Search Results for author: Nagarajan Natarajan

Found 22 papers, 1 papers with code

Provably Robust DPO: Aligning Language Models with Noisy Feedback

no code implementations1 Mar 2024 Sayak Ray Chowdhury, Anush Kini, Nagarajan Natarajan

Our experiments on IMDb sentiment generation and Anthropic's helpful-harmless dataset show that rDPO is robust to noise in preference labels compared to vanilla DPO and other heuristics proposed by practitioners.

NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness

no code implementations29 Jan 2024 Manav Singhal, Tushar Aggarwal, Abhijeet Awasthi, Nagarajan Natarajan, Aditya Kanade

We propose a new benchmark NoFunEval to evaluate code LMs on non-functional requirements and simple classification instances for both functional and non-functional requirements.

GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval

no code implementations31 Oct 2023 Daman Arora, Anush Kini, Sayak Ray Chowdhury, Nagarajan Natarajan, Gaurav Sinha, Amit Sharma

Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents.

Passage Retrieval Re-Ranking +1

Differentially Private Reward Estimation with Preference Feedback

no code implementations30 Oct 2023 Sayak Ray Chowdhury, Xingyu Zhou, Nagarajan Natarajan

Within a standard minimax estimation framework, we provide tight upper and lower bounds on the error in estimating $\theta^*$ under both local and central models of DP.

Adversarial Attack

Frustrated with Code Quality Issues? LLMs can Help!

no code implementations22 Sep 2023 Nalin Wadhwa, Jui Pradhan, Atharv Sonwane, Surya Prakash Sahu, Nagarajan Natarajan, Aditya Kanade, Suresh Parthasarathy, Sriram Rajamani

We present a tool, CORE (short for COde REvisions), architected using a pair of LLMs organized as a duo comprised of a proposer and a ranker.

Instruction Following Program Repair

Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent

3 code implementations15 Feb 2021 Ajaykrishna Karthikeyan, Naman jain, Nagarajan Natarajan, Prateek Jain

Decision trees provide a rich family of highly non-linear but efficient models, due to which they continue to be the go-to family of predictive models by practitioners across domains.

Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization

no code implementations15 Feb 2021 Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain

We study online learning with bandit feedback (i. e. learner has access to only zeroth-order oracle) where cost/reward functions $\f_t$ admit a "pseudo-1d" structure, i. e. $\f_t(\w) = \loss_t(\pred_t(\w))$ where the output of $\pred_t$ is one-dimensional.

Decision Making

Programming by Rewards

no code implementations14 Jul 2020 Nagarajan Natarajan, Ajaykrishna Karthikeyan, Prateek Jain, Ivan Radicek, Sriram Rajamani, Sumit Gulwani, Johannes Gehrke

The goal of the synthesizer is to synthesize a "decision function" $f$ which transforms the features to a decision value for the black-box component so as to maximize the expected reward $E[r \circ f (x)]$ for executing decisions $f(x)$ for various values of $x$.

Program Synthesis

On Scaling Data-Driven Loop Invariant Inference

no code implementations26 Nov 2019 Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain

Automated synthesis of inductive invariants is an important problem in software verification.

Leveraging Distributional Semantics for Multi-Label Learning

no code implementations18 Sep 2017 Rahul Wadbude, Vivek Gupta, Piyush Rai, Nagarajan Natarajan, Harish Karnick, Prateek Jain

Our approach is novel in that it highlights interesting connections between label embedding methods used for multi-label learning and paragraph/document embedding methods commonly used for learning representations of text data.

Document Embedding Missing Labels +1

Consistency Analysis for Binary Classification Revisited

no code implementations ICML 2017 Krzysztof Dembczyński, Wojciech Kotłowski, Oluwasanmi Koyejo, Nagarajan Natarajan

Statistical learning theory is at an inflection point enabled by recent advances in understanding and optimizing a wide range of metrics.

Binary Classification Classification +2

Active Heteroscedastic Regression

no code implementations ICML 2017 Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan

In this work, we consider a theoretical analysis of the label requirement of active learning for regression under a heteroscedastic noise model, where the noise depends on the instance.

Active Learning Binary Classification +1

Learning from Binary Labels with Instance-Dependent Corruption

no code implementations3 May 2016 Aditya Krishna Menon, Brendan van Rooyen, Nagarajan Natarajan

Suppose we have a sample of instances paired with binary labels corrupted by arbitrary instance- and label-dependent noise.

Consistent Multilabel Classification

no code implementations NeurIPS 2015 Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon

In particular, we show that for multilabel metrics constructed as instance-, micro- and macro-averages, the population optimal classifier can be decomposed into binary classifiers based on the marginal instance-conditional distribution of each label, with a weak association between labels via the threshold.

Classification General Classification

Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics

no code implementations7 May 2015 Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon

We provide a general theoretical analysis of expected out-of-sample utility, also referred to as decision-theoretic classification, for non-decomposable binary classification metrics such as F-measure and Jaccard coefficient.

Binary Classification Classification +1

PU Learning for Matrix Completion

no code implementations22 Nov 2014 Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon

For the first case, we propose a "shifted matrix completion" method that recovers M using only a subset of indices corresponding to ones, while for the second case, we propose a "biased matrix completion" method that recovers the (thresholded) binary matrix.

Binary Classification Clustering +3

Learning with Noisy Labels

no code implementations NeurIPS 2013 Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari

In this paper, we theoretically study the problem of binary classification in the presence of random classification noise --- the learner, instead of seeing the true labels, sees labels that have independently been flipped with some small probability.

Binary Classification General Classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.