Publications (17)
Active Learning with Simple Questions
Vasilis Kontonis, Mingchen Ma, Christos Tzamos
We consider an active learning setting where a learner is presented with a pool S of n unlabeled examples belonging to a domain X and asks queries to find the underlying labeling t…
Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise
Ilias Diakonikolas, Mingchen Ma, Lisheng Ren +1
We study the task of Multiclass Linear Classification (MLC) in the distribution-free PAC model with Random Classification Noise (RCN). Specifically, the learner is given a set of l…
Robust Regression of General ReLUs with Queries
Ilias Diakonikolas, Daniel M. Kane, Mingchen Ma
We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss. In the passive learning setti…
Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models
John Cooper, Ilias Diakonikolas, Mingchen Ma +1
Hybrid sequence models--combining Transformer and state-space model layers--seek to gain the expressive versatility of attention as well as the computational efficiency of state-sp…
Skilled AI Agents for Embedded and IoT Systems Development
Yiming Li, Yuhan Cheng, Mingchen Ma +6
Large language models (LLMs) and agentic systems have shown promise for automated software development, but applying them to hardware-in-the-loop (HIL) embedded and Internet-of-Thi…
ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis
Stephen Ni-Hahn, Chao Péter Yang, Mingchen Ma +3
Artificial Intelligence (AI) for music generation is undergoing rapid developments, with recent symbolic models leveraging sophisticated deep learning and diffusion model algorithm…
K-median: exact recovery in the extended stochastic ball model
Alberto Del Pia, Mingchen Ma
We study exact recovery conditions for the linear programming relaxation of the k-median problem in the stochastic ball model (SBM). In Awasthi et al. (2015), the authors give a ti…
Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals
Ilias Diakonikolas, Giannis Iakovidis, Mingchen Ma
We study the task of agnostic learning of multiclass linear classifiers under the Gaussian distribution. Given labeled examples from a distribution over $\mathbb{R}^d \tim…
Proximity in Concave Integer Quadratic Programming
Alberto Del Pia, Mingchen Ma
A classic result by Cook, Gerards, Schrijver, and Tardos provides an upper bound of on the proximity of optimal solutions of an Integer Linear Programming problem and its st…
AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling
Ancheng Xu, Di Yang, Renhao Li +13
Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential…
Learning Intersections of Two Margin Halfspaces under Factorizable Distributions
Ilias Diakonikolas, Mingchen Ma, Lisheng Ren +1
Learning intersections of halfspaces is a central problem in Computational Learning Theory. Even for just two halfspaces, it remains a major open question whether learning is possi…
Clustering with Queries under Semi-Random Noise
Alberto Del Pia, Mingchen Ma, Christos Tzamos
The seminal paper by Mazumdar and Saha \cite{MS17a} introduced an extensive line of work on clustering with noisy queries. Yet, despite significant progress on the problem, the pro…
Efficiently Learning Drifting Halfspaces with Massart Noise
Mingchen Ma, Guyang Cao, Jelena Diakonikolas +1
We study the problem of learning a drifting concept in the presence of Massart noise. In this framework, an online learner has access to a history of independent samples whose labe…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
Buying Information for Stochastic Optimization
Mingchen Ma, Christos Tzamos
Stochastic optimization is one of the central problems in Machine Learning and Theoretical Computer Science. In the standard model, the algorithm is given a fixed distribution know…
Active Learning of General Halfspaces: Label Queries vs Membership Queries
Ilias Diakonikolas, Daniel M. Kane, Mingchen Ma
We study the problem of learning general (i.e., not necessarily homogeneous) halfspaces under the Gaussian distribution on in the presence of some form of query access. In th…
IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol
Ningyuan Yang, Guanliang Lyu, Mingchen Ma +7
The integration of Large Language Models (LLMs) with Internet-of-Things (IoT) systems faces significant challenges in hardware heterogeneity and control complexity. The Model Conte…