166 citations · 595 across the 71 of their papers we have counts for
79 papers
Improved Variance Estimation in Homoskedastic Nonparametric Random-Design Regression via a Two-Scale Approach
Edgar Dobriban, Rajarshi Mukherjee, James M. Robins +1
We study estimation of a constant conditional variance in nonparametric regression with a -dimensional random design. This is an important problem, and similar questions a…
Generalized Hierarchical Conformal Prediction
Soham Mallick, Eric Tchetgen Tchetgen, Edgar Dobriban +1
Many prediction problems arise with data collected in groups. In this setting, hierarchical conformal prediction (HCP) (Lee et al., 2026) provides distribution-free prediction sets…
Sharp Minimax Theory for Randomized Experiments
Timothy Sudijono, Edgar Dobriban, Eric Tchetgen Tchetgen
We study minimax-optimal designs and estimators for estimating the sample average treatment effect in finite population randomized experiments, where both design and estimator are…
The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests
Edgar Dobriban
We show that the Benjamini--Hochberg procedure can fail to control the false discovery rate (FDR) at its nominal level for correlated two-sided Gaussian -values. We construct a…
MultiRisk: Multiple Risk Control via Iterative Score Thresholding
Sunay Joshi, Yan Sun, Hamed Hassani +1
As generative AI systems are increasingly deployed in real-world applications, regulating multiple dimensions of model behavior has become essential. We focus on test-time filterin…
Fair Classification by Direct Intervention on Operating Characteristics
Kevin Jiang, Edgar Dobriban
We develop new classifiers under group fairness in the attribute-aware setting for binary classification with multiple group fairness constraints (e.g., demographic parity (DP), eq…
Statistical Methods in Generative AI
Edgar Dobriban
Generative Artificial Intelligence is emerging as an important technology, promising to be transformative in many areas. At the same time, generative AI techniques are based on sam…
Singleton-Optimized Conformal Prediction
Tao Wang, Yan Sun, Edgar Dobriban
Conformal prediction can be used to construct prediction sets that cover the true outcome with a desired probability, but can sometimes lead to large prediction sets that are costl…
General Synthetic-Powered Inference
Meshi Bashari, Yonghoon Lee, Roy Maor Lotan +2
The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and chall…
Conformal Information Pursuit for Interactively Guiding Large Language Models
Kwan Ho Ryan Chan, Yuyan Ge, Edgar Dobriban +2
A significant use case of instruction-finetuned Large Language Models (LLMs) is to solve question-answering tasks interactively. In this setting, an LLM agent is tasked with making…
Synthetic-Powered Predictive Inference
Meshi Bashari, Roy Maor Lotan, Yonghoon Lee +2
Conformal prediction is a framework for predictive inference with a distribution-free, finite-sample guarantee. However, it tends to provide uninformative prediction sets when cali…
Foundations of Top- Decoding For Language Models
Georgy Noarov, Soham Mallick, Tao Wang +5
Top- decoding is a widely used method for sampling from LLMs: at each token, only the largest next-token-probabilities are kept, and the next token is sampled after re-norma…
Finding Distributions that Differ, with False Discovery Rate Control
Yonghoon Lee, Edgar Dobriban, Eric Tchetgen Tchetgen
We consider the problem of comparing a reference distribution with several other distributions. Given a sample from both the reference and the comparison groups, we aim to identify…
Conformal Inference under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularization
Sunay Joshi, Shayan Kiyani, George Pappas +2
We consider the problem of conformal prediction under covariate shift. Given labeled data from a source domain and unlabeled data from a covariate shifted target domain, we seek to…
Batch Predictive Inference
Yonghoon Lee, Eric Tchetgen Tchetgen, Edgar Dobriban
Constructing prediction sets with coverage guarantees for unobserved outcomes is a core problem in modern statistics. Methods for predictive inference have been developed for a wid…
A Confidence Interval for the Expected Calibration Error
Yan Sun, Pratik Chaudhari, Ian J. Barnett +1
Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration of probabilistic predictions remains…
Evaluating the Performance of Large Language Models via Debates
Behrad Moniri, Hamed Hassani, Edgar Dobriban
Large Language Models (LLMs) are rapidly evolving and impacting various fields, necessitating the development of effective methods to evaluate and compare their performance. Most c…
Watermarking Language Models with Error Correcting Codes
Patrick Chao, Yan Sun, Edgar Dobriban +1
Recent progress in large language models enables the creation of realistic machine-generated content. Watermarking is a promising approach to distinguish machine-generated text fro…
One-Shot Safety Alignment for Large Language Models via Optimal Dualization
Xinmeng Huang, Shuo Li, Edgar Dobriban +3
The growing safety concerns surrounding large language models raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safe…
Uncertainty in Language Models: Assessment through Rank-Calibration
Xinmeng Huang, Shuo Li, Mengxin Yu +5
Language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctl…
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Patrick Chao, Edoardo Debenedetti, Alexander Robey +9
Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, w…
Inference in Randomized Least Squares and PCA via Normality of Quadratic Forms
Leda Wang, Zhixiang Zhang, Edgar Dobriban
Randomized algorithms can be used to speed up the analysis of large datasets. In this paper, we develop a unified methodology for statistical inference via randomized sketching or…
Minimax Optimal Fair Classification with Bounded Demographic Disparity
Xianli Zeng, Guang Cheng, Edgar Dobriban
Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness. While extensive research aims to reduce disparity, the effect of using a \…
Conditional Predictive Inference for Missing Outcomes
Yonghoon Lee, Edgar Dobriban, Eric Tchetgen Tchetgen
We study the problem of conditional predictive inference on multiple outcomes missing at random (MAR) -- or equivalently, under covariate shift. While the weighted conformal predic…