1 citations · 1 across the 14 of their papers we have counts for
15 papers
Social Choice Foundations for Simulation-Augmented Generation
Sonja Kraiczy, Smitha Milli, Ratip Emin Berker +6
Simulation-augmented generation (SAGE) is a recent technical proposal in which models simulate individuals' viewpoints at inference time in order to provide more representative ans…
Cold-Start Personalization via Training-Free Priors from Structured World Models
Avinandan Bose, Shuyue Stella Li, Faeze Brahman +6
Cold-start personalization requires inferring user preferences through interaction when no user-specific historical data is available. The core challenge is a routing problem: each…
PrefDisco: Benchmarking Proactive Personalized Reasoning
Shuyue Stella Li, Avinandan Bose, Faeze Brahman +4
Current large language model (LLM) development treats task-solving and preference-alignment as separate challenges, optimizing first for objective correctness, then for alignment t…
LoRe: Personalizing LLMs via Low-Rank Reward Modeling
Avinandan Bose, Zhihan Xiong, Yuejie Chi +3
Personalizing large language models (LLMs) to accommodate diverse user preferences is essential for enhancing alignment and user satisfaction. Traditional reinforcement learning fr…
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
Leo Boisvert, Mihir Bansal, Chandra Kiran Reddy Evuru +9
We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1) It is a plug-in framework and integrates easily into realistic ag…
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning
Avinandan Bose, Laurent Lessard, Maryam Fazel +1
The rise of foundation models fine-tuned on human feedback from potentially untrusted users has increased the risk of adversarial data poisoning, necessitating the study of robustn…
Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
Avinandan Bose, Zhihan Xiong, Aadirupa Saha +2
Reinforcement Learning from Human Feedback (RLHF) is currently the leading approach for aligning large language models with human preferences. Typically, these models rely on exten…
Offline Multi-task Transfer RL with Representational Penalization
Avinandan Bose, Simon Shaolei Du, Maryam Fazel
We study the problem of representation transfer in offline Reinforcement Learning (RL), where a learner has access to episodic data from a number of source tasks collected a priori…
Initializing Services in Interactive ML Systems for Diverse Users
Avinandan Bose, Mihaela Curmei, Daniel L. Jiang +4
This paper investigates ML systems serving a group of users, with multiple models/services, each aimed at specializing to a sub-group of users. We consider settings where upon depl…
Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary
Tien Mai, Avinandan Bose, Arunesh Sinha +1
This work studies Stackelberg network interdiction games -- an important class of games in which a defender first allocates (randomized) defense resources to a set of critical node…
Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees
Avinandan Bose, Arunesh Sinha, Tien Mai
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO so…
Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models
Changyu Chen, Avinandan Bose, Shih-Fen Cheng +1
Realistic fine-grained multi-agent simulation of real-world complex systems is crucial for many downstream tasks such as reinforcement learning. Recent work has used generative mod…
Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling
Avinandan Bose, Pradeep Varakantham
Owing to the benefits for customers (lower prices), drivers (higher revenues), aggregation companies (higher revenues) and the environment (fewer vehicles), on-demand ride pooling…
Changepoint Analysis of Topic Proportions in Temporal Text Data
Avinandan Bose, Soumendu Sundar Mukherjee
Changepoint analysis deals with unsupervised detection and/or estimation of time-points in time-series data, when the distribution generating the data changes. In this article, we…
NeurInt : Learning to Interpolate through Neural ODEs
Avinandan Bose, Aniket Das, Yatin Dandi +1
A wide range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation present in the data distribution.…