9 papers
LeHami: Symplectic Latent Dynamics for World Models
Romina Yalovetzky, Randall Balestriero, Yann LeCun
Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract the latent distribution over…
Enhanced localized conformal prediction with imperfect auxiliary information
Yinjie Min, Liuhua Peng, Changliang Zou
There is growing interest in constructing conformal prediction sets that provide approximate or asymptotic conditional coverage guarantees, capturing local data heterogeneity. Howe…
LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
Xunye Tian, Zhijian Zhou, Liuhua Peng +1
Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-based feature representations)…
Efficient Federated Estimation and Inference for High-Dimensional Tail Index Regression
Haoyu Geng, Liuhua Peng, Changliang Zou +1
Tail index regression studies how covariates affect tail heaviness in heavy-tailed data. In many applications, data are distributed across heterogeneous sources, where direct pooli…
A Unified Theory of Conditional Coverage in Conformal Prediction with Applications
Yinjie Min, Liuhua Peng, Changliang Zou
Conformal prediction provides prediction sets with finite-sample marginal coverage, but many applications require coverage guarantees that adapt to individual test points, a subpop…
Learning U-Statistics with Active Inference
Xiaoning Wang, Yuyang Huo, Liuhua Peng +1
-statistics play a central role in statistical inference. In many modern applications, however, acquiring the labels required for -statistics is costly. Motivated by recent a…
Stable Localized Conformal Prediction via Transduction
Yinjie Min, Liuhua Peng, Changliang Zou
Existing evaluations of conformal prediction, such as prediction efficiency and test-conditional coverage, are defined in expectation over the calibration data. In practice, when o…
Spatial-Sign based High dimensional Change Point Inference
Jixuan Liu, Long Feng, Liuhua Peng +1
High-dimensional changepoint inference, adaptable to diverse alternative scenarios, has attracted significant attention in recent years. In this paper, we propose an adaptive and r…
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
Zhekai Du, Yinjie Min, Jingjing Li +5
Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may con…