no code implementations • 23 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.
no code implementations • 3 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.
no code implementations • 27 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.
no code implementations • 10 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.
no code implementations • 14 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.
no code implementations • 28 Jan 2023 • Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha
Directed Exploration is a crucial challenge in reinforcement learning (RL), especially when rewards are sparse.
Model-based Reinforcement Learning
reinforcement-learning
+1
no code implementations • 12 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.
no code implementations • 2 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.
no code implementations • 28 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.
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.
no code implementations • 7 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.
1 code implementation • 28 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.
1 code implementation • 25 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.
no code implementations • 21 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.
no code implementations • 6 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.