Posters

Strategic agents gaming recommender systems is solved by a novel mechanism that incentivizes truthful behavior while minimizing regret, offering a solution to a key challenge in online learning.

This paper presents a novel probabilistic regret bound (PRB) framework for Bayesian optimization, replacing the traditional fixed-budget stopping rule with a criterion based on the probability of find…

STONE: A novel submodular optimization framework drastically cuts 3D object detection training costs by cleverly selecting the most informative LiDAR point cloud data for labeling, achieving state-of-…

Stochastic zeroth-order optimization of strongly convex functions with Lipschitz Hessian achieves optimal sample complexity, as proven by matching upper and lower bounds with a novel two-stage algorit…

Bias-free performative prediction is achieved using a novel lazy deployment scheme with SGD, handling non-convex loss functions.

New streaming algorithms for instrumental variable regression achieve fast convergence rates, solving the problem efficiently without matrix inversions or mini-batches, enabling real-time causal analy…

Stochastic Optimal Control Matching (SOCM) significantly reduces errors in stochastic optimal control by learning a matching vector field using a novel iterative diffusion optimization technique.

Researchers extended stochastic optimal control theory to infinite-dimensional spaces, enabling the creation of diffusion bridges for generative modeling in function spaces, demonstrating applications…

A novel algorithm significantly improves stochastic optimal control by accurately modeling sensorimotor noise, achieving substantially lower costs than current state-of-the-art solutions, particularly…

Stochastic Newton Proximal Extragradient (SNPE) achieves faster global and local convergence rates for strongly convex functions, improving upon existing stochastic Newton methods by requiring signifi…

Deep kernel machines now achieve 94.5% accuracy on CIFAR-10, matching neural networks, by using stochastic kernel regularization to improve generalization.

Stochastic extragradient with flip-flop shuffling & anchoring achieves provably faster convergence in minimax optimization.

Contextual bandits with graph feedback achieve near-optimal regret by leveraging a novel graph-theoretic quantity that interpolates between independence and maximum acyclic subgraph numbers, depending…

Stochastic Concept Bottleneck Models (SCBMs) revolutionize interpretable ML by efficiently modeling concept dependencies, drastically improving intervention effectiveness and enabling CLIP-based conce…

Stochastic Amortization accelerates feature and data attribution by training amortized models using noisy, yet unbiased, labels, achieving order-of-magnitude speedups over existing methods.

Human understanding of formal specifications for robot validation is surprisingly poor; active learning, while improving engagement, doesn’t significantly boost accuracy.

Stepwise Alignment for Constrained Policy Optimization (SACPO) efficiently aligns LLMs with human values, prioritizing both helpfulness and harmlessness via a novel stepwise approach.

Adaptive learning rate tuners often underperform; Curvature Dynamics Aware Tuning (CDAT) prioritizes long-term curvature stabilization, outperforming tuned constant learning rates.

On-device training with fixed-point forward gradients enables efficient model personalization on resource-constrained edge devices, overcoming backpropagation’s memory limitations.

StepbaQ enhances quantized diffusion models by correcting accumulated quantization errors via a novel sampling step correction mechanism, significantly improving model accuracy without modifying exist…