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Krishnaram Kenthapadi

7 accepted papers

2025

Mastering the Craft of Data Synthesis for CodeLLMs

NAACL 2025long

Large language models (LLMs) have shown impressive performance in code understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data synthesis and filtering techniques have been widely adopted and sho…

2025

Permissioned LLMs: Enforcing Access Control in Large Language Models

NeurIPS 2025poster

In enterprise settings, organizational data is segregated, siloed and carefully protected by elaborate access control frameworks. These access control structures can completely break down if an LLM fine-tuned on the siloed data serves requests, for downstream tasks, from individuals with disparate a…

Cited by 0SourceScholar
2022

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

NeurIPS 2022accept

We show that deep networks trained to satisfy demographic parity often do so through a form of race or gender awareness, and that the more we force a network to be fair, the more accurately we can recover race or gender from the internal state of the network. Based on this observation, we investigat…

2022

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks

ICLR 2022poster

As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explored test-time (evasion) attacks in RL and corresponding defenses, its robustness against training-time (poisoning) attacks…

2022

Generating Distributional Adversarial Examples to Evade Statistical Detectors

ICML 2022spotlight

Deep neural networks (DNNs) are known to be highly vulnerable to adversarial examples (AEs) that include malicious perturbations. Assumptions about the statistical differences between natural and adversarial inputs are commonplace in many detection techniques. As a best practice, AE detectors are ev…

Cited by 7SourcePDFScholar
2022

Pairwise Fairness for Ordinal Regression

AISTATS 2022poster

We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor has the form of a threshold model, composed of a scoring…

2021

Differentially Private Query Release Through Adaptive Projection

ICML 2021oral

We propose, implement, and evaluate a new algo-rithm for releasing answers to very large numbersof statistical queries likek-way marginals, sub-ject to differential privacy. Our algorithm makesadaptive use of a continuous relaxation of thePro-jection Mechanism, which answers queries on theprivate da…