activity
20192026
most citedProvable Defense against Backdoor Policies in Reinforcement Learning

7 citations · 10 across the 7 of their papers we have counts for

collaborators

8 papers

cs.IR2026

RPTune: Learned Context Curation for LLM Catalog Search

Chuxuan Hu, Hejie Cui, Norman Huang +3

For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-stage retrieval designed primaril…

cs.LG2025

In-Context Learning with Hypothesis-Class Guidance

Ziqian Lin, Shubham Kumar Bharti, Kangwook Lee

Recent research has investigated the underlying mechanisms of in-context learning (ICL) both theoretically and empirically, often using data generated from simple function classes.…

cs.AI2024

CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts

Shubham Bharti, Shiyun Cheng, Jihyun Rho +5

We introduce CHARTOM, a visual theory-of-mind benchmark designed to evaluate multimodal large language models' capability to understand and reason about misleading data visualizati…

cs.LG2023

Optimally Teaching a Linear Behavior Cloning Agent

Shubham Kumar Bharti, Stephen Wright, Adish Singla +1

We study optimal teaching of Linear Behavior Cloning (LBC) learners. In this setup, the teacher can select which states to demonstrate to an LBC learner. The learner maintains a ve…

cs.LG2022★ 7 cited

Provable Defense against Backdoor Policies in Reinforcement Learning

Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla +1

We propose a provable defense mechanism against backdoor policies in reinforcement learning under subspace trigger assumption. A backdoor policy is a security threat where an adver…

cs.LG2022★ 2 cited

The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning

Eric Pulick, Shubham Bharti, Yiding Chen +4

As machine learning (ML) is more tightly woven into society, it is imperative that we better characterize ML's strengths and limitations if we are to employ it responsibly. Existin…

cs.LG2020★ 1 cited

The Sample Complexity of Teaching-by-Reinforcement on Q-Learning

Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma +2

We study the sample complexity of teaching, termed as "teaching dimension" (TDim) in the literature, for the teaching-by-reinforcement paradigm, where the teacher guides the studen…

cs.LG2019

On the relationship between multitask neural networks and multitask Gaussian Processes

Karthikeyan K, Shubham Kumar Bharti, Piyush Rai

Despite the effectiveness of multitask deep neural network (MTDNN), there is a limited theoretical understanding on how the information is shared across different tasks in MTDNN. I…