75 citations · 76 across the 25 of their papers we have counts for
29 papers
Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion
Renxiang Huang, Yuchen Liang, Yingbin Liang +2
Discrete diffusion models have become a powerful framework for modeling complex discrete distributions across a range of applications. While theoretical convergence guarantees depe…
Communication-Aware Model Distributed Inference via Latent Representation Compression
Peyman Gholami, Theodoros-Thirimachos Davarakis, Teng Li +9
We study optimization of distributed model inference over resource-constrained edge resources. We propose a framework that optimizes the trade-off between model accuracy and commun…
Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
Linfeng Cao, Ming Shi, Ness B. Shroff
Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depends on both unknown rewards and…
From Scores to Gibbs Correctors: Accelerating Uniform-Rate Discrete Diffusion Models
Yuchen Liang, Ness Shroff, Yingbin Liang
Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, but, especially for uniform-rate models, they often require many steps to g…
Regret Bounds for Reinforcement Learning from Multi-Source Imperfect Preferences
Ming Shi, Yingbin Liang, Ness B. Shroff +1
Reinforcement learning from human feedback (RLHF) replaces hard-to-specify rewards with pairwise trajectory preferences, yet regret-oriented theory often assumes that preference la…
An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability
Ashutosh Soni, Peizhong Ju, Atilla Eryilmaz +1
We study the stochastic multi-armed bandit (MAB) problem where an underlying network structure enables side-observations across related actions. We use a bipartite graph to link ac…
Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration
Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici +1
While offline reinforcement learning provides reliable policies for real-world deployment, its inherent pessimism severely restricts an agent's ability to explore and collect novel…
Sharp Convergence Rates for Masked Diffusion Models
Yuchen Liang, Zhiheng Tan, Ness Shroff +1
Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, with masked (absorbing-rate) variants emerging as competitive alternatives…
Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
Yining Li, Peizhong Ju, Ness Shroff
Reinforcement Learning from Human Feedback (RLHF) plays a significant role in aligning Large Language Models (LLMs) with human preferences. While RLHF with expected reward constrai…
Constraint-Rectified Training for Efficient Chain-of-Thought
Qinhang Wu, Sen Lin, Ming Zhang +2
Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), especially when combined with reinforcement learning (RL) based post-t…
Learnable Chernoff Baselines for Inference-Time Alignment
Sunil Madhow, Yuchen Liang, Ness Shroff +2
We study inference-time reward-guided alignment for generative models. Existing methods often rely on either architecture-specific adaptations or computationally costly inference p…
Discrete Diffusion Models: Novel Analysis and New Sampler Guarantees
Yuchen Liang, Yingbin Liang, Lifeng Lai +1
Discrete diffusion models have recently gained significant prominence in applications involving natural language and graph data. A key factor influencing their effectiveness is the…
Mixture-of-Transformers Learn Faster: A Theoretical Study on Classification Problems
Hongbo Li, Qinhang Wu, Sen Lin +2
Mixture-of-Experts (MoE) models improve transformer efficiency but lack a unified theoretical explanation, especially when both feed-forward and attention layers are allowed to spe…
Monitoring State Transitions in Markovian Systems with Sampling Cost
Kumar Saurav, Ness B. Shroff, Yingbin Liang
We consider a node-monitor pair, where the node's state varies with time. The monitor needs to track the node's state at all times; however, there is a fixed cost for each state qu…
Large Language Models Achieve Gold Medal Performance at the International Olympiad on Astronomy & Astrophysics (IOAA)
Lucas Carrit Delgado Pinheiro, Ziru Chen, Bruno Caixeta Piazza +4
While task-specific demonstrations show early success in applying large language models (LLMs) to automate some astronomical research tasks, they only provide incomplete views of a…
Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics
Mohamed A. Abd-Elmagid, Ming Shi, Eylem Ekici +1
We consider a real-time monitoring system where a source node (with energy limitations) aims to keep the information status at a destination node as fresh as possible by scheduling…
Optimal Parallel Scheduling under Concave Speedup Functions
Chengzhang Li, Peizhong Ju, Atilla Eryilmaz +1
Efficient scheduling of parallel computation resources across multiple jobs is a fundamental problem in modern cloud/edge computing systems for many AI-based applications. Allocati…
Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models
Yuchen Liang, Renxiang Huang, Lifeng Lai +2
Discrete state space diffusion models have shown significant advantages in applications involving discrete data, such as text and image generation. It has also been observed that t…
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective
Junze Deng, Qinhang Wu, Peizhong Ju +3
Rehearsal-based methods have shown superior performance in addressing catastrophic forgetting in continual learning (CL) by storing and training on a subset of past data alongside…
Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces
Amirhossein Roknilamouki, Arnob Ghosh, Ming Shi +3
In Reinforcement Learning (RL), tasks with instantaneous hard constraints present significant challenges, particularly when the decision space is non-convex or non-star-convex. Thi…
Performing Load Balancing under Constraints
Andrea Fox, Francesco De Pellegrini, Eitan Altman +2
Join-the-shortest queue (JSQ) and its variants have often been used in solving load balancing problems. The aim of such policies is to minimize the average system occupation, e.g.,…
Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization
Linfeng Cao, Ming Shi, Ness B. Shroff
Multi-objective multi-armed bandit (MO-MAB) problems traditionally aim to achieve Pareto optimality. However, real-world scenarios often involve users with varying preferences acro…
BeST -- A Novel Source Selection Metric for Transfer Learning
Ashutosh Soni, Peizhong Ju, Atilla Eryilmaz +1
One of the most fundamental, and yet relatively less explored, goals in transfer learning is the efficient means of selecting top candidates from a large number of previously train…
Artificial Intelligence of Things: A Survey
Shakhrul Iman Siam, Hyunho Ahn, Li Liu +7
The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In t…