Optimization

LeTCC: A novel learning-theoretic framework for resilient distributed computing, achieving faster convergence and higher accuracy than existing methods by integrating learning theory principles with c…

Researchers developed novel algorithms to generate structurally diverse graphs, improving graph algorithm testing and neural network evaluation.

This paper delivers novel, universally applicable bounds for the smallest NTK eigenvalue, regardless of data distribution or dimension, leveraging the hemisphere transform.

BDNE: a novel boundary decomposition method accurately estimates the nadir objective vector in complex multi-objective optimization problems.

This paper presents a novel algorithm for binary search using distributional predictions, achieving optimal query complexity O(H(p) + log n) and demonstrating enhanced robustness against prediction er…

This paper presents efficient algorithms to detect whether an agent updates beliefs optimally (Bayesian) or exhibits bias towards their prior beliefs, using information design and signaling schemes.

This paper presents a novel, UCB-like algorithm for bandits with stochastic and adversarial constraints, achieving optimal performance without the stringent assumptions of prior primal-dual methods.

Learners can improve accuracy in strategic classification by selectively revealing partial classifier information to agents, strategically guiding agent behavior and maximizing accuracy.

GraphComBO efficiently optimizes functions defined on node subsets within graphs using Bayesian Optimization. It tackles challenges posed by combinatorial complexity and computationally expensive fun…

Boost machine learning model robustness by minimizing a novel data-driven risk criterion that blends Bayesian nonparametrics and smooth ambiguity aversion, ensuring superior out-of-sample performance.

This study solves a key challenge in neural network learning, deriving a closed-form expression for the Bayes-optimal test error of extensive-width networks with quadratic activation functions from qu…

BEEBO: a novel acquisition function for Bayesian Optimization, offering superior explore-exploit balance and handling large batches efficiently, even with noisy data.

Randomness-efficient algorithms are developed for online decision making, requiring only 2log n random bits and achieving near-optimal competitiveness for metrical task systems.

MAXMINLCB, a novel game-theoretic algorithm, efficiently solves bandit problems with preference feedback over continuous domains, providing anytime-valid, rate-optimal regret guarantees.

This paper reveals the optimal mistake bounds for online multiclass classification under bandit feedback, showing the cost of limited feedback is at most O(k) times higher than full information, where…

This paper revolutionizes AI alignment by applying social choice theory axioms to RLHF, exposing flaws in existing methods and proposing novel, axiomatically guaranteed reward learning rules.

Deep Neural Collapse (DNC) explained via Average Gradient Outer Product (AGOP).

This study presents a novel single-step outlier rectification method using optimal transport with a concave cost function, surpassing the limitations of conventional two-stage approaches by jointly op…

More sophisticated autobidders surprisingly worsen online auction efficiency; a fine-grained analysis reveals that less powerful, uniform bidders lead to better market outcomes.

This paper rigorously analyzes alpha-divergence variational inference, proving its convergence and providing convergence rates, thereby advancing the theoretical foundations of this increasingly impor…