Since the creation of the Web, recommender systems (RSs) have been an indispensable
personalization mechanism in information filtering. Most state-of-the-art RSs primarily …

We design learning rate schedules that minimize regret for SGD-based online learning in
the presence of a changing data distribution. We fully characterize the optimal learning rate …

Recommendation models rely on deep learning networks and large embedding tables,
resulting in computationally and memory-intensive processes. These models are typically …

This work studies algorithms for learning from aggregate responses. We focus on the
construction of aggregation sets (called bags in the literature) for event-level loss functions …

Embedding tables are used by machine learning systems to work with categorical features.
In modern Recommendation Systems, these tables can be very large, necessitating the …

" Garbage in, Garbage out" is a familiar maxim to ML practitioners and researchers, because
the quality of a learned data representation is highly crucial to the quality of any ML model …

Deep recommender systems rely heavily on large embedding tables to handle high-
cardinality categorical features such as user/item identifiers, and face significant memory …

Deep neural networks bear substantial cloud computational loads and often surpass client
devices' capabilities. Research has concentrated on reducing the inference burden of …

Information Retrieval (IR) systems used in search and recommendation platforms frequently
employ Learning-to-Rank (LTR) models to rank items in response to user queries. These …