3 citations · 7 across the 19 of their papers we have counts for
20 papers
Entropy Regularization: A Free Correction to Cross-Entropy for Verified Demonstrations
Mihir Dhanakshirur, Adam Ousherovitch, Ambuj Tewari
Large language models are often post-trained on expert demonstrations using cross-entropy (CE), even when the downstream objective is not to imitate the demonstrated solution but t…
Direct Optimization of Generators for Search in Automated Theorem Proving
Adam Ousherovitch, Ambuj Tewari
Fine-tuned Large Language Models (LLMs) significantly advance Automated Theorem Proving (ATP), but are often deployed as guiding policies within tree search rather than for single-…
A General-Purpose Molecular Foundation Model Transfers Across Diverse Olfactory Tasks
Yikun Han, Yi Wang, Neil Mankodi +2
Foundation models have transformed molecular property prediction, yet it remains unclear whether a molecular foundation model, fine-tuned on a single canonical olfactory prediction…
Distribution-Free Robust Predict-Then-Optimize in Function Spaces
Yash Patel, Ambuj Tewari
The need to rapidly solve PDEs in engineering design workflows has spurred the rise of neural surrogate models. In particular, neural operator models provide a discretization-invar…
On Generation in Metric Spaces
Jiaxun Li, Vinod Raman, Ambuj Tewari
We study generation in separable metric instance spaces. We extend the language generation framework from Kleinberg and Mullainathan [2024] beyond countable domains by defining nov…
A Characterization of List Language Identification in the Limit
Moses Charikar, Chirag Pabbaraju, Ambuj Tewari
We study the problem of language identification in the limit, where given a sequence of examples from a target language, the goal of the learner is to output a sequence of guesses…
A Greedy PDE Router for Blending Neural Operators and Classical Methods
Sahana Rayan, Yash Patel, Ambuj Tewari
When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency…
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
Yash Patel, Unique Subedi, Ambuj Tewari
We consider the problem of learning the evolution operator for the time-dependent Schrödinger equation, where the Hamiltonian may vary with time. Existing neural network-based surr…
Continuum Transformers Perform In-Context Learning by Operator Gradient Descent
Abhiti Mishra, Yash Patel, Ambuj Tewari
Transformers robustly exhibit the ability to perform in-context learning, whereby their predictive accuracy on a task can increase not by parameter updates but merely with the plac…
Offline Constrained Reinforcement Learning under Partial Data Coverage
Seokmin Ko, Ambuj Tewari, Kihyuk Hong
We study offline constrained reinforcement learning with general function approximation in discounted constrained Markov decision processes. Prior methods either require full data…
Learning to Partially Defer for Sequences
Sahana Rayan, Ambuj Tewari
In the Learning to Defer (L2D) framework, a prediction model can either make a prediction or defer it to an expert, as determined by a rejector. Current L2D methods train the rejec…
A Training-free Method for LLM Text Attribution
Tara Radvand, Mojtaba Abdolmaleki, Izak Duenyas +2
Verifying the provenance of text is increasingly important for firms, educational institutions, and online platforms as Large Language Models (LLMs) produce output that is nearly i…
Near Optimal Pure Exploration in Logistic Bandits
Eduardo Ochoa Rivera, Ambuj Tewari
Bandit algorithms have garnered significant attention due to their practical applications in real-world scenarios. However, beyond simple settings such as multi-arm or linear bandi…
Leveraging Offline Data in Linear Latent Contextual Bandits
Chinmaya Kausik, Kevin Tan, Ambuj Tewari
Leveraging offline data is an attractive way to accelerate online sequential decision-making. However, it is crucial to account for latent states in users or environments in the of…
Conformal Robust Control of Linear Systems
Yash Patel, Sahana Rayan, Ambuj Tewari
End-to-end engineering design pipelines, in which designs are evaluated using concurrently defined optimal controllers, are becoming increasingly common in practice. To discover de…
Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
Eduardo Ochoa Rivera, Yash Patel, Ambuj Tewari
Distribution-free uncertainty estimation for ensemble methods is increasingly desirable due to the widening deployment of multi-modal black-box predictive models. Conformal predict…
Examining the Impact of Local Condition Violations on Energy Computations in DFT
Vaibhav Khanna, Bikash Kanungo, Vikram Gavini +2
This work introduces Extent of Violation Indices (EVIs), a novel metric for quantifying how well exchange-correlation functionals adhere to local conditions. Applying EVIs to a div…
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks
Ziping Xu, Zifan Xu, Runxuan Jiang +2
Multitask Reinforcement Learning (MTRL) approaches have gained increasing attention for its wide applications in many important Reinforcement Learning (RL) tasks. However, while re…
Conformal Contextual Robust Optimization
Yash Patel, Sahana Rayan, Ambuj Tewari
Data-driven approaches to predict-then-optimize decision-making problems seek to mitigate the risk of uncertainty region misspecification in safety-critical settings. Current appro…
RL Boltzmann Generators for Conformer Generation in Data-Sparse Environments
Yash Patel, Ambuj Tewari
The generation of conformers has been a long-standing interest to structural chemists and biologists alike. A subset of proteins known as intrinsically disordered proteins (IDPs) f…