1 citations · 1 across the 9 of their papers we have counts for
11 papers
UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding
Chumeng Liang, Linxuan Wang, Xinyu Peng +6
Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters have achieved breakthrough perf…
Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets
Hyunjoo Kim, Sicheng Wu, Agastya Venkatraman +2
Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models. One impo…
Generalized Discrete Diffusion with Self-Correction
Linxuan Wang, Ziyi Wang, Yikun Bai +3
Self-correction is an effective technique for maintaining parallel sampling in discrete diffusion models with minimal performance degradation. Prior work has explored self-correcti…
f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment
Rajdeep Haldar, Lantao Mei, Guang Lin +2
Recent work shows that preference alignment objectives can be interpreted as divergence estimators between aligned (preferred) & unaligned (less-preferred) distributions, yielding…
Task-tailored Pre-processing: Fair Downstream Supervised Learning
Jinwon Sohn, Guang Lin, Qifan Song
Fairness-aware machine learning has recently attracted various communities to mitigate discrimination against certain societal groups in data-driven tasks. For fair supervised lear…
Trusted Multi-view Learning for Long-tailed Classification
Chuanqing Tang, Yifei Shi, Guanghao Lin +2
Class imbalance has been extensively studied in single-view scenarios; however, addressing this challenge in multi-view contexts remains an open problem, with even scarcer research…
SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions
Ziyi Wang, Nan Jiang, Guang Lin +1
Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight pruning or low-bit quantization indivi…
LLM Safety Alignment is Divergence Estimation in Disguise
Rajdeep Haldar, Ziyi Wang, Qifan Song +2
We present a theoretical framework showing that popular LLM alignment methods, including RLHF and its variants, can be understood as divergence estimators between aligned (safe or…
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility
Rajdeep Haldar, Yue Xing, Qifan Song +1
Recent works have shown theoretically and empirically that redundant data dimensions are a source of adversarial vulnerability. However, the inverse doesn't seem to hold in practic…
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
Jiajun Liang, Qian Zhang, Wei Deng +2
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimat…
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
Jinwon Sohn, Qifan Song, Guang Lin
As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas. This work proposes fai…