arXiv subjects

Tal Sarig

Publications and source records attributed to Tal Sarig.

4 recordsLinked to original sources

arXiv2026

Exact Unlearning via Quantized Sufficient Statistics

Exact unlearning requires a deployed predictor to match one rebuilt without the information named by a deletion request. Existing general-purpose exact methods localize retraining through disjoint shards, but every request still invalidates a model, and smaller shards reduce the data available to each constituent predictor. We introduce Quantized Sufficient Statistics (QSS), which separates a small frozen schema from mutable, sum-decomposable content. The schema learns global structure; the content stores local prediction corrections as additive statistics indexed by quantized regions. Deleting content is therefore exact subtraction rather than optimization. We distinguish two guarantees: QSS-L exactly removes a label while retaining the unlabelled input, whereas QSS-E exactly removes both input and label by learning the schema without deletable examples. A deletion takes the arithmetic fast path with probability $1-ρ$ and triggers a full rebuild with probability $ρ$; all reported expected latencies include both events. Across 15 vision, text, and tabular datasets at $ρ=0.5\%$, QSS-L is within 2 percentage points of SISA on 11 tasks and provides 4--483$\times$ lower expected deletion latency on the low-class-count tasks where a compact schema is effective. QSS-E quantifies the additional accuracy cost of removing every trace of an input.

cs.LG↗

arXiv2026

Bandits via Additive Quantized Representations

Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles and neural methods capture nonlinearities but require periodic retraining and large replay buffers. Linear models update efficiently per observation with O(1) memory, but are fundamentally restricted to linear reward structures. We propose Residual Quantization (RQ) as a representation layer to bridge this gap. An offline-trained RQ codebook maps continuous contexts into discrete centroid assignments across multiple levels, set dynamically through a shadow mechanism. This enables a spectrum of additive bandit algorithms that achieve nonlinear expressivity with strictly bounded memory. Across 13 datasets, RQ variants beat their non-RQ counterparts on 11 of 13 datasets, often by wide margins, while matching doubling-retrain XGBoost and neural baselines using up to 1000 times less memory.

cs.LG↗

arXiv2023

balance -- a Python package for balancing biased data samples

Surveys are an important research tool, providing unique measurements on subjective experiences such as sentiment and opinions that cannot be measured by other means. However, because survey data is collected from a self-selected group of participants, directly inferring insights from it to a population of interest, or training ML models on such data, can lead to erroneous estimates or under-performing models. In this paper we present balance, an open-source Python package by Meta, offering a simple workflow for analyzing and adjusting biased data samples with respect to a population of interest. The balance workflow includes three steps: understanding the initial bias in the data relative to a target we would like to infer, adjusting the data to correct for the bias by producing weights for each unit in the sample based on propensity scores, and evaluating the final biases and the variance inflation after applying the fitted weights. The package provides a simple API that can be used by researchers and data scientists from a wide range of fields on a variety of data. The paper provides the relevant context, methodological background, and presents the package's API.

stat.CO↗

arXiv2020

Weights and Methodology Brief for the COVID-19 Symptom Survey by University of Maryland and Carnegie Mellon University, in Partnership with Facebook

Facebook is partnering with academic institutions to support COVID-19 research. Currently, we are inviting Facebook app users in the United States to take a survey collected by faculty at Carnegie Mellon University (CMU) Delphi Research Center, and we are inviting Facebook app users in more than 200 countries or territories globally to take a survey collected by faculty at the University of Maryland (UMD) Joint Program in Survey Methodology (JPSM). As part of this initiative, we are applying best practices from survey statistics to design and execute two components: (1) sampling design and (2) survey weights, which make the sample more representative of the general population. This paper describes the methods we used in these efforts in order to allow data users to execute their analyses using the weights.

cs.SI↗