160 citations · 1.5k across the 122 of their papers we have counts for
59 papers · 1 filter
From Non-Convex to Strongly Convex: Curvature-Adaptive FTPL for Online Optimization
Moses Charikar, Chirag Pabbaraju, Ambuj Tewari
Curvature adaptivity is a classical theme in online optimization: for convex Lipschitz losses, adaptive methods interpolate between the optimal regret for general con…
Compute Aligned Training: Optimizing for Test Time Inference
Adam Ousherovitch, Ambuj Tewari
Scaling test-time compute has emerged as a powerful mechanism for enhancing Large Language Model (LLM) performance. However, standard post-training paradigms, Supervised Fine-Tunin…
Characterizing the Multiclass Learnability of Forgiving 0-1 Loss Functions
Jacob Trauger, Tyson Trauger, Ambuj Tewari
In this paper we will give a characterization of the learnability of forgiving 0-1 loss functions in the multiclass setting with effectively finite cardinality of the output and la…
If generative AI is the answer, what is the question?
Ambuj Tewari
Beginning with text and images, generative AI has expanded to audio, video, computer code, and molecules. Yet, if generative AI is the answer, what is the question? We explore the…
A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
Kihyuk Hong, Ambuj Tewari
We study reinforcement learning in infinite-horizon average-reward settings with linear MDPs. Previous work addresses this problem by approximating the average-reward setting by di…
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
Woojin Chae, Kihyuk Hong, Yufan Zhang +2
This paper proposes a computationally tractable algorithm for learning infinite-horizon average-reward linear mixture Markov decision processes (MDPs) under the Bellman optimality…