Replicability is Asymptotically Free in Multi-armed Bandits
This work is motivated by the growing demand for reproducible machine learning. We study the stochastic multi-armed bandit problem. In particular, we consider a replicable algorithm that ensures, with...
View ArticleThe Limits of Assumption-free Tests for Algorithm Performance
Algorithm evaluation and comparison are fundamental questions in machine learning and statistics -- how well does an algorithm perform at a given modeling task, and which algorithm performs best? Many...
View ArticleExploring Perceptual Limitation of Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual capability in answering visual questions, however, little is known about the limits of their perception. In...
View ArticleMaking Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like
Laughter is one of the most expressive and natural aspects of human speech, conveying emotions, social cues, and humor. However, most text-to-speech (TTS) systems lack the ability to produce realistic...
View ArticleA Deep Learning Method for Optimal Investment Under Relative Performance...
Graphon games have been introduced to study games with many players who interact through a weighted graph of interaction. By passing to the limit, a game with a continuum of players is obtained, in...
View ArticleStrategically-Robust Learning Algorithms for Bidding in First-Price Auctions
Learning to bid in repeated first-price auctions is a fundamental problem at the interface of game theory and machine learning, which has seen a recent surge in interest due to the transition of...
View ArticleRegression Trees for Fast and Adaptive Prediction Intervals
Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create statistically valid...
View ArticleSampling from the Mean-Field Stationary Distribution
We study the complexity of sampling from the stationary distribution of a mean-field SDE, or equivalently, the complexity of minimizing a functional over the space of probability measures which...
View ArticleNoise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian...
Adapting to a priori unknown noise level is a very important but challenging problem in sequential decision-making as efficient exploration typically requires knowledge of the noise level, which is...
View ArticleDifferentially Private Training of Mixture of Experts Models
This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As Large Language...
View ArticleLessons Learned from Mining the Hugging Face Repository
The rapidly evolving fields of Machine Learning (ML) and Artificial Intelligence have witnessed the emergence of platforms like Hugging Face (HF) as central hubs for model development and sharing. This...
View ArticleTowards Explainable, Safe Autonomous Driving with Language Embeddings for...
This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that...
View ArticleBioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis
This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on the network weights...
View ArticleSelf-Consistent Conformal Prediction
In decision-making guided by machine learning, decision-makers often take identical actions in contexts with identical predicted outcomes. Conformal prediction helps decision-makers quantify outcome...
View ArticleOn the Effectiveness of Machine Learning-based Call Graph Pruning: An...
Static call graph (CG) construction often over-approximates call relations, leading to sound, but imprecise results. Recent research has explored machine learning (ML)-based CG pruning as a means to...
View ArticleCLIPPER: Robust Data Association without an Initial Guess
Identifying correspondences in noisy data is a critically important step in estimation processes. When an informative initial estimation guess is available, the data association challenge is less...
View ArticleHow do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
In day-to-day communication, people often approximate the truth - for example, rounding the time or omitting details - in order to be maximally helpful to the listener. How do large language models...
View ArticleOpen-ended VQA benchmarking of Vision-Language models by exploiting...
The evaluation of text-generative vision-language models is a challenging yet crucial endeavor. By addressing the limitations of existing Visual Question Answering (VQA) benchmarks and proposing...
View ArticleHighly Accurate Disease Diagnosis and Highly Reproducible Biomarker...
Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks...
View ArticleSynergizing Spatial Optimization with Large Language Models for Open-Domain...
In this paper, we for the first time propose the task of Open-domain Urban Itinerary Planning (OUIP) for citywalk, which directly generates itineraries based on users' requests described in natural...
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