Papers
Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
Gergely Neu, Nneka Okolo
Debating with More Persuasive LLMs Leads to More Truthful Answers
Akbir Khan, John Hughes, Dan Valentine et al.
Debiased Distribution Compression
Lingxiao Li, Raaz Dwivedi, Lester Mackey
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
Xinyu Zhang, Wenjie Qiu, Yi-Chen Li et al.
Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
Yuxing Liu, Lesi Chen, Luo Luo
Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
Cheng Tan, Zhangyang Gao, Hanqun Cao et al.
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Jianxiong Li, Jinliang Zheng, Yinan Zheng et al.
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Junyuan Hong, Jinhao Duan, Chenhui Zhang et al.
Decoding-time Realignment of Language Models
Tianlin Liu, Shangmin Guo, Leonardo Bianco et al.
Decomposing and Editing Predictions by Modeling Model Computation
Harshay Shah, Andrew Ilyas, Aleksander Madry
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Bairu Hou, Yujian Liu, Kaizhi Qian et al.
Deconstructing the Goldilocks Zone of Neural Network Initialization
Artem M Vysogorets, Anna Dawid, Julia Kempe
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
Zhi Zhou, Ming Yang, Jiang-Xin Shi et al.
DE-COP: Detecting Copyrighted Content in Language Models Training Data
André Vicente Duarte, Xuandong Zhao, Arlindo L. Oliveira et al.
Decouple then Classify: A Dynamic Multi-view Labeling Strategy with Shared and Specific Information
Xinhang Wan, Jiyuan Liu, Xinwang Liu et al.
Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks
Mikkel Jordahn, Pablo M. Olmos
Decoupling Learning and Decision-Making: Breaking the $\mathcalO(\sqrtT)$ Barrier in Online Resource Allocation with First-Order Methods
Wenzhi Gao, Chunlin Sun, Chenyu Xue et al.
Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration
Xiong-Hui Chen, Junyin Ye, Hang Zhao et al.
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Zenan Ling, Longbo Li, Zhanbo Feng et al.
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Yahong Yang, Juncai He
Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
Yirui Liu, Xinghao Qiao, Yulong Pei et al.
Deep Fusion: Efficient Network Training via Pre-trained Initializations
Hanna Mazzawi, Javier Gonzalvo, Michael Wunder et al.
Deep Networks Always Grok and Here is Why
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
Deep Neural Room Acoustics Primitive
Yuhang He, Anoop Cherian, Gordon Wichern et al.