Posters

Time-Constrained Robust MDPs (TC-RMDPs) improve reinforcement learning by addressing limitations of traditional methods, offering a novel framework for handling real-world uncertainties and yielding m…

Networks trained on continuous sensory data spontaneously develop place cell-like responses, demonstrating that time-encoded experience can create spatial maps in the brain.

Shuffled SGD’s convergence is now better understood through a primal-dual analysis, yielding tighter bounds that align with its superior empirical performance.

This paper presents an algorithm achieving Õ(√T) optimal regret for bandit non-stochastic control with strongly-convex and smooth cost functions, overcoming prior limitations of suboptimal bounds.

Learning user preferences accurately from limited data is key; this paper shows that surprisingly small datasets suffice for precise prediction, and provides efficient algorithms to achieve this.

This paper introduces ‘Thought of Search,’ a novel, efficient planning approach using LLMs that prioritizes soundness and completeness. It leverages LLMs to generate Python code for search components,…

W-DBO efficiently tackles stale data in dynamic Bayesian Optimization by leveraging a novel Wasserstein distance-based criterion to remove irrelevant observations, maintaining high sampling frequency …

SPRY: A memory-efficient federated learning algorithm for finetuning LLMs on resource-constrained devices, achieving high accuracy and speed.

This research enhances meta-learning robustness by theoretically grounding and practically improving tail-risk minimization, achieving improved fast adaptation in the task space.

Novel theoretical guarantees for Diffusion Flow Matching (DFM) models are established, bounding the KL divergence under mild assumptions on data and base distributions.

Deep learning’s sequence modeling is revolutionized by selective state-space models (SSMs)! This paper provides theoretical grounding for their superior performance, revealing the crucial role of gati…

New theoretical bounds reveal how neural network architecture impacts the Gauss-Newton matrix’s conditioning, paving the way for improved optimization.

Researchers unveil the origins of degree bias in Graph Neural Networks (GNNs), proving high-degree nodes’ lower misclassification probability and proposing methods to alleviate this bias for fairer GN…

Strong student models can learn from weaker teachers, even correcting errors and generalizing beyond the teacher’s expertise. This paper provides new theoretical bounds explaining this ‘weak-to-strong…

Using real images retrieved from a generator’s training data outperforms using synthetic images generated by that same model for image classification.

PCA tree: a novel hierarchical dimensionality reduction model visualized using oblique trees and local PCAs, offering speed and interpretability.

Contrary to expectations, pre-trained visual representations surprisingly don’t improve model-based reinforcement learning’s sample efficiency or generalization; data diversity and network architectu…

TD learning surprisingly outperforms Monte Carlo methods for rare event prediction in Markov chains, achieving relative accuracy with polynomially, instead of exponentially, many observed transitions.

Partial preferences and noisy votes hinder accurate ranking recovery; this paper introduces scalable SP voting variants, empirically demonstrating superior performance in recovering ground truth ranki…

Star geometry reveals optimal data-driven regularizers!