Search Results for author: Prasad Tadepalli

Found 33 papers, 6 papers with code

Adversarial Attacks on Combinatorial Multi-Armed Bandits

no code implementations8 Oct 2023 Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu, Haoyu Zhao

Contrary to prior understanding of multi-armed bandits, our work reveals a surprising fact that the attackability of a specific CMAB instance also depends on whether the bandit instance is known or unknown to the adversary.

Multi-Armed Bandits

Parametrically Retargetable Decision-Makers Tend To Seek Power

no code implementations27 Jun 2022 Alexander Matt Turner, Prasad Tadepalli

We show that a range of qualitatively dissimilar decision-making procedures incentivize agents to seek power.

Decision Making Montezuma's Revenge

Explainable Models via Compression of Tree Ensembles

no code implementations16 Jun 2022 Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli

Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs).

Explainable Models

From Heatmaps to Structural Explanations of Image Classifiers

no code implementations13 Sep 2021 Li Fuxin, Zhongang Qi, Saeed Khorram, Vivswan Shitole, Prasad Tadepalli, Minsuk Kahng, Alan Fern

This paper summarizes our endeavors in the past few years in terms of explaining image classifiers, with the aim of including negative results and insights we have gained.

DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs

2 code implementations ICLR 2021 Aayam Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern

We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience.

Offline RL reinforcement-learning +1

On the Sub-Layer Functionalities of Transformer Decoder

no code implementations Findings of the Association for Computational Linguistics 2020 Yilin Yang, Longyue Wang, Shuming Shi, Prasad Tadepalli, Stefan Lee, Zhaopeng Tu

There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT); meanwhile, the decoder remains largely unexamined despite its critical role.

Machine Translation NMT +1

Avoiding Side Effects in Complex Environments

2 code implementations NeurIPS 2020 Alexander Matt Turner, Neale Ratzlaff, Prasad Tadepalli

By preserving optimal value for a single randomly generated reward function, AUP incurs modest overhead while leading the agent to complete the specified task and avoid many side effects.

Relation Extraction with Explanation

no code implementations ACL 2020 Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Prasad Tadepalli

Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences.

Relation Relation Extraction +1

Optimal Policies Tend to Seek Power

1 code implementation NeurIPS 2021 Alexander Matt Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli

Some researchers speculate that intelligent reinforcement learning (RL) agents would be incentivized to seek resources and power in pursuit of their objectives.

Reinforcement Learning (RL)

The Choice Function Framework for Online Policy Improvement

no code implementations1 Oct 2019 Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli

There are notable examples of online search improving over hand-coded or learned policies (e. g. AlphaZero) for sequential decision making.

Decision Making

Description-Based Zero-shot Fine-Grained Entity Typing

no code implementations NAACL 2019 Rasha Obeidat, Xiaoli Fern, Hamed Shahbazi, Prasad Tadepalli

Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text.

Entity Typing Vocal Bursts Type Prediction

Conservative Agency via Attainable Utility Preservation

3 code implementations26 Feb 2019 Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli

Reward functions are easy to misspecify; although designers can make corrections after observing mistakes, an agent pursuing a misspecified reward function can irreversibly change the state of its environment.

Saliency Learning: Teaching the Model Where to Pay Attention

no code implementations NAACL 2019 Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, Prasad Tadepalli

Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances.

Interactive Naming for Explaining Deep Neural Networks: A Formative Study

no code implementations18 Dec 2018 Mandana Hamidi-Haines, Zhongang Qi, Alan Fern, Fuxin Li, Prasad Tadepalli

For this purpose, we developed a user interface for "interactive naming," which allows a human annotator to manually cluster significant activation maps in a test set into meaningful groups called "visual concepts".

General Classification

Learning Scripts as Hidden Markov Models

no code implementations11 Sep 2018 J. Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Fern, Thomas G. Dietterich

Scripts have been proposed to model the stereotypical event sequences found in narratives.

Clustering

Attentional Multi-Reading Sarcasm Detection

no code implementations9 Sep 2018 Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli

We also study the behavior of the proposed model to provide explanations for the model's decisions.

Sarcasm Detection

Event Detection with Neural Networks: A Rigorous Empirical Evaluation

no code implementations EMNLP 2018 J. Walker Orr, Prasad Tadepalli, Xiaoli Fern

Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts.

Event Detection

Joint Neural Entity Disambiguation with Output Space Search

no code implementations COLING 2018 Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Chao Ma, Rasha Obeidat, Prasad Tadepalli

In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS).

Entity Disambiguation

Coactive Learning for Locally Optimal Problem Solving

no code implementations18 Apr 2014 Robby Goetschalckx, Alan Fern, Prasad Tadepalli

Coactive learning is an online problem solving setting where the solutions provided by a solver are interactively improved by a domain expert, which in turn drives learning.

Symbolic Opportunistic Policy Iteration for Factored-Action MDPs

no code implementations NeurIPS 2013 Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli

We address the scalability of symbolic planning under uncertainty with factored states and actions.

A Bayesian Approach for Policy Learning from Trajectory Preference Queries

no code implementations NeurIPS 2012 Aaron Wilson, Alan Fern, Prasad Tadepalli

We consider the problem of learning control policies via trajectory preference queries to an expert.

Autonomous Learning of Action Models for Planning

no code implementations NeurIPS 2011 Neville Mehta, Prasad Tadepalli, Alan Fern

This paper introduces two new frameworks for learning action models for planning.

Inverting Grice's Maxims to Learn Rules from Natural Language Extractions

no code implementations NeurIPS 2011 Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern

However, unlike standard approaches to missing data, in this setting we know that facts are more likely to be missing from the text in cases where the reader can infer them from the facts that are mentioned combined with the domain knowledge.

A Computational Decision Theory for Interactive Assistants

no code implementations NeurIPS 2010 Alan Fern, Prasad Tadepalli

A variation of this policy is shown to achieve worst-case regret that is logarithmic in the number of goals for any goal distribution.

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