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20182026
most citedOptimal Secure Control with Linear Temporal Logic Constraints

14 citations · 72 across the 41 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

Polyhedral Instability Governs Regret in Online Learning

Yuetai Li, Fengqing Jiang, Yichen Feng +6

Many online decision problems over combinatorial actions are addressed via convex relaxations, leading to online convex optimization with piecewise linear objectives and induced po…

cs.LG2026

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks

Fengqing Jiang, Yuetai Li, Yichen Feng +6

Hypergraphs provide a natural framework to model higher-order interactions in scientific, social, and biological systems. Hypergraph neural networks (HGNNs) aim to learn from such…

cs.LG2025

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Zhangchen Xu, Yuetai Li, Fengqing Jiang +4

Reinforcement Learning (RL) has become a powerful tool for enhancing the reasoning abilities of large language models (LLMs) by optimizing their policies with reward signals. Yet,…

cs.LG2025

SoSBench: Benchmarking Safety Alignment on Six Scientific Domains

Fengqing Jiang, Fengbo Ma, Zhangchen Xu +8

Large language models (LLMs) exhibit advancing capabilities in complex tasks, such as reasoning and graduate-level question answering, yet their resilience against misuse, particul…

cs.LG2024

Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning

Zhangchen Xu, Fengqing Jiang, Luyao Niu +2

Federated learning (FL) enables multiple participants to train a global machine learning model without sharing their private training data. Peer-to-peer (P2P) FL advances existing…

cs.LG2022

LDL: A Defense for Label-Based Membership Inference Attacks

Arezoo Rajabi, Dinuka Sahabandu, Luyao Niu +2

The data used to train deep neural network (DNN) models in applications such as healthcare and finance typically contain sensitive information. A DNN model may suffer from overfitt…

cs.LG2021

Reinforcement Learning Beyond Expectation

Bhaskar Ramasubramanian, Luyao Niu, Andrew Clark +1

The inputs and preferences of human users are important considerations in situations where these users interact with autonomous cyber or cyber-physical systems. In these scenarios,…