Vahid Tarokh

Stable by Construction: Variational Latent Markov Operators for Long-Horizon PDE Prediction

Junyi Liao, Johann Guilleminot, Vahid Tarokh

Neural PDE solvers provide efficient surrogates for time-dependent physical systems, but autoregressive prediction over long horizons remains challenging because local errors can i…

cs.CV2026

Task-Based CT Protocol Optimization Using Reinforcement Learning and Virtual Imaging Trials

Jiaqi Zou, David Fenwick, Vahid Tarokh +6

Protocol optimization in computed tomography (CT) aims to improve diagnostic image quality while reducing radiation dose, but the interdependence of acquisition and reconstruction…

Diffusion-Based Data-Driven Assortment Optimization

Junyi Liao, Xiaohui Jiang, Zhengwei Tong +2

Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit (MNL) and its variants. Whi…

cs.CL2026

Rethinking Token Prediction: Tree-Structured Diffusion Language Model

Zihao Wu, Haoming Yang, Juncheng Dong +1

Discrete diffusion language models have emerged as a competitive alternative to auto-regressive language models, but training them efficiently under limited parameter and memory bu…

Learning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers

Juncheng Dong, Bowen He, Moyang Guo +3

In-context reinforcement learning (ICRL) leverages the in-context learning capabilities of transformer models (TMs) to efficiently generalize to unseen sequential decision-making t…

In-Context Reinforcement Learning From Suboptimal Historical Data

Juncheng Dong, Moyang Guo, Ethan X. Fang +2

Transformer models have achieved remarkable empirical successes, largely due to their in-context learning capabilities. Inspired by this, we explore training an autoregressive tran…

Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization

Junyi Liao, Zihan Zhu, Ethan Fang +2

Estimating the unknown reward functions driving agents' behaviors is of central interest in inverse reinforcement learning and game theory. To tackle this problem, we develop a uni…

CARE: Turning LLMs Into Causal Reasoning Expert

Juncheng Dong, Yiling Liu, Ahmed Aloui +2

Large language models (LLMs) have recently demonstrated impressive capabilities across a range of reasoning and generation tasks. However, research studies have shown that LLMs lac…

cs.AI2025

Neuro-Logic Lifelong Learning

Bowen He, Xiaoan Xu, Alper Kamil Bozkurt +2

Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Ar- tificial Intelligence (AI). While most research has focused on des…

cs.AI2025

STARK: Strategic Team of Agents for Refining Kernels

Juncheng Dong, Yang Yang, Tao Liu +5

The efficiency of GPU kernels is central to the progress of modern AI, yet optimizing them remains a difficult and labor-intensive task due to complex interactions between memory h…

Reinforcement Learning-Based Optimization of CT Acquisition and Reconstruction Parameters Through Virtual Imaging Trials

David Fenwick, Navid NaderiAlizadeh, Vahid Tarokh +7

Protocol optimization is critical in Computed Tomography (CT) to achieve high diagnostic image quality while minimizing radiation dose. However, due to the complex interdependencie…

PASTA: A Unified Framework for Offline Assortment Learning

Juncheng Dong, Weibin Mo, Zhengling Qi +3

We study a broad class of assortment optimization problems in an offline and data-driven setting. In such problems, a firm lacks prior knowledge of the underlying choice model, and…

Conditional Average Treatment Effect Estimation Under Hidden Confounders

Ahmed Aloui, Juncheng Dong, Ali Hasan +1

One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders. Since testing for h…

stat.ML2025

Boosting In-Context Learning in LLMs Through the Lens of Classical Supervised Learning

Korel Gundem, Juncheng Dong, Dennis Zhang +2

In-Context Learning (ICL) allows Large Language Models (LLMs) to adapt to new tasks with just a few examples, but their predictions often suffer from systematic biases, leading to…

Teleportation With Null Space Gradient Projection for Optimization Acceleration

Zihao Wu, Juncheng Dong, Ahmed Aloui +1

Optimization techniques have become increasingly critical due to the ever-growing model complexity and data scale. In particular, teleportation has emerged as a promising approach,…

S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time Series Forecasting

Zihao Wu, Juncheng Dong, Haoming Yang +1

Time series forecasting has recently achieved significant progress with multi-scale models to address the heterogeneity between long and short range patterns. Despite their state-o…

Score-Based Metropolis-Hastings Algorithms

Ahmed Aloui, Ali Hasan, Juncheng Dong +2

In this paper, we introduce a new approach for integrating score-based models with the Metropolis-Hastings algorithm. While traditional score-based diffusion models excel in accura…

Offline Stochastic Optimization of Black-Box Objective Functions

Juncheng Dong, Zihao Wu, Hamid Jafarkhani +2

Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search…

Learn2Mix: Training Neural Networks Using Adaptive Data Integration

Shyam Venkatasubramanian, Vahid Tarokh

Accelerating model convergence in resource-constrained environments is essential for fast and efficient neural network training. This work presents learn2mix, a new training strate…

Steinmetz Neural Networks for Complex-Valued Data

Shyam Venkatasubramanian, Ali Pezeshki, Vahid Tarokh

We introduce a new approach to processing complex-valued data using DNNs consisting of parallel real-valued subnetworks with coupled outputs. Our proposed class of architectures, r…