport boasts strict error control, avoids singularity, needs no hyperparameter tuning, and offers rigorous convergence a…

S-SOS: A new algorithm solves complex, parameterized polynomial problems with provable convergence, enabling efficient solutions for high-dimensional applications like sensor network localization.

ROPINN: Revolutionizing Physics-Informed Neural Networks with Region Optimization

ZO-GDEGA: A unified algorithm achieves faster, more robust zeroth-order minimax optimization with lower complexity and weaker conditions, solving stochastic nonconvex-concave problems.

This paper proves that higher-order GNNs can universally approximate strong branching in MILP solvers, whereas simpler GNNs can only accurately approximate for a specific class of problems.

Enhanced VQAs via Hamming Weight Preserving ansatz and parity checks achieve superior performance on quantum chemistry and combinatorial problems, showcasing quantum advantage potential in NISQ era.

Reshuffling data splits during hyperparameter optimization surprisingly improves model generalization, offering a computationally cheaper alternative to standard methods.

This paper presents the first replicable uniformity tester with nearly linear dependence on the replicability parameter, enhancing the reliability of scientific studies using distribution testing algo…

This paper reveals near-optimal relationships between geometric partitions and replicability in machine learning, establishing the optimality of existing algorithms and introducing a new neighborhood …

KATE: A new scale-invariant AdaGrad variant achieves state-of-the-art convergence without square roots, outperforming AdaGrad and matching/exceeding Adam’s performance.

ReLIZO boosts zeroth-order optimization by cleverly reusing past queries, drastically cutting computation costs while maintaining gradient estimation accuracy.

New algorithm reliably learns Gaussian halfspaces with significantly improved sample and computational complexity compared to existing methods, offering strong computational separation from standard a…

ReEvo, a novel integration of evolutionary search and LLM reflections, generates state-of-the-art heuristics for combinatorial optimization problems, demonstrating superior sample efficiency.

Recurrent neural networks struggle with long-term memory due to a newly identified ‘curse of memory’: increasing parameter sensitivity with longer memory. This work provides insights into RNN optimiza…

Randomized truthful auctions outperform deterministic ones when bidders employ learning algorithms, maximizing revenue in repeated interactions.