no code implementations • 3 Jan 2024 • Abhishek Sinha, Shreya Singh
Deep learning algorithms are often said to be data hungry.
no code implementations • 26 Nov 2023 • Abhishek Sinha, Himanshi Tibrewal, Mansi Gupta, Nikhar Waghela, Shivank Garg
In this attack, adversaries aim to determine whether a particular point was used during the training of a target model.
no code implementations • 29 Oct 2023 • Abhishek Sinha, Rahul Vaze
This is achieved via a black box reduction of the constrained problem to the standard OCO problem for a recursively constructed sequence of surrogate cost functions.
no code implementations • 22 Oct 2023 • Siddhant Chaudhary, Abhishek Sinha
In this paper, we consider the $\alpha$-Fair Contextual Bandits problem, where the objective is to maximize the global $\alpha$-fair utility function - a non-decreasing concave function of the cumulative rewards in the adversarial setting.
no code implementations • 11 Apr 2023 • Abhishek Sinha
In this paper, we consider a fair prediction problem in the stochastic setting with hard lower bounds on the rate of accrual of rewards for a set of arms.
no code implementations • 28 Sep 2022 • Sourav Sahoo, Siddhant Chaudhary, Samrat Mukhopadhyay, Abhishek Sinha
In this connection, we propose an online learning policy called SCore (Subset Selection with Core) that solves the problem for a large class of reward functions.
no code implementations • 15 Aug 2022 • Naram Mhaisen, Abhishek Sinha, Georgios Paschos, Georgios Iosifidis
We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle (provided by, e. g., a Neural Network).
1 code implementation • 10 May 2022 • Ativ Joshi, Abhishek Sinha
In learning theory, the performance of an online policy is commonly measured in terms of the static regret metric, which compares the cumulative loss of an online policy to that of an optimal benchmark in hindsight.
no code implementations • NeurIPS 2021 • Debjit Paria, Abhishek Sinha
We show that $\texttt{LeadCache}$ is regret-optimal up to a factor of $\tilde{O}(n^{3/8}),$ where $n$ is the number of users.
1 code implementation • NeurIPS 2021 • Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.
no code implementations • 15 Oct 2021 • Samrat Mukhopadhyay, Sourav Sahoo, Abhishek Sinha
Unlike the classic version, where the learner selects exactly one expert from a pool of $N$ experts at each round, in this problem, the learner can select a subset of $k$ experts at each round $(1\leq k\leq N)$.
no code implementations • ICLR 2022 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics.
2 code implementations • 12 Jun 2021 • Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.
no code implementations • ICLR Workshop Neural_Compression 2021 • Abhishek Sinha, Jiaming Song, Stefano Ermon
We illustrate that with one set of representations, the hybrid approach is able to achieve good performance on multiple downstream tasks such as classification, reconstruction, and generation.
2 code implementations • ICLR 2021 • Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
Ranked #6 on Image Generation on CIFAR-100
no code implementations • 18 Jan 2021 • Samrat Mukhopadhyay, Abhishek Sinha
The objective is to design a caching policy that incurs minimal regret while considering both the rewards due to cache-hits and the switching cost due to the file fetches.
no code implementations • 1 Jan 2021 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Based on ideas from decision theory, we investigate a new class of discrepancies that are based on the optimal decision loss.
1 code implementation • 17 Sep 2020 • Debjit Paria, Abhishek Sinha
We show that $\texttt{LeadCache}$ is regret-optimal up to a factor of $\tilde{O}(n^{3/8}),$ where $n$ is the number of users.
no code implementations • 4 May 2020 • Gunjan Aggarwal, Abhishek Sinha, Nupur Kumari, Mayank Singh
In this paper, we leverage models with interpretable perceptually-aligned features and show that adversarial training with low max-perturbation bound can improve the performance of models for zero-shot and weakly supervised localization tasks.
no code implementations • 15 Jan 2020 • Pinkesh Badjatiya, Mausoom Sarkar, Abhishek Sinha, Siddharth Singh, Nikaash Puri, Jayakumar Subramanian, Balaji Krishnamurthy
We show how agents trained with SQLoss evolve cooperative behavior in several social dilemma matrix games.
no code implementations • 6 Dec 2019 • Gunjan Aggarwal, Abhishek Sinha
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves the texture of one and the shape of the other input.
no code implementations • 1 Dec 2019 • Tejus Gupta, Abhishek Sinha, Nupur Kumari, Mayank Singh, Balaji Krishnamurthy
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks.
1 code implementation • ECCV 2020 • Mayank Singh, Nupur Kumari, Puneet Mangla, Abhishek Sinha, Vineeth N. Balasubramanian, Balaji Krishnamurthy
Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust.
Ranked #1 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Error Rate metric)
BIG-bench Machine Learning Weakly-Supervised Object Localization
no code implementations • 26 Oct 2019 • Aditya Pandey, Abhishek Sinha, Aishwarya PS
A large amount of work has been done on the KDD 99 dataset, most of which includes the use of a hybrid anomaly and misuse detection model done in parallel with each other.
7 code implementations • 28 Jul 2019 • Puneet Mangla, Mayank Singh, Abhishek Sinha, Nupur Kumari, Vineeth N. Balasubramanian, Balaji Krishnamurthy
A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
1 code implementation • 13 May 2019 • Mayank Singh, Abhishek Sinha, Nupur Kumari, Harshitha Machiraju, Balaji Krishnamurthy, Vineeth N. Balasubramanian
We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers.
1 code implementation • 23 Apr 2018 • Akilesh B, Abhishek Sinha, Mausoom Sarkar, Balaji Krishnamurthy
We develop an attention mechanism for multi-modal fusion of visual and textual modalities that allows the agent to learn to complete the task and achieve language grounding.
no code implementations • 3 Jan 2018 • Mayank Singh, Abhishek Sinha, Balaji Krishnamurthy
Recently, Neural networks have seen a huge surge in its adoption due to their ability to provide high accuracy on various tasks.
no code implementations • ICLR 2018 • Abhishek Sinha, Akilesh B, Mausoom Sarkar, Balaji Krishnamurthy
In this work, we focus on the problem of grounding language by training an agent to follow a set of natural language instructions and navigate to a target object in a 2D grid environment.
no code implementations • 8 Nov 2017 • Biswarup Bhattacharya, Abhishek Sinha
Power grids are one of the most important components of infrastructure in today's world.
no code implementations • 8 Nov 2017 • Biswarup Bhattacharya, Abhishek Sinha
Non-availability of reliable and sustainable electric power is a major problem in the developing world.
no code implementations • 8 Sep 2017 • Biswarup Bhattacharya, Abhishek Sinha
Sustainable and economical generation of electrical power is an essential and mandatory component of infrastructure in today's world.
no code implementations • 17 Apr 2017 • Abhishek Sinha, Mausoom Sarkar, Aahitagni Mukherjee, Balaji Krishnamurthy
In this paper, we explore the idea of learning weight evolution pattern from a simple network for accelerating training of novel neural networks.