Search Results for author: Souradip Chakraborty

Found 15 papers, 2 papers with code

Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey

no code implementations23 Oct 2023 Soumya Suvra Ghosal, Souradip Chakraborty, Jonas Geiping, Furong Huang, Dinesh Manocha, Amrit Singh Bedi

But in parallel to the development of detection frameworks, researchers have also concentrated on designing strategies to elude detection, i. e., focusing on the impossibilities of AI-generated text detection.

Misinformation Text Detection

PARL: A Unified Framework for Policy Alignment in Reinforcement Learning

no code implementations3 Aug 2023 Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Dinesh Manocha, Huazheng Wang, Mengdi Wang, Furong Huang

We present a novel unified bilevel optimization-based framework, \textsf{PARL}, formulated to address the recently highlighted critical issue of policy alignment in reinforcement learning using utility or preference-based feedback.

Bilevel Optimization Procedure Learning +2

Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in Multi-Agent RL

no code implementations27 May 2023 Xiangyu Liu, Souradip Chakraborty, Yanchao Sun, Furong Huang

Based on such a generalized attack framework, the attacker can also regulate the state distribution shift caused by the attack through an attack budget, and thus produce stealthy adversarial policies that can exploit the victim agent.

On the Possibilities of AI-Generated Text Detection

no code implementations10 Apr 2023 Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, Furong Huang

Our work addresses the critical issue of distinguishing text generated by Large Language Models (LLMs) from human-produced text, a task essential for numerous applications.

Text Detection

RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback

no code implementations14 Mar 2023 Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar, Mohamed Elnoor, Priya Narayanan, Carl Busart, Pratap Tokekar, Amrit Singh Bedi, Dinesh Manocha

Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may result in catastrophic failures.

Continuous Control Zero-Shot Learning

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies

no code implementations12 Jun 2022 Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Pratap Tokekar, Dinesh Manocha

In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems.

Continuous Control OpenAI Gym

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

no code implementations2 Jun 2022 Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha

Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity grows unbounded with time.

Continuous Control Model-based Reinforcement Learning +2

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

no code implementations28 Jan 2022 Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian Sadler, Pratap Tokekar, Alec Koppel

Doing so incurs a persistent bias that appears in the attenuation rate of the expected policy gradient norm, which is inversely proportional to the radius of the action space.

Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning

no code implementations SEMEVAL 2020 Ekansh Verma, Vinodh Motupalli, Souradip Chakraborty

In this paper, we present our approach for the {'}Detection of Propaganda Techniques in News Articles{'} task as a part of the 2020 edition of International Workshop on Semantic Evaluation.

Ensemble Learning

FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints

no code implementations7 Oct 2020 Souradip Chakraborty, Ekansh Verma, Saswata Sahoo, Jyotishka Datta

Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold.

Fairness Representation Learning

G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

1 code implementation28 Sep 2020 Souradip Chakraborty, Aritra Roy Gosthipaty, Sayak Paul

In this work, we propose that, with the normalized temperature-scaled cross-entropy (NT-Xent) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch.

Contrastive Learning Denoising +2

G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

1 code implementation25 Sep 2020 Souradip Chakraborty, Aritra Roy Gosthipaty, Sayak Paul

In this work, we propose that, with the normalized temperature-scaled cross-entropy (NT-Xent) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch.

Contrastive Learning Denoising +1

Learning Representation for Mixed Data Types with a Nonlinear Deep Encoder-Decoder Framework

no code implementations21 Sep 2020 Saswata Sahoo, Souradip Chakraborty

Representation of data on mixed variables, numerical and categorical types to get suitable feature map is a challenging task as important information lies in a complex non-linear manifold.

Clustering

Graph Spectral Feature Learning for Mixed Data of Categorical and Numerical Type

no code implementations6 May 2020 Saswata Sahoo, Souradip Chakraborty

In this work, we propose a novel strategy to explicitly model the probabilistic dependence structure among the mixed type of variables by an undirected graph.

Vocal Bursts Type Prediction

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