AI Theory

Researchers derived tighter generalization bounds for label-specific representation learning (LSRL) methods, improving understanding of LSRL’s success and offering guidance for future algorithm develo…

Unlocking deep learning’s generalization mystery, this research pioneers a theoretical understanding of memorization neural network generalizability, revealing critical network structural requirements…

New theoretical bounds on Bayesian coreset approximation errors enable efficient large-scale Bayesian inference, overcoming prior limitations and improving coreset construction methods.

This research establishes fundamental convergence properties for the widely-used SAM optimization algorithm, significantly advancing our theoretical understanding and practical applications.

Near-optimal algorithms solve convex simple bilevel problems by reformulating them into functionally constrained problems, achieving near-optimal convergence rates.

FUGAL: a groundbreaking graph alignment method surpassing state-of-the-art accuracy without compromising efficiency by directly aligning adjacency matrices.

A novel framework extends optimization algorithms from linear/quadratic functions to a broader class of ‘upper-linearizable’ functions, providing a unified approach for concave and DR-submodular optim…

This paper introduces a novel geometric approach to concept-based representation learning, provably recovering interpretable concepts from diverse data without strict causal assumptions or many interv…

First-order methods conquer linearly constrained bilevel optimization, achieving near-optimal convergence rates and enhancing high-dimensional applicability.

FERERO, a novel framework, tackles multi-objective learning by efficiently finding preference-guided Pareto solutions using flexible preference modeling and convergent algorithms.

FEEL-SNN enhances spiking neural network robustness by mimicking biological visual attention and adaptive leak factors, resulting in improved resilience against noise and attacks.

Brain-inspired recurrent neural networks learn efficiently by using feedback control to approximate optimal gradients, enabling rapid movement corrections and efficient adaptation to persistent errors…

Speed up repeated evasion attacks on tree ensembles by 36x using feature perturbation insights!

Faster differentially private top-k selection achieved via a novel joint exponential mechanism with pruning, reducing time complexity from O(dk) to O(d+k²/ɛlnd).

Faster algorithms achieve optimal excess risk in user-level private stochastic convex optimization, overcoming limitations of prior methods without restrictive assumptions.

Faster primal-dual algorithms achieve order-optimal complexity for convex optimization with strongly convex constraints, improving convergence rates and solving large-scale problems efficiently.

Fast Tree-Field Integrators (FTFIs) revolutionize graph processing by enabling polylog-linear time computation for integrating tensor fields on trees, providing significant speedups for various machin…

Fast T2T: Optimization Consistency Boosts Diffusion-Based Combinatorial Optimization!

This paper introduces a novel approach for fast rates in online convex optimization by exploiting the curvature of feasible sets, achieving logarithmic regret bounds under specific conditions.

This paper presents efficient algorithms for designing cost-optimal proxy experiments to identify causal effects, significantly improving upon prior methods.