106 citations · 118 across the 11 of their papers we have counts for
12 papers
Bayesian Optimization with Rich Auxiliary Information via LLMs
Tejus Gupta, Efe Mert Karagözlü, Rohit Sonker +2
Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet it typically reduces each expensive experiment to an input and its objective value, disc…
Making Every Tool Call Count: Necessary Tool-Evidence Path Rewards for Agentic Vision-Language Models
Xingming Long, Yu Liu, Zhiwei Yang +7
Modern vision-language models (VLMs) can directly answer many image-grounded questions, yet they often struggle with complex queries requiring fine-grained visual details or extern…
CONTRAMEM: Learning Self-Evolving Procedural Memory from Contrasting Multi-Model Trajectories
Zheyuan Deng, Binghang Lu, Hanqi Feng +11
Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and verifier-sensitive writes, yet…
CARE: Pre-Execution Command Verification for Shell-Executing LLM Agents
Yu Liu, Wenxiao Zhang, Zhiwei Yang +7
Large Language Model (LLM) agents are increasingly used for coding and terminal automation, making shell-command dispatch a high-stakes runtime control point. We study command-leve…
Efficient Convexification of Kolmogorov-Arnold Networks with Polynomial Functional Forms Via a Continuous Graham Scan Approach
Tianwei Li, Daniel Ovalle, Barnabas Poczos +3
Deterministic global optimization of nonlinear models is important in many scientific and engineering applications. This framework typically involves repeatedly solving convex rela…
Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation
Adrita Das, Peiran Jiang, Dantong Zhu +2
Diffusion models have emerged as a powerful class of generative models for molecular design, capable of capturing complex structural distributions and achieving high fidelity in 3D…
Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization
Hanqi Feng, Peng Qiu, Mengchun Zhang +4
Recent advances in diffusion models have shown remarkable potential for antibody design, yet existing approaches apply uniform generation strategies that cannot adapt to each antig…
AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration
Peng Qiu, Hanqi Feng, Meng-Chun Zhang +1
Red-blood-cell lysis (HC50) is the principal safety barrier for antimicrobial-peptide (AMP) therapeutics, yet existing models only say "toxic" or "non-toxic." AmpLyze closes this g…
Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design
Amira Alakhdar, Barnabas Poczos, Newell Washburn
Developing bioactive molecules remains a central, time- and cost-heavy challenge in drug discovery, particularly for novel targets lacking structural or functional data. Pharmacoph…
Diffusion Models in Drug Design
Amira Alakhdar, Barnabas Poczos, Newell Washburn
Diffusion models have emerged as powerful tools for molecular generation, particularly in the context of 3D molecular structures. Inspired by non-equilibrium statistical physics, t…
Controllable Text Generation in the Instruction-Tuning Era
Dhananjay Ashok, Barnabas Poczos
While most research on controllable text generation has focused on steering base Language Models, the emerging instruction-tuning and prompting paradigm offers an alternate approac…
Undercomplete Blind Subspace Deconvolution via Linear Prediction
Zoltan Szabo, Barnabas Poczos, Andras Lorincz
We present a novel solution technique for the blind subspace deconvolution (BSSD) problem, where temporal convolution of multidimensional hidden independent components is observed…