94 citations · 229 across the 34 of their papers we have counts for
36 papers
MatrixFormer: A Foundation Model for Matrix Completion
Dwaipayan Saha, Jacob Feitelberg, Kyuseong Choi +2
Matrix completion underlies problems from tabular imputation to causal inference, yet existing tabular foundation models treat it as entry-by-entry prediction, repeating context fo…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
Albert Gong, Kyuseong Choi, Abhineet Agarwal +5
Large language models can write, patch, and search code, but oncall root cause analysis (RCA) demands something different: reasoning over noisy metrics, logs, traces, and source co…
Express Language Modeling
Albert Gong, Annabelle Michael Carrell, Raaz Dwivedi +1
We introduce a new tool, Express, for converting a non-causal attention approximation into a causal approximation with matching approximation guarantees. When combined with the sta…
GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function
Brian M Cho, Raaz Dwivedi, Nathan Kallus
Inference on the conditional mean function (CMF) is central to tasks from adaptive experimentation to optimal treatment assignment and algorithmic fairness auditing. In this work,…
GOPO: Policy Optimization using Ranked Rewards
Kyuseong Choi, Dwaipayan Saha, Woojeong Kim +2
Standard reinforcement learning from human feedback (RLHF) trains a reward model on pairwise preference data and then uses it for policy optimization. However, while reward models…
Counterfactual Forecasting for Panel Data
Navonil Deb, Raaz Dwivedi, Sumanta Basu
We address the challenge of forecasting counterfactual outcomes in a panel data with missing entries and temporally dependent latent factors -- a common scenario in causal inferenc…
One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data
Jacob Feitelberg, Dwaipayan Saha, Kyuseong Choi +3
Missing data in tabular datasets forces practitioners into a hard choice: deploy a general-purpose imputer that may perform poorly for the problem at hand, or wait for someone to d…
N: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara +8
Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoretical guarantees, including entry-w…
Adaptively-weighted Nearest Neighbors for Matrix Completion
Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi
In this technical note, we introduce and analyze AWNN: an adaptively weighted nearest neighbor method for performing matrix completion. Nearest neighbor (NN) methods are widely use…
Low-Rank Thinning
Annabelle Michael Carrell, Albert Gong, Abhishek Shetty +2
The goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match t…
Cheap Permutation Testing
Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey
Permutation tests are a popular choice for distinguishing distributions and testing independence, due to their exact, finite-sample control of false positives and their minimax opt…
Two-Sided Nearest Neighbors: An adaptive and minimax optimal procedure for matrix completion
Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi
Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has…
Supervised Kernel Thinning
Albert Gong, Kyuseong Choi, Raaz Dwivedi
The kernel thinning algorithm of Dwivedi & Mackey (2024) provides a better-than-i.i.d. compression of a generic set of points. By generating high-fidelity coresets of size signific…
Learning Counterfactual Distributions via Kernel Nearest Neighbors
Kyuseong Choi, Jacob Feitelberg, Caleb Chin +2
Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate d…
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
Jacob Feitelberg, Kyuseong Choi, Anish Agarwal +1
We study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both…
FairPair: A Robust Evaluation of Biases in Language Models through Paired Perturbations
Jane Dwivedi-Yu, Raaz Dwivedi, Timo Schick
The accurate evaluation of differential treatment in language models to specific groups is critical to ensuring a positive and safe user experience. An ideal evaluation should have…
Debiased Distribution Compression
Lingxiao Li, Raaz Dwivedi, Lester Mackey
Modern compression methods can summarize a target distribution more succinctly than i.i.d. sampling but require access to a low-bias input sequence like a Markov chain…
Doubly Robust Inference in Causal Latent Factor Models
Alberto Abadie, Anish Agarwal, Raaz Dwivedi +1
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. Th…
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
Susobhan Ghosh, Raphael Kim, Prasidh Chhabria +5
There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such seq…
Compress Then Test: Powerful Kernel Testing in Near-linear Time
Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey
Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on sample points. However, existing kernel tests either run in …
Doubly robust nearest neighbors in factor models
Raaz Dwivedi, Caleb Chin, Sabina Tomkins +3
We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models. We consider a matrix completion problem with missin…
On counterfactual inference with unobserved confounding
Abhin Shah, Raaz Dwivedi, Devavrat Shah +1
Given an observational study with independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one -dimensional sample…
Counterfactual inference in sequential experiments
Raaz Dwivedi, Katherine Tian, Sabina Tomkins +3
We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points using treatment policies…
Distribution Compression in Near-linear Time
Abhishek Shetty, Raaz Dwivedi, Lester Mackey
In distribution compression, one aims to accurately summarize a probability distribution using a small number of representative points. Near-optimal thinning procedure…