2 citations · 4 across the 15 of their papers we have counts for
11 papers · 1 filter
When to Follow the Teacher: Advantage-Based On-Policy Distillation
Mahdi Sabbaghi, George Pappas, Hamed Hassani +1
Online Policy Distillation (OPD) often outperforms offline methods such as Supervised Fine-Tuning (SFT) by training on trajectories sampled directly from the student. However, stan…
Edit Discovery in Human-AI Interactions
Alireza Mirrokni, Shayan Kiyani, George Pappas +1
Human-AI interactions are becoming a rich source of data for improving AI systems: while many model responses are already satisfactory, others expose failures that could become val…
Multi-Round Human-AI Collaboration with User-Specified Requirements
Sima Noorani, Shayan Kiyani, Hamed Hassani +1
As humans increasingly rely on multiround conversational AI for high stakes decisions, principled frameworks are needed to ensure such interactions reliably improve decision qualit…
When to Trust the Cheap Check: Weak and Strong Verification for Reasoning
Shayan Kiyani, Sima Noorani, George Pappas +1
Reasoning with LLMs increasingly unfolds inside a broader verification loop. Internally, systems use cheap checks, such as self-consistency or proxy rewards, which we call weak ver…
Robust Policy Optimization to Prevent Catastrophic Forgetting
Mahdi Sabbaghi, George Pappas, Adel Javanmard +1
Large language models are commonly trained through multi-stage post-training: first via RLHF, then fine-tuned for other downstream objectives. Yet even small downstream updates can…
Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
Sima Noorani, Shayan Kiyani, George Pappas +1
Uncertainty quantification (UQ) is essential for safe deployment of generative AI models such as large language models (LLMs), especially in high stakes applications. Conformal pre…
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
Shayan Kiyani, George Pappas, Aaron Roth +1
A fundamental question in data-driven decision making is how to quantify the uncertainty of predictions in ways that can usefully inform downstream action. This interface between p…
Adversarial Reasoning at Jailbreaking Time
Mahdi Sabbaghi, Paul Kassianik, George Pappas +3
As large language models (LLMs) are becoming more capable and widespread, the study of their failure cases is becoming increasingly important. Recent advances in standardizing, mea…
Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks
Mahdi Sabbaghi, George Pappas, Hamed Hassani +1
Despite the success of Transformers on language understanding, code generation, and logical reasoning, they still fail to generalize over length on basic arithmetic tasks such as a…
Conformal Prediction with Learned Features
Shayan Kiyani, George Pappas, Hamed Hassani
In this paper, we focus on the problem of conformal prediction with conditional guarantees. Prior work has shown that it is impossible to construct nontrivial prediction sets with…
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
Kevin Kögler, Alexander Shevchenko, Hamed Hassani +1
Autoencoders are a prominent model in many empirical branches of machine learning and lossy data compression. However, basic theoretical questions remain unanswered even in a shall…