8 citations · 39 across the 18 of their papers we have counts for
18 papers
Selective Matching Losses -- Not All Scores Are Created Equal
Gil I. Shamir, Manfred K. Warmuth
Learning systems match predicted scores to observations over some domain. Often, it is critical to produce accurate predictions in some subset (or region) of the domain, yet less i…
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo +15
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from…
Learning to Rank when Grades Matter
Le Yan, Zhen Qin, Gil Shamir +3
Graded labels are ubiquitous in real-world learning-to-rank applications, especially in human rated relevance data. Traditional learning-to-rank techniques aim to optimize the rank…
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
Rohan Anil, Sandra Gadanho, Da Huang +9
For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central problem. Ad clicks constitute a significant class of user engagements and are often…
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
Gil I. Shamir, Dong Lin
Real world recommendation systems influence a constantly growing set of domains. With deep networks, that now drive such systems, recommendations have been more relevant to the use…
Reproducibility in Optimization: Theoretical Framework and Limits
Kwangjun Ahn, Prateek Jain, Ziwei Ji +3
We initiate a formal study of reproducibility in optimization. We define a quantitative measure of reproducibility of optimization procedures in the face of noisy or error-prone op…