Search Results for author:
Found 45 papers, 11 papers with code
CURATRON: Complete Robust Preference Data for Robust Alignment of Large Language Models
This paper addresses the challenges of aligning large language models (LLMs) with human values via preference learning (PL), with a focus on the issues of incomplete and corrupted data in preference datasets.
InteraRec: Interactive Recommendations Using Multimodal Large Language Models
Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests.
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
We present a novel statistical approach to incorporating uncertainty awareness in model-free distributional reinforcement learning involving quantile regression-based deep Q networks.
User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models
in a fashion similar to generative AI tools, where they can easily provide immediate feedback as well as adapt the deployed models as desired.
InteraSSort: Interactive Assortment Planning Using Large Language Models
Assortment planning, integral to multiple commercial offerings, is a key problem studied in e-commerce and retail settings.
STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy
In diverse test scenarios involving adverse weather and sensor malfunctions, we show that STARNet enhances prediction accuracy by approximately 10% by filtering out untrustworthy sensor streams.
Conformalized Multimodal Uncertainty Regression and Reasoning
This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning regressor.
Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection
We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy.
Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge
In the expanding landscape of AI-enabled robotics, robust quantification of predictive uncertainties is of great importance.
Generative AI for Business Strategy: Using Foundation Models to Create Business Strategy Tools
Generative models (foundation models) such as LLMs (large language models) are having a large impact on multiple fields.
Lightweight, Uncertainty-Aware Conformalized Visual Odometry
Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics, and recent progress in the field has produced highly accurate point predictions in complex environments.
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies
We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning.
Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder
Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain.
Estimating the Personality of White-Box Language Models
Our work seeks to address this gap by exploring the personality traits of several large-scale language models designed for open-ended text generation and the datasets used for training them.
ENOS: Energy-Aware Network Operator Search for Hybrid Digital and Compute-in-Memory DNN Accelerators
Our proposed ENOS framework allows an optimal layer-wise integration of inference operators and computing modes to achieve the desired balance of energy and accuracy.
Choice-Aware User Engagement Modeling andOptimization on Social Media
We address the problem of maximizing user engagement with content (in the form of like, reply, retweet, and retweet with comments)on the Twitter platform.
Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays
Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3D map representation using a harmonic mean of "Gaussian-like" mixture (HMGM) model.
Indoor Localization
Robotics
Hardware Architecture
Image and Video Processing
B.7; I.2.9
Price Discrimination in the Presence of Customer Loyalty and Differing Firm Costs
We study how loyalty behavior of customers and differing costs to produce undifferentiated products by firms can influence market outcomes.
Off-Policy Optimization of Portfolio Allocation Policies under Constraints
The dynamic portfolio optimization problem in finance frequently requires learning policies that adhere to various constraints, driven by investor preferences and risk.
KATRec: Knowledge Aware aTtentive Sequential Recommendations
Sequential recommendation systems model dynamic preferences of users based on their historical interactions with platforms.
Deep Reinforcement Learning for Crowdsourced Urban Delivery: System States Characterization, Heuristics-guided Action Choice, and Rule-Interposing Integration
This paper investigates the problem of assigning shipping requests to ad hoc couriers in the context of crowdsourced urban delivery.
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit
In this paper, we propose an optimistic algorithm and show that the regret is bounded by $O(\sqrt{dT} + \kappa)$, significantly improving the performance over existing methods.
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect
We study the effect of persistence of engagement on learning in a stochastic multi-armed bandit setting.
Multi-Purchase Behavior: Modeling, Estimation and Optimization
We study the problem of modeling purchase of multiple products and utilizing it to display optimized recommendations for online retailers and e-commerce platforms.
Optimizing Revenue while showing Relevant Assortments at Scale
For an arbitrary collection of assortments, our algorithms can find a solution in time that is sub-linear in the number of assortments, and for the simpler case of cardinality constraints - linear in the number of items (existing methods are quadratic or worse).
$MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference
This work discusses the implementation of Markov Chain Monte Carlo (MCMC) sampling from an arbitrary Gaussian mixture model (GMM) within SRAM.
Incentivising Exploration and Recommendations for Contextual Bandits with Payments
We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users.
Supported-BinaryNet: Bitcell Array-based Weight Supports for Dynamic Accuracy-Latency Trade-offs in SRAM-based Binarized Neural Network
To reduce the power overheads, we propose a dynamic drop out a part of the support parameters.
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage
Models of user behavior are critical inputs in many prescriptive settings and can be viewed as decision rules that transform state information available to the user into actions.
Thompson Sampling for a Fatigue-aware Online Recommendation System
In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period.
Bandits with Temporal Stochastic Constraints
We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it.
Block-Structure Based Time-Series Models For Graph Sequences
Although the computational and statistical trade-off for modeling single graphs, for instance, using block models is relatively well understood, extending such results to sequences of graphs has proven to be difficult.
An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions
We propose a new efficient online algorithm to learn the parameters governing the purchasing behavior of a utility maximizing buyer, who responds to prices, in a repeated interaction setting.
Optimizing Revenue over Data-driven Assortments
For an arbitrary collection of assortments, our algorithms can find a solution in time that is sub-linear in the number of assortments and for the simpler case of cardinality constraints - linear in the number of items (existing methods are quadratic or worse).
Optimization and Control Data Structures and Algorithms
Symmetry Learning for Function Approximation in Reinforcement Learning
In this paper we explore methods to exploit symmetries for ensuring sample efficiency in reinforcement learning (RL), this problem deserves ever increasing attention with the recent advances in the use of deep networks for complex RL tasks which require large amount of training data.
Efficient Reinforcement Learning via Initial Pure Exploration
Based of the scores she obtains in these practice tests, she would formulate a strategy for maximizing her scores in the actual tests.
Provable Inductive Robust PCA via Iterative Hard Thresholding
The robust PCA problem, wherein, given an input data matrix that is the superposition of a low-rank matrix and a sparse matrix, we aim to separate out the low-rank and sparse components, is a well-studied problem in machine learning.
Learning to Partition using Score Based Compatibilities
For one of the four objectives, we show $NP$ hardness under the score structure and give a $\frac{1}{2}$ approximation algorithm for which no constant approximation was known thus far.
Faster Reinforcement Learning Using Active Simulators
In this work, we propose several online methods to build a \emph{learning curriculum} from a given set of target-task-specific training tasks in order to speed up reinforcement learning (RL).
Learning Personalized Optimal Control for Repeatedly Operated Systems
We consider the problem of online learning of optimal control for repeatedly operated systems in the presence of parametric uncertainty.
Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes
A recent goal in the Reinforcement Learning (RL) framework is to choose a sequence of actions or a policy to maximize the reward collected or minimize the regret incurred in a finite time horizon.
Robust Optimization using Machine Learning for Uncertainty Sets
Our goal is to build robust optimization problems for making decisions based on complex data from the past.
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge
In this paper, we consider a supervised learning setting where side knowledge is provided about the labels of unlabeled examples.
Machine Learning with Operational Costs
This work proposes a way to align statistical modeling with decision making.
On Combining Machine Learning with Decision Making
We present a new application and covering number bound for the framework of "Machine Learning with Operational Costs (MLOC)," which is an exploratory form of decision theory.
