7 citations · 24 across the 16 of their papers we have counts for
22 papers
Spend Where It Matters: Particle Search with MH Refinement for Budget-Constrained Audio-Visual Alignment
Shivanshu Shekhar, Raghavendra Addanki, Tong Zhang +1
Reward-guided inference-time alignment can improve joint audio-visual generation, but optimizing multiple alignment objectives requires many expensive model evaluations, making exi…
Skill-based Agentic Evaluation for Real-time Data Science Tasks
Aniruddha Tamhane, Raghavendra Addanki, Ayushi Aggarwal +4
We present a framework for evaluating data-science agents on live, continuously updated data using executable ground truth and format-agnostic factoid scoring. Consider this exampl…
COSAC: Counterfactual Credit Assignment in Sequential Cooperative Teams
Shripad Deshmukh, Jayakumar Subramanian, Raghavendra Addanki +1
In cooperative teams where agents act in a fixed order and share a single team-level reward (multi-agent language systems, sequential robotic tasks), per-agent credit assignment is…
GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling
Shivanshu Shekhar, Uttaran Bhattacharya, Raghavendra Addanki +3
Aligning video generative models with human preferences remains challenging: current approaches rely on Vision-Language Models (VLMs) for reward modeling, but these models struggle…
ML-Tool-Bench: Tool-Augmented Planning for ML Tasks
Yaswanth Chittepu, Raghavendra Addanki, Tung Mai +2
The development of autonomous machine learning (ML) agents capable of end-to-end data science workflows represents a significant frontier in artificial intelligence. These agents m…
Leveraging semantic similarity for experimentation with AI-generated treatments
Lei Shi, David Arbour, Raghavendra Addanki +2
Large Language Models (LLMs) enable a new form of digital experimentation where treatments combine human and model-generated content in increasingly sophisticated ways. The main me…
Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization
Subhojyoti Mukherjee, Viet Dac Lai, Raghavendra Addanki +6
Offline reinforcement learning (RL) is a variant of RL where the policy is learned from a previously collected dataset of trajectories and rewards. In our work, we propose a practi…
Causal Discovery in Semi-Stationary Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu +2
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas…
Causal Discovery-Driven Change Point Detection in Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu +2
Change point detection in time series aims to identify moments when the probability distribution of time series changes. It is widely applied in many areas, such as human activity…
Limits of Approximating the Median Treatment Effect
Raghavendra Addanki, Siddharth Bhandari
Average Treatment Effect (ATE) estimation is a well-studied problem in causal inference. However, it does not necessarily capture the heterogeneity in the data, and several approac…
Continuous Treatment Effects with Surrogate Outcomes
Zhenghao Zeng, David Arbour, Avi Feller +4
In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missin…
One Pass Streaming Algorithm for Super Long Token Attention Approximation in Sublinear Space
Raghav Addanki, Chenyang Li, Zhao Song +1
Attention computation takes both the time complexity of and the space complexity of simultaneously, which makes deploying Large Language Models (LLMs) in streamin…
Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform
Akash V. Maharaj, Ritwik Sinha, David Arbour +7
A/B tests are the gold standard for evaluating digital experiences on the web. However, traditional "fixed-horizon" statistical methods are often incompatible with the needs of mod…
Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai +2
Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some tr…
Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries
Raghavendra Addanki, Andrew McGregor, Cameron Musco
We study the problem of estimating the number of edges in an -vertex graph, accessed via the Bipartite Independent Set query model introduced by Beame et al. (ITCS '18). In this…
Improved Approximation and Scalability for Fair Max-Min Diversification
Raghavendra Addanki, Andrew McGregor, Alexandra Meliou +1
Given an -point metric space where each point belongs to one of different categories or groups and a set of integers , the fair Max-…
Collaborative Causal Discovery with Atomic Interventions
Raghavendra Addanki, Shiva Prasad Kasiviswanathan
We introduce a new Collaborative Causal Discovery problem, through which we model a common scenario in which we have multiple independent entities each with their own causal graph,…
How to Design Robust Algorithms using Noisy Comparison Oracle
Raghavendra Addanki, Sainyam Galhotra, Barna Saha
Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as -center clustering an…
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Raghavendra Addanki, Andrew McGregor, Cameron Musco
We study the problem of learning the causal relationships between a set of observed variables in the presence of latents, while minimizing the cost of interventions on the observed…
Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor +1
We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two interventio…
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Khushbu Agarwal, Tome Eftimov, Raghavendra Addanki +3
Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the applicatio…
Dynamic Set Cover: Improved Algorithms & Lower Bounds
Amir Abboud, Raghavendra Addanki, Fabrizio Grandoni +2
We give new upper and lower bounds for the {\em dynamic} set cover problem. First, we give a -approximation for fully dynamic set cover in (amortized)…