12 citations · 15 across the 8 of their papers we have counts for
11 papers
From Valid to Useful: Post-Verification Acquisition for Recursive Self-Improving Recommendation
Tonmoy Hasan, Taylor Foust, Shao Tang +6
Sequential recommenders can generate synthetic interaction sequences and retrain on the augmented corpus in a recursive self-improvement loop. To limit error accumulation, current…
Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale
Edesio Alcoba, Kevin Rossell, Aman Gupta +13
Customer experience (CX) agents use tools and large language models to address customer requests and guide conversational interactions with an organization's products. Improving th…
Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework
Aman Gupta, Kevin Rossell, Edesio Alcobaça +8
The rapid rise in LLM capabilities has made AI agents increasingly viable across a broad range of tasks. Among the most promising applications is building production-ready customer…
Effective Quantization of Muon Optimizer States
Aman Gupta, Rafael Celente, Abhishek Shivanna +7
The Muon optimizer, based on matrix orthogonalization, has recently shown faster convergence and better computational efficiency over AdamW in LLM pre-training. However, the memory…
Your Spending Needs Attention: Modeling Financial Habits with Transformers
D. T. Braithwaite, Misael Cavalcanti, R. Austin McEver +9
Predictive models play a crucial role in the financial industry, enabling risk prediction, fraud detection, and personalized recommendations, where slight changes in core model per…
Logit Attenuating Weight Normalization
Aman Gupta, Rohan Ramanath, Jun Shi +4
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems. Without appropriately tuned …
Efficient Vertex-Oriented Polytopic Projection for Web-scale Applications
Rohan Ramanath, S. Sathiya Keerthi, Yao Pan +2
We consider applications involving a large set of instances of projecting points to polytopes. We develop an intuition guided by theoretical and empirical analysis to show that whe…
Lambda Learner: Fast Incremental Learning on Data Streams
Rohan Ramanath, Konstantin Salomatin, Jeffrey D. Gee +5
One of the most well-established applications of machine learning is in deciding what content to show website visitors. When observation data comes from high-velocity, user-generat…
An Attentive Survey of Attention Models
Sneha Chaudhari, Varun Mithal, Gungor Polatkan +1
Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehens…
Towards Deep and Representation Learning for Talent Search at LinkedIn
Rohan Ramanath, Hakan Inan, Gungor Polatkan +6
Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search…
Deploying Deep Ranking Models for Search Verticals
Rohan Ramanath, Gungor Polatkan, Liqin Xu +3
In this paper, we present an architecture executing a complex machine learning model such as a neural network capturing semantic similarity between a query and a document; and depl…