8 citations · 14 across the 16 of their papers we have counts for
17 papers
Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control
Zuyuan Zhang, Vaneet Aggarwal, Tian Lan
Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to optimize, while constraints ar…
On the Convergence of Self-Improving Online LLM Alignment
Xudong Wu, Pangpang Liu, Vaneet Aggarwal +1
The Self-Improving Alignment (SAIL) algorithm addresses distribution shift by reducing a bilevel formulation of the problem to an efficient, single-level method. Empirically, SAIL…
GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
Mengbo Wang, Shourya Verma, Aditya Malusare +6
Spatial transcriptomics (ST) technologies can be used to align transcriptomes with histopathological morphology, presenting exciting new opportunities for biomolecular discovery. U…
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
Aditya Malusare, Vineet Punyamoorty, Vaneet Aggarwal
Recent breakthroughs in generative modeling have demonstrated remarkable capabilities in molecular generation, yet the integration of comprehensive biomedical knowledge into these…
Contrastive Cross-Modal Learning for Infusing Chest X-ray Knowledge into ECGs
Vineet Punyamoorty, Aditya Malusare, Vaneet Aggarwal
Modern diagnostic workflows are increasingly multimodal, integrating diverse data sources such as medical images, structured records, and physiological time series. Among these, el…
Rack Position Optimization in Large-Scale Heterogeneous Data Centers
Chang-Lin Chen, Jiayu Chen, Tian Lan +3
As rapidly growing AI computational demands accelerate the need for new hardware installation and maintenance, this work explores optimal data center resource management by balanci…
BalancedDPO: Adaptive Multi-Metric Alignment
Dipesh Tamboli, Souradip Chakraborty, Aditya Malusare +3
Diffusion models have achieved remarkable progress in text-to-image generation, yet aligning them with human preference remains challenging due to the presence of multiple, sometim…
Network Diffuser for Placing-Scheduling Service Function Chains with Inverse Demonstration
Zuyuan Zhang, Vaneet Aggarwal, Tian Lan
Network services are increasingly managed by considering chained-up virtual network functions and relevant traffic flows, known as the Service Function Chains (SFCs). To deal with…
Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion
Vineet Punyamoorty, Pascal Jutras-Dubé, Ruqi Zhang +3
By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models a…
Variational Offline Multi-agent Skill Discovery
Jiayu Chen, Tian Lan, Vaneet Aggarwal
Skills are effective temporal abstractions established for sequential decision making, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task…
Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformer
Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran +2
Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learni…
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
Jiayu Chen, Bhargav Ganguly, Yang Xu +3
Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data. S…
Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model
Aditya Malusare, Vaneet Aggarwal
Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significa…
Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision
Aditya Malusare, Harish Kothandaraman, Dipesh Tamboli +2
This paper presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder-decoder Transformer…
Domain Adaptive Few-Shot Open-Set Learning
Debabrata Pal, Deeptej More, Sai Bhargav +3
Few-shot learning has made impressive strides in addressing the crucial challenges of recognizing unknown samples from novel classes in target query sets and managing visual shifts…
Coded Caching with Heterogeneous User Profiles
Ciyuan Zhang, Su Wang, Vaneet Aggarwal +1
Coded caching utilizes pre-fetching during off-peak hours and multi-casting for delivery in order to balance the traffic load in communication networks. Several works have studied…
Optimizing TCP Loss Recovery Performance Over Mobile Data Networks
Ke Liu, Zhongbin Zha, Wenkai Wan +3
Recent advances in high-speed mobile networks have revealed new bottlenecks in ubiquitous TCP protocol deployed in the Internet. In addition to differentiating non-congestive loss…