4 papers
Best-of-Better-: Generating Pre-Aligned Responses with In-Context Learning
Eric Lei, Hsiang Hsu, Chun-Fu Chen
Inference-time alignment methods, such as Best-of-, offer a flexible alternative to training-based alignment by using reward models to select high-quality responses generated by…
Best-of-Tails: Bridging Optimism and Pessimism in Inference-Time Alignment
Hsiang Hsu, Eric Lei, Chun-Fu Chen
Inference-time alignment effectively steers large language models (LLMs) by generating multiple candidates from a reference model and selecting among them with an imperfect reward…
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
Neural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design…
Optimal Neural Compressors for the Rate-Distortion-Perception Tradeoff
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
Recent efforts in neural compression have focused on the rate-distortion-perception (RDP) tradeoff, where the perception constraint ensures the source and reconstruction distributi…