7 citations · 19 across the 13 of their papers we have counts for
14 papers
Off-Policy Evaluation with Strategic Agents via Local Disclosure
Kiet Q. H. Vo, Abbavaram Gowtham Reddy, Julian Rodemann +2
We study off-policy evaluation (OPE) under strategic behavior where decision subjects (or agents) respond to a decision maker's policy by strategically modifying their covariates.…
When Shift Happens - Confounding Is to Blame
Abbavaram Gowtham Reddy, Celia Rubio-Madrigal, Rebekka Burkholz +1
Distribution shifts introduce uncertainty that undermines the robustness and generalization capabilities of machine learning models. While conventional wisdom suggests that learnin…
Efficient Vocabulary-Free Fine-Grained Visual Recognition in the Age of Multimodal LLMs
Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy +1
Fine-grained Visual Recognition (FGVR) involves distinguishing between visually similar categories, which is inherently challenging due to subtle inter-class differences and the ne…
Detecting and Measuring Confounding Using Causal Mechanism Shifts
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Detecting and measuring confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of un…
Teaching Transformers Causal Reasoning through Axiomatic Training
Aniket Vashishtha, Abhinav Kumar, Atharva Pandey +4
For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since active interventions are costly, we study to what extent a system can learn c…
Can Better Text Semantics in Prompt Tuning Improve VLM Generalization?
Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy +1
Going beyond mere fine-tuning of vision-language models (VLMs), learnable prompt tuning has emerged as a promising, resource-efficient alternative. Despite their potential, effecti…
Debiasing Machine Unlearning with Counterfactual Examples
Ziheng Chen, Jia Wang, Jun Zhuang +7
The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of their historical actions by implementing machine-learning techniques. These techniques…
Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar +3
Large Language Models (LLMs) have been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variab…
On Counterfactual Data Augmentation Under Confounding
Abbavaram Gowtham Reddy, Saketh Bachu, Saloni Dash +3
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data. These biases, such as spurious correlations, arise due to var…
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive…
Counterfactual Generation Under Confounding
Abbavaram Gowtham Reddy, Saloni Dash, Amit Sharma +1
A machine learning model, under the influence of observed or unobserved confounders in the training data, can learn spurious correlations and fail to generalize when deployed. For…
On Causally Disentangled Representations
Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N Balasubramanian
Representation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability. I…
Matching Learned Causal Effects of Neural Networks with Domain Priors
Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian +1
A trained neural network can be interpreted as a structural causal model (SCM) that provides the effect of changing input variables on the model's output. However, if training data…
Causality in Neural Networks -- An Extended Abstract
Abbavaram Gowtham Reddy
Causal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and r…