23 citations · 106 across the 25 of their papers we have counts for
16 papers · 1 filter
Private Learning with Public Feature Conditioning
Shuli Jiang, Walid Krichene, Nicolas Mayoraz
We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features -- common in applications such as recommendation and adve…
MAC-Attention: a Match-Amend-Complete Scheme for Fast and Accurate Attention Computation
Jinghan Yao, Sam Adé Jacobs, Walid Krichene +2
Long-context decoding in LLMs is IO-bound: each token re-reads an ever-growing KV cache. Prior accelerations cut bytes via compression, which lowers fidelity, or selection/eviction…
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features
Lynn Chua, Qiliang Cui, Badih Ghazi +9
Motivated by problems arising in digital advertising, we introduce the task of training differentially private (DP) machine learning models with semi-sensitive features. In this se…
Private Learning with Public Features
Walid Krichene, Nicolas Mayoraz, Steffen Rendle +3
We study a class of private learning problems in which the data is a join of private and public features. This is often the case in private personalization tasks such as recommenda…
Multi-Task Differential Privacy Under Distribution Skew
Walid Krichene, Prateek Jain, Shuang Song +3
We study the problem of multi-task learning under user-level differential privacy, in which users contribute data to tasks, each involving a subset of users. One important…
Differentially Private Image Classification from Features
Harsh Mehta, Walid Krichene, Abhradeep Thakurta +2
Leveraging transfer learning has recently been shown to be an effective strategy for training large models with Differential Privacy (DP). Moreover, somewhat surprisingly, recent w…
Reciprocity in Machine Learning
Mukund Sundararajan, Walid Krichene
Machine learning is pervasive. It powers recommender systems such as Spotify, Instagram and YouTube, and health-care systems via models that predict sleep patterns, or the risk of…
ALX: Large Scale Matrix Factorization on TPUs
Harsh Mehta, Steffen Rendle, Walid Krichene +1
We present ALX, an open-source library for distributed matrix factorization using Alternating Least Squares, written in JAX. Our design allows for efficient use of the TPU architec…
iALS++: Speeding up Matrix Factorization with Subspace Optimization
Steffen Rendle, Walid Krichene, Li Zhang +1
iALS is a popular algorithm for learning matrix factorization models from implicit feedback with alternating least squares. This algorithm was invented over a decade ago but still…
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene +4
We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Sq…
Superbloom: Bloom filter meets Transformer
John Anderson, Qingqing Huang, Walid Krichene +2
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash to…
Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Francois Belletti, Karthik Lakshmanan, Walid Krichene +7
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap, we propose…
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games
Maximilian Balandat, Walid Krichene, Claire Tomlin +1
We study a general version of the adversarial online learning problem. We are given a decision set in a reflexive Banach space and a sequence of reward vectors in…
Dual Averaging on Compactly-Supported Distributions And Application to No-Regret Learning on a Continuum
Walid Krichene
We consider an online learning problem on a continuum. A decision maker is given a compact feasible set , and is faced with the following sequential problem: at iteration~, t…
Learning Nash Equilibria in Congestion Games
Walid Krichene, Benjamin Drighès, Alexandre M. Bayen
We study the repeated congestion game, in which multiple populations of players share resources, and make, at each iteration, a decentralized decision on which resources to utilize…
Arriving on time: estimating travel time distributions on large-scale road networks
Timothy Hunter, Aude Hofleitner, Jack Reilly +5
Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which req…