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2020 – today
- 2021
[i23]Rasool Fakoor, Jonas Mueller, Pratik Chaudhari, Alexander J. Smola:
Continuous Doubly Constrained Batch Reinforcement Learning. CoRR abs/2102.09225 (2021)- 2020
- [j3]Matteo Terzi, Gian Antonio Susto, Pratik Chaudhari:
Directional adversarial training for cost sensitive deep learning classification applications. Eng. Appl. Artif. Intell. 91: 103550 (2020) - [j2]Rita Fioresi, Pratik Chaudhari, Stefano Soatto:
A Geometric Interpretation of Stochastic Gradient Descent Using Diffusion Metrics. Entropy 22(1): 101 (2020) - [j1]Andrew Hua
, Pratik Chaudhari, Nicole Johnson, Joshua Quinton, Bruce Schatz, David Buchner, Manuel E. Hernandez:
Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data. IEEE J. Biomed. Health Informatics 24(9): 2452-2460 (2020) - [c18]Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto:
A Baseline for Few-Shot Image Classification. ICLR 2020 - [c17]Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola:
Meta-Q-Learning. ICLR 2020 - [c16]Hao Li, Pratik Chaudhari, Hao Yang, Michael Lam, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto:
Rethinking the Hyperparameters for Fine-tuning. ICLR 2020 - [c15]Yansong Gao, Pratik Chaudhari:
A Free-Energy Principle for Representation Learning. ICML 2020: 3367-3376 - [c14]Marco Maggipinto, Gian Antonio Susto, Pratik Chaudhari:
Proximal Deterministic Policy Gradient. IROS 2020: 5438-5444 - [c13]Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola:
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. NeurIPS 2020 - [i22]Hao Li, Pratik Chaudhari, Hao Yang, Michael Lam, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto:
Rethinking the Hyperparameters for Fine-tuning. CoRR abs/2002.11770 (2020) - [i21]Yansong Gao, Pratik Chaudhari:
A Free-Energy Principle for Representation Learning. CoRR abs/2002.12406 (2020) - [i20]Rasool Fakoor, Pratik Chaudhari, Jonas Mueller, Alexander J. Smola:
TraDE: Transformers for Density Estimation. CoRR abs/2004.02441 (2020) - [i19]Achin Jain, Pratik Chaudhari, Manfred Morari:
BayesRace: Learning to race autonomously using prior experience. CoRR abs/2005.04755 (2020) - [i18]Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola:
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. CoRR abs/2006.14284 (2020) - [i17]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning. CoRR abs/2006.15199 (2020) - [i16]Marco Maggipinto, Gian Antonio Susto, Pratik Chaudhari:
Proximal Deterministic Policy Gradient. CoRR abs/2008.00759 (2020) - [i15]Xiaoyi Chen, Pratik Chaudhari:
MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation. CoRR abs/2008.07081 (2020) - [i14]Yansong Gao, Pratik Chaudhari:
An Information-Geometric Distance on the Space of Tasks. CoRR abs/2011.00613 (2020) - [i13]Christopher D. Hsu, Heejin Jeong, George J. Pappas, Pratik Chaudhari:
Scalable Reinforcement Learning Policies for Multi-Agent Control. CoRR abs/2011.08055 (2020)
2010 – 2019
- 2019
- [c12]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
P3O: Policy-on Policy-off Policy Optimization. UAI 2019: 1017-1027 - [i12]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
P3O: Policy-on Policy-off Policy Optimization. CoRR abs/1905.01756 (2019) - [i11]Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto:
A Baseline for Few-Shot Image Classification. CoRR abs/1909.02729 (2019) - [i10]Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola:
Meta-Q-Learning. CoRR abs/1910.00125 (2019) - [i9]Matteo Terzi, Gian Antonio Susto, Pratik Chaudhari:
Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications. CoRR abs/1910.03468 (2019) - [i8]Rita Fioresi, Pratik Chaudhari, Stefano Soatto:
A geometric interpretation of stochastic gradient descent using diffusion metrics. CoRR abs/1910.12194 (2019) - 2018
- [c11]Pratik Chaudhari, Stefano Soatto:
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks. ICLR (Poster) 2018 - [c10]Pratik Chaudhari, Stefano Soatto:
Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks. ITA 2018: 1-10 - 2017
- [c9]Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier:
Partial differential equations for training deep neural networks. ACSSC 2017: 1627-1631 - [c8]Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina:
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. ICLR (Poster) 2017 - [i7]Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier:
Deep Relaxation: partial differential equations for optimizing deep neural networks. CoRR abs/1704.04932 (2017) - [i6]Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar:
Parle: parallelizing stochastic gradient descent. CoRR abs/1707.00424 (2017) - [i5]Pratik Chaudhari, Stefano Soatto:
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks. CoRR abs/1710.11029 (2017) - 2016
- [i4]Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina:
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. CoRR abs/1611.01838 (2016) - 2015
- [i3]Pratik Chaudhari, Stefano Soatto:
Trivializing The Energy Landscape Of Deep Networks. CoRR abs/1511.06485 (2015) - 2014
- [c7]Pratik Chaudhari, Tichakorn Wongpiromsarn, Emilio Frazzoli:
Incremental minimum-violation control synthesis for robots interacting with external agents. ACC 2014: 1761-1768 - [c6]Minghui Zhu, Michael W. Otte, Pratik Chaudhari, Emilio Frazzoli:
Game theoretic controller synthesis for multi-robot motion planning Part I: Trajectory based algorithms. ICRA 2014: 1646-1651 - [c5]Valerio Varricchio, Pratik Chaudhari, Emilio Frazzoli:
Sampling-based algorithms for optimal motion planning using process algebra specifications. ICRA 2014: 5326-5332 - [i2]Minghui Zhu, Michael W. Otte, Pratik Chaudhari, Emilio Frazzoli:
Game theoretic controller synthesis for multi-robot motion planning Part I : Trajectory based algorithms. CoRR abs/1402.2708 (2014) - 2013
- [c4]Pratik Chaudhari, Sertac Karaman, David Hsu, Emilio Frazzoli:
Sampling-based algorithms for continuous-time POMDPs. ACC 2013: 4604-4610 - [c3]Luis I. Reyes Castro, Pratik Chaudhari, Jana Tumova, Sertac Karaman, Emilio Frazzoli, Daniela Rus:
Incremental sampling-based algorithm for minimum-violation motion planning. CDC 2013: 3217-3224 - [i1]Luis I. Reyes Castro, Pratik Chaudhari, Jana Tumova, Sertac Karaman, Emilio Frazzoli, Daniela Rus:
Incremental Sampling-based Algorithm for Minimum-violation Motion Planning. CoRR abs/1305.1102 (2013) - 2012
- [c2]Pratik Chaudhari, Sertac Karaman, Emilio Frazzoli:
Sampling-based algorithm for filtering using Markov chain approximations. CDC 2012: 5972-5978
2000 – 2009
- 2009
- [c1]Pratik Chaudhari:
Localization using Average Landmark Vector in the Presence of Clutter. NaBIC 2009: 1592-1595
Coauthor Index
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