75 citations · 76 across the 25 of their papers we have counts for
12 papers · 1 filter
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…