45 citations · 59 across the 9 of their papers we have counts for
9 papers
Defense via Consensus: Mitigating Subliminal Learning through Multi-Teacher Distillation
Adhyyan Narang, Artin Tajdini, Claire Zhang +1
Fine-tuning can introduce unwanted behaviors through deliberate poisoning or unexpected generalization from seemingly benign data. Because these effects may be difficult to anticip…
Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing
Adhyyan Narang, Sarah Dean, Lillian J Ratliff +1
In many economically relevant contexts where machine learning is deployed, multiple platforms obtain data from the same pool of users, each of whom selects the platform that best s…
On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback
Marcus Williams, Micah Carroll, Adhyyan Narang +3
As LLMs become more widely deployed, there is increasing interest in directly optimizing for feedback from end users (e.g. thumbs up) in addition to feedback from paid annotators.…
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
Adhyyan Narang, Andrew Wagenmaker, Lillian Ratliff +1
In this paper, we study the non-asymptotic sample complexity for the pure exploration problem in contextual bandits and tabular reinforcement learning (RL): identifying an epsilon-…
Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback
Adhyyan Narang, Omid Sadeghi, Lillian J Ratliff +2
In the context of online interactive machine learning with combinatorial objectives, we extend purely submodular prior work to more general non-submodular objectives. This includes…
Towards Sample-efficient Overparameterized Meta-learning
Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk +2
An overarching goal in machine learning is to build a generalizable model with few samples. To this end, overparameterization has been the subject of immense interest to explain th…
Multiplayer Performative Prediction: Learning in Decision-Dependent Games
Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy +2
Learning problems commonly exhibit an interesting feedback mechanism wherein the population data reacts to competing decision makers' actions. This paper formulates a new game theo…
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective
Adhyyan Narang, Vidya Muthukumar, Anant Sahai
State-of-the-art deep learning classifiers are heavily overparameterized with respect to the amount of training examples and observed to generalize well on "clean" data, but be hig…
Classification vs regression in overparameterized regimes: Does the loss function matter?
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian +3
We compare classification and regression tasks in an overparameterized linear model with Gaussian features. On the one hand, we show that with sufficient overparameterization all t…