3 citations · 5 across the 11 of their papers we have counts for
11 papers
When Less is Enough: Efficient Inference via Collaborative Reasoning
Yilei Chen, Sharut Gupta, Yannis Paschalidis +2
In this work, we introduce DUET (Dual-model Efficient Two-stage inference), a collaborative inference framework in which a capable model and a lightweight model work together to so…
Canonicalizing Multimodal Contrastive Representation Learning
Sharut Gupta, Sanyam Kansal, Stefanie Jegelka +2
As models and data scale, independently trained networks often induce analogous notions of similarity. But, matching similarities is weaker than establishing an explicit correspond…
ReasonCACHE: Teaching LLMs To Reason Without Weight Updates
Sharut Gupta, Phillip Isola, Stefanie Jegelka +4
Can Large language models (LLMs) learn to reason without any weight update and only through in-context learning (ICL)? ICL is strikingly sample-efficient, often learning from only…
Learning Diffusion Models with Flexible Representation Guidance
Chenyu Wang, Cai Zhou, Sharut Gupta +4
Diffusion models can be improved with additional guidance towards more effective representations of input. Indeed, prior empirical work has already shown that aligning internal rep…
Sequential-Parallel Duality in Prefix Scannable Models
Morris Yau, Sharut Gupta, Valerie Engelmayer +3
Modern neural sequence models are designed to meet the dual mandate of parallelizable training and fast sequential inference. Recent developments have given rise to various models,…
Understanding the Role of Equivariance in Self-supervised Learning
Yifei Wang, Kaiwen Hu, Sharut Gupta +3
Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features (\eg colors) by b…
An Information Criterion for Controlled Disentanglement of Multimodal Data
Chenyu Wang, Sharut Gupta, Xinyi Zhang +4
Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities. By disentangling modality-specific information from information that i…
In-Context Symmetries: Self-Supervised Learning through Contextual World Models
Sharut Gupta, Chenyu Wang, Yifei Wang +2
At the core of self-supervised learning for vision is the idea of learning invariant or equivariant representations with respect to a set of data transformations. This approach, ho…
Removing Biases from Molecular Representations via Information Maximization
Chenyu Wang, Sharut Gupta, Caroline Uhler +1
High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the…
Context is Environment
Sharut Gupta, Stefanie Jegelka, David Lopez-Paz +1
Two lines of work are taking the central stage in AI research. On the one hand, the community is making increasing efforts to build models that discard spurious correlations and ge…
Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
Tasuku Soma, Khashayar Gatmiry, Sharut Gupta +1
Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems i…